New Trends in Applied Artificial Intelligence, 20 conf

Lecture Notes in Artificial Intelligence Edited by J. G. Carbonell and J. Siekmann

Subseries of Lecture Notes in Computer Science

4570

Hiroshi G. Okuno Moonis Ali (Eds.)

New Trends in Applied Artificial Intelligence 20th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, IEA/AIE 2007 Kyoto, Japan, June 26-29, 2007 Proceedings

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Series Editors Jaime G. Carbonell, Carnegie Mellon University, Pittsburgh, PA, USA Jörg Siekmann, University of Saarland, Saarbrücken, Germany Volume Editors Hiroshi G. Okuno Kyoto University Graduate School of Informatics Yoshida-Hommachi, Sakyo, Kyoto 606-8501, Japan E-mail: [email protected] Moonis Ali Texas State University – San Marcos Department of Computer Science Nueces 247, 601 University Drive, San Marcos, TX 78666-4616, USA E-mail: [email protected]

Library of Congress Control Number: Applied for

CR Subject Classification (1998): I.2, F.1, F.2, I.5, F.4.1, D.2, H.4, H.2.8, H.5.2 LNCS Sublibrary: SL 7 – Artificial Intelligence ISSN ISBN-10 ISBN-13

0302-9743 3-540-73322-1 Springer Berlin Heidelberg New York 978-3-540-73322-5 Springer Berlin Heidelberg New York

This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. Springer is a part of Springer Science+Business Media springer.com © Springer-Verlag Berlin Heidelberg 2007 Printed in Germany Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper SPIN: 12082827 06/3180 543210

Preface

“The true sign of intelligence is not knowledge but imagination.” Albert Einstein Applied artificial intelligence researchers have been focusing on developing and employing methods and systems to solve real-life problems in all areas including engineering, science, industry, automation & robotics, business & finance, cyberspace, and man–machine interactions. The 20th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE-2007) held in Kyoto, Japan presented such work performed by many scientists worldwide. The previous IEA/AIE conference held in Japan was the Ninth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert systems (IEA/AIE-1996) in Fukuoka in 1996. The duration of 11 years between two conferences demanded drastic changes around applied artificial intelligence research. The main causes are the rapid expansion of the Internet and the deluge of electronic and on-line text data. The Program Committee focused on Asian-originating technologies, such as active mining, integration of the Internet and broadcasting, chance discovery, real-world interactions, and fuzzy logic applications. The first four are from Japan and the last one is from Taiwan and China. We received 462 papers from all parts of the world. Each paper was sent to at least three Program Committee members for review. Only 116 papers were selected for presentation and publication in the proceedings. We would like to express our sincere thanks to the Program Committee and all the reviewers for their hard work. The papers in the proceedings cover the following topics: application systems, agent-based system chance discovery and social network, constraint satisfaction, data mining, e-commerce, education, fuzzy logic, fuzzy system applications, heuristic search genetic algorithm machine learning manufacturing, neural network, real-world interaction, robot, speech, text processing, vision, and other applications. The set of selected papers presented at IEA/AIE-2007, sponsored by the International Society of Applied Intelligence (ISAI) and the Graduate School of Informatics, Kyoto University, is offered in this volume. These papers highlight new trends of applied artificial intelligence and show how new research could lead to new and innovative applications. We hope that you find these papers to be educational, useful in your own research, and stimulating. In addition to the ISAI and Graduate School of Informatics, Kyoto University, the main sponsors of the conference, our special thanks are due to the Association for the Advancement of Artificial Intelligence (AAAI, former American

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Preface

Association for Artificial Intelligence), the Association for Computing Machinery (ACM/SIGART), the Canadian Society for the Computational Studies of Intelligence (CSCSI/SCEIO), the European Neural Network Society (ENNS), the Human Interface Society (HIS), the Institute of Electronics, Information and Communication Engineers (IEICE), the International Neural Network Society (INNS), the Information Processing Society of Japan (IPSJ), the Japanese Society for Artificial Intelligence (JSAI), the Robotics Society of Japan (RSJ), the Taiwanese Association for Artificial Intelligence (TAAI), and Texas State University San Marcos. We wish to thank the members of the Program Committee, especially those who played specific roles: Kazunori Komatani (Publicity Chair), Tokyoaki Nishida (Program Co-chair), Yukio Osama (Program Co-chair), Shyi-Ming Chen and Takayuki Ito (Special Session Organizers), and Tetsuya Ogata (Local Organization Chair). We would like to thank Secreatari@t Inc. and Miki Nishii for their efficiency in dealing with the registration and management issues. We would also like to thank the Artificial Intelligence Research Promotion Foundation and the Support Center for Advanced Telecommunications Technology Research, Foundations, Google Japan Inc., Honda Research Institute Japan Co., Ltd., and TOME R&D Inc. for their financial support for IEA/AIE-2007. There are many other participants, as well as all the authors, who were critically important in the organization of this conference. The conference would not have been possible without their valuable support. April 2007

Hiroshi G. Okuno Moonis Ali

Organization

The IEA/AIE-2007 was organized by the International Society of Applied Intelligence and the Graduate School of Informatics, Kyoto University in cooperation with AAAI, ACM/SIGART, CSCSI/SCEIO, ENNS, HIS, IEICE, INNS, IPSJ, JSAI, RSJ, TAAI, and Texas State University San Marcos.

Chairs General Chair Program Chair Program Co-chairs Publicity Chair Local Organization Chair

Moonis Ali (Texas State University San Marcos, USA) Hiroshi G. Okuno (Kyoto University, Japan) Toyoaki Nishida (Kyoto University, Japan) Yukio Osawa (The University of Tokyo, Japan) Kazunori Komatani (Kyoto University, Japan) Tetsuya Ogata (Kyoto University, Japan)

Program Committee Akutsu, Tatsuya, Japan Auberge, Veronique, France Bannister, Peter R., UK Belli, Fevzi, Germany Borzemski, Leszek, Poland Brezillon, Patrick, France Burke, Edmund K, UK Chan, C. W., Hong Kong Chen, Shyi-Ming, Taiwan Chien, Been-Chian, Taiwan Chung, Paul, UK Clifton, David, UK Correa da Silva, Flavio Soares, Brazil Dapoigny, Richard, France Desimone, Roberto, Australia Dimitriadis, A. Yannis, Spain Duan, Hai-Bin, China Esposito, Floriana, Italy Galichet, Sylvie, France Guesgen, Hans Werner, New Zealand Harandi, Mehdi T., USA Hattori, Hiromitsu, USA Hendtlass, Tim, Australia

Horvath, Gabor, Hungary Huget, Marc-Philippe, France Iida, Fumiya, Switzerland Ishizuka, Mitsuru, Japan Ito, Takayuki, Japan Jacquenet, Francois, France Jain, Lakhmi, Australia Jo, Geun Sik, Korea Kabanza, Froduald, Canada Kaikhah, Khosrow, USA Kaneda, Shigeo, Japan Kim, Jin Hyung, Korea Kinoshita, Tetsuo, Japan Krol, Dariusz, Poland Kumar, Amruth, USA Kerschberg, Larry, USA Laurent, Foulloy, France Lee, Chang-Hwan, Korea Lee, Chang-Shing, Taiwan Lee, Huey-Ming, Taiwan Lee, Seungkeun, France Liu, Ying, Singapore Madani, Kurosh, France

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Organization

Maki, Atsuto, Japan Matsuo, Tokuro, Japan Matthews, Manton M., USA Minato, Shin-ichi, Japan Mira, Jose, Spain Monostori, Laszlo, Hungary Mori, Tatsunori, Japan Munoz-Avila, Hector, USA Murphey, Yi Lu, USA Nakano, Mikio, Japan Neumann, Bernd, Germany Nguyen, Ngoc Thanh, Poland Onoda, Takashi, Japan Orchard, Robert, Canada Pellegrini, Christian, Switzerland Potter, Walter Don, USA Ramaswamy, Srini, USA Randall, Marcus Christian, Australia Rayward-Smith, Victor J., UK Roche, Christophe, France

Saitta, Lorenza, Italy Sanchez-Marre, Miquel, Spain Shimazu, Akira, Japan Shpitalni, Moshe, IL Suzuki, Kazuhiko, Japan Tanaka, Takushi, Japan Treur, Jan, The Netherlands Uehara, Kuniaki, Japan Vadera, Sunil, UK Valtorta, Marco, USA Vancza, Jozsef, Hungary Wang, Xue Z., UK Washio, Takashi, Japan Watanabe, Toyohide, Japan Williams, Graham, Australia Wittig, Thies, Germany Yang, Chunsheng, Canada Yang, Yubin, Australia Yannis, Karnavas L., Greece Zaiping, Chen, China

Additonal Reviewers Asakura, Koichi, Japan Asano, Futoshi, Japan Aydin, Kemal, USA Bai, Ruibin, UK Biba, Marenglen, Italy Blachuta, Marian, Poland ´ Blanco, Angela, Spain Bosse, Tibor, The Netherlands Boukezzoula, Reda, France Castiello, Ciro, Italy Cuong, Le Anh, Japan d’Amato, Claudia, Italy Egri, Peter, Hungary Elsalloukh, Hassan, USA Fanizzi, Nicola, Italy Ferilli, Stefano, Italy Fuente, M.J., Spain Fukumoto, Junichi, Japan Habe, Hitoshi, Japan Hanaue, Kouichi, Japan Hasegawa, Yuji, Japan Hayashida, Morihiro, Japan

Heuvelink, Annerieke, The Netherlands Hirayama, Takatsugu, Japan Hoogendoorn, Mark, The Netherlands Ikezaki, Masakazu, Japan Ito, Shinya, USA Jabir, Shayma, USA Joshi, Hemant, USA Kato, Jien, Japan Kojiri, Tomoko, Japan Koshizen, Takamasa, Japan Kukla, Grzegorz, Poland Kumon, Makoto, Japan Kuga, Masahiro, Japan Le, Nguyen Minh, Japan Li, Jinpeng, UK Mart´ın-Merino, Manuel, Spain Mauro, Nicola Di, Italy McCollum, Barry, UK Mencar, Corrado, Italy Mete, Mutlu, USA Mukai, Naoto, Japan Nakadai, Kazuhiro, Japan

Organization

Nakajima, Hirofumi, Japan Nakamura, Makoto, Japan Nishide, Shun, Japan Nobuhara, Shohei, Japan Nomiya, Hiroki, Japan Nowak, Andrzej, Poland Okabe, Tatsuya, Japan Orlowski, Cezary, Poland Overeinder, Benno, The Netherlands Oyama, Satoshi, Japan Qu, Rong, UK Pan, Youlian, Canada Pilot, Tomasz, Poland Poland, Jan, Japan Sainz-Palmero, Gregorio, Spain Sakurada, Hideki, Japan Sakurai, Kouichi, Japan Sakurai, Yuko, Japan Seki, Kazuhiro, Japan Serban, R., The Netherlands Sharpanskykh, Alexei, The Netherlands Shirahama, Kimiaki, Japan Singh, Ravi Pratap, Italy

Smith, Brian A., USA Stegers, Ruud, The Netherlands Szlachetko, Bogdan, Poland Tajima, Keishi, Japan Takai, Takeshi, Japan Takeuchi, Johane, Japan Tamura, Takeyuki, Japan Thai, Nguyen Phuong, Japan Than, Nguyen Tri, Japan Tinmar, Trevor, UK Trawinski, Bogdan, Poland Vinh, Nguyen Van, Japan Walkowiak, Tomasz, Poland Yada, Tetsushi, Japan Yokoo, Makoto, Japan Yasumura, Yoshiaki, Japan Yoshimoto, Hiromasa, Japan Yuruk, Nurcan, USA Zatwarnicki, Krzysztof, Poland Zeugmann, Thomas, Japan Zhang, Chuanlei, USA Zheng, Xinliang, USA

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Table of Contents

Keynotes Towards New Content Services by Fusion of Web and Broadcasting Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Katsumi Tanaka

1

Pattern Discovery from Graph-Structured Data - A Data Mining Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hiroshi Motoda

12

Text Processing A Collocation-Based WSD Model: RFR-SUM . . . . . . . . . . . . . . . . . . . . . . . Weiguang Qu, Zhifang Sui, Genlin Ji, Shiwen Yu, and Junsheng Zhou

23

A Simple Probability Based Term Weighting Scheme for Automated Text Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ying Liu and Han Tong Loh

33

Text Classification for Healthcare Information Support . . . . . . . . . . . . . . . Rey-Long Liu

44

[Special] Fuzzy System Applications I Nurse Scheduling Using Fuzzy Multiple Objective Programming . . . . . . . Seyda Topaloglu and Hasan Selim

54

Fuzzy Adaptive Threshold Determining in the Key Inheritance Based Sensor Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hae Young Lee and Tae Ho Cho

64

A New Approach for Evaluating Students’ Answerscripts Based on Interval-Valued Fuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hui-Yu Wang and Shyi-Ming Chen

74

Vision I An Intelligent Multimedia E-Learning System for Pronunciations . . . . . . Wen-Chen Huang, Tsai-Lu Chang-Chien, and Hsiu-Pi Lin

84

Phase-Based Feature Matching Under Illumination Variances . . . . . . . . . . Masaaki Nishino, Atsuto Maki, and Takashi Matsuyama

94

Text Extraction for Spam-Mail Image Filtering Using a Text Color Estimation Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ji-Soo Kim, S.H. Kim, H.J. Yang, H.J. Son, and W.P. Kim

105

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[Special] Real World Interaction Intention Through Interaction: Toward Mutual Intention in Real World Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yasser F.O. Mohammad and Toyoaki Nishida

115

An Interactive Framework for Document Retrieval and Presentation with Question-Answering Function in Restricted Domain . . . . . . . . . . . . . . Teruhisa Misu and Tatsuya Kawahara

126

Generating Cartoon-Style Summary of Daily Life with Multimedia Mobile Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sung-Bae Cho, Kyung-Joong Kim, and Keum-Sung Hwang

135

[Special] Fuzzy System Applications II Economic Turning Point Forecasting Using Neural Network with Weighted Fuzzy Membership Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Soo H. Chai and Joon S. Lim

145

A New Approach for Automatically Constructing Concept Maps Based on Fuzzy Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shih-Ming Bai and Shyi-Ming Chen

155

Application of Fuzzy Logic for Adaptive Interference Canceller in CDMA Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yung-Fa Huang, Ping-Ho Ting, and Tan-Hsu Tan

166

Vision II Robust Multi-scale Full-Band Image Watermarking for Copyright Protection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jung-Chun Liu, Chu-Hsing Lin, Li-Ching Kuo, and Jen-Chieh Chang Perimeter Intercepted Length and Color t -Value as Features for Nature-Image Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yung-Fu Chen, Meng-Hsiun Tsai, Chung-Chuan Cheng, Po-Chou Chan, and Yuan-Heng Zhong Selecting an Appropriate Segmentation Method Automatically Using ANN Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yih-Chih Chiou and Meng-Ru Tsai

176

185

195

Genetic Algorithm Efficient Reinforcement Hybrid Evolutionary Learning for Recurrent Wavelet-Based Neuro-fuzzy Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cheng-Hung Chen, Cheng-Jian Lin, and Chi-Yung Lee

207

A Relation-Based Genetic Algorithm for Partitioning Problems with Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jiah-Shing Chen, Yao-Tang Lin, and Liang-Yu Chen

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Table of Contents

Constrained Optimization of a Newsboy Problem with Return Policy Using KKT Conditions and GA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . P.C. Yang, H.M. Wee, S.L. Chung, and S.H. Kang

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227

[Special] Fuzzy System Applications III Fuzzy Interpolative Reasoning Via Cutting and Transformations Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yaun-Kai Ko and Shyi-Ming Chen

238

Using Fuzzy Theory for Packaging Attribute Deployment for New Notebook Computer Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hsin Rau, Chien-Ping Liao, Wei-Jung Shiang, and Chiu-Hsiang Lin

250

Fuzzy System Model to Assist with Real Estate Appraisals . . . . . . . . . . . . Dariusz Kr´ ol, Tadeusz Lasota, Wojciech Nalepa, and Bogdan Trawi´ nski Application of Fuzzy System on a Server-Dependent Queue Modeled with Empirical Bayesian Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pei-Chun Lin and Jenhung Wang

260

270

Robot Real-Time Auditory and Visual Talker Tracking Through Integrating EM Algorithm and Particle Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hyun-Don Kim, Kazunori Komatani, Tetsuya Ogata, and Hiroshi G. Okuno Self-organizing Multiple Models for Imitation: Teaching a Robot to Dance the YMCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ¨ urk Axel Tidemann and Pinar Ozt¨ An Efficient Flow-Shop Scheduling Algorithm Based on a Hybrid Particle Swarm Optimization Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I-Hong Kuo, Shi-Jinn Horng, Tzong-Wann Kao, Tsung-Lieh Lin, and Pingzhi Fan Towards the Automated Design of Phased Array Ultrasonic Transducers – Using Particle Swarms to Find “Smart” Start Points . . . . . . . . . . . . . . . . Stephen Chen, Sarah Razzaqi, and Vincent Lupien

280

291

303

313

Poster Solution of the Perspective-Three-Point Problem . . . . . . . . . . . . . . . . . . . . . Loic Merckel and Toyoaki Nishida

324

A Decision Support System for Underground Mining Method Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Serafettin Alpay and Mahmut Yavuz

334

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Efficient Modified Bidirectional A∗ Algorithm for Optimal Route-Finding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Taeg-Keun Whangbo

344

Toward a Large Scale E-Market: A Greedy and Local Search Based Winner Determination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Naoki Fukuta and Takayuki Ito

354

Agent Based Dynamic Job Shop Simulation System . . . . . . . . . . . . . . . . . . S ¸ erafettin Alpay

364

A Manufacturing-Environmental Model Using Bayesian Belief Networks for Assembly Design Decision Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wooi Ping Cheah, Kyoung-Yun Kim, Hyung-Jeong Yang, Sook-Young Choi, and Hyung-Jae Lee Evaluation of Two Simultaneous Continuous Speech Recognition with ICA BSS and MFT-Based ASR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ryu Takeda, Shun’ichi Yamamoto, Kazunori Komatani, Tetsuya Ogata, and Hiroshi G. Okuno

374

384

Knowledge Based Discovery in Systems Biology Using CF-Induction . . . . Andrei Doncescu, Katsumi Inoue, and Yoshitaka Yamamoto

395

Environment Recognition System for Biped Walking Robot Using Vision Based Sensor Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tae-Koo Kang, Heejun Song, Dongwon Kim, and Gwi-Tae Park

405

Design of a SOA-Oriented E-Diagnostics System for Hydroelectric Generating Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Liangliang Zhan, Yongchuan Zhang, Jianzhong Zhou, Yucheng Peng, and Zheng Li

415

Genetic Algorithm II A Systematic Layout Planning of Visualizing Devices on a Non-rectangular Plane by Genetic Heuristics . . . . . . . . . . . . . . . . . . . . . . . . Chir-Ho Chang and Jin-Ling Lin

424

Promising Search Regions of Crossover Operators for Function Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hiroshi Someya

434

Automatic Fingerprints Image Generation Using Evolutionary Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ung-Keun Cho, Jin-Hyuk Hong, and Sung-Bae Cho

444

A Hybrid Genetic Algorithm for the Cut Order Planning Problem . . . . . Ahlem Bouziri and Rym M’hallah

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Fuzzy Logic I Supervised Adaptive Control of Unknown Nonlinear Systems Using Fuzzily Blended Time-Varying Canonical Model . . . . . . . . . . . . . . . . . . . . . Yau-Zen Chang and Zhi-Ren Tsai

464

Multi-agent System with Hybrid Intelligence Using Neural Network and Fuzzy Inference Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kevin I-Kai Wang, Waleed H. Abdulla, and Zoran Salcic

473

Analysis of Log Files Applying Mining Techniques and Fuzzy Logic . . . . V´ıctor H. Escobar-Jeria, Mar´ıa J. Mart´ın-Bautista, Daniel S´ anchez, and Mar´ıa-Amparo Vila

483

Stability Analysis for Nonlinear Systems Subjected to External Force . . . Ken Yeh, Cheng-Wu Chen, Shu-Hao Lin, Chen-Yuan Chen, Chung-Hung Tsai, and Jine-Lih Shen

493

Manufacturing Integrated Framework for Reverse Logistics . . . . . . . . . . . . . . . . . . . . . . . . . Heng-Li Yang and Chen-Shu Wang

501

Screening Paper Formation Variations on Production Line . . . . . . . . . . . . Marcus Ejnarsson, Carl Magnus Nilsson, and Antanas Verikas

511

Multi-modal Data Integration Using Graph for Collaborative Assembly Design Information Sharing and Reuse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Hyung-Jae Lee, Kyoung-Yun Kim, Hyung-Jeong Yang, Soo-Hyung Kim, and Sook-Young Choi Enhanced Probabilistic Filtering for Improving the Efficiency of Local Searches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Byoungho Kang and Kwang Ryel Ryu

521

531

Data Mining I A Weighted Feature C-Means Clustering Algorithm for Case Indexing and Retrieval in Cased-Based Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chuang-Cheng Chiu and Chieh-Yuan Tsai Neural Networks for Inflow Forecasting Using Precipitation Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Karla Figueiredo, Carlos R. Hall Barbosa, Andr´e V.A. Da Cruz, Marley Vellasco, Marco Aur´elio C. Pacheco, and Roxana J. Conteras A Gradational Reduction Approach for Mining Sequential Patterns . . . . . Jen-Peng Huang, Guo-Cheng Lan, and Huang-Cheng Kuo

541

552

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A Kernel Method for Measuring Structural Similarity Between XML Documents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Buhwan Jeong, Daewon Lee, Hyunbo Cho, and Boonserm Kulvatunyou

572

Neural Network I A Neural Network Based Data Least Squares Algorithm for Channel Equalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jun-Seok Lim

582

Novelty Detection in Large-Vehicle Turbocharger Operation . . . . . . . . . . . David A. Clifton, Peter R. Bannister, and Lionel Tarassenko

591

Equalization of 16 QAM Signals with Reduced BiLinear Recurrent Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dong-Chul Park and Yunsik Lee

601

Enhanced Neural Filter Design and Its Application to the Active Control of Nonlinear Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Cheng-Yuan Chang, I-Ling Chung, Chang-Min Chou, and Fuh-Hsin Hwang

611

Constraint Satisfaction Case Analysis of Criminal Behaviour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tibor Bosse, Charlotte Gerritsen, and Jan Treur

621

Diagnosing Dependent Failures in the Hardware and Software of Mobile Autonomous Robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . J¨ org Weber and Franz Wotawa

633

PrDLs: A New Kind of Probabilistic Description Logics About Belief . . . Jia Tao, Zhao Wen, Wang Hanpin, and Wang Lifu

644

Multi-constraint System Scheduling Using Dynamic and Delay Ant Colony System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shih-Tang Lo, Ruey-Maw Chen, and Yueh-Min Huang

655

Data Mining II Constructing Domain Ontology Using Structural and Semantic Characteristics of Web-Table Head . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sung-won Jung, Mi-young Kang, and Hyuk-chul Kwon Maintenance of Fast Updated Frequent Trees for Record Deletion Based on Prelarge Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chun-Wei Lin, Tzung-Pei Hong, Wen-Hsiang Lu, and Chih-Hung Wu

665

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Solving a Committee Formation and Scheduling Problem by Frequent Itemset Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chienwen Wu

XVII

685

Neural Network II Dual Gradient Descent Algorithm on Two-Layered Feed-Forward Artificial Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Bumghi Choi, Ju-Hong Lee, and Tae-Su Park

696

A New Hybrid Learning Algorithm for Drifting Environments . . . . . . . . . Khosrow Kaikhah

705

Solving Inequality Constraints Job Scheduling Problem by Slack Competitive Neural Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Ruey-Maw Chen, Shih-Tang Lo, and Yueh-Min Huang

715

Fuzzy Logic II Intelligent OS Process Scheduling Using Fuzzy Inference with User Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sungsoo Lim and Sung-Bae Cho Cardinality-Based Fuzzy Time Series for Forecasting Enrollments . . . . . . Jing-Rong Chang, Ya-Ting Lee, Shu-Ying Liao, and Ching-Hsue Cheng A New Fuzzy Interpolative Reasoning Method for Sparse Fuzzy Rule-Based Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Li-Wei Lee and Shyi-Ming Chen

725 735

745

Machine Learning I A New Multi-class Support Vector Machine with Multi-sphere in the Feature Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pei-Yi Hao and Yen-Hsiu Lin

756

Construction of Prediction Module for Successful Ventilator Weaning . . . Jiin-Chyr Hsu, Yung-Fu Chen, Hsuan-Hung Lin, Chi-Hsiang Li, and Xiaoyi Jiang

766

Extension of ICF Classifiers to Real World Data Sets . . . . . . . . . . . . . . . . . Kazuya Haraguchi and Hiroshi Nagamochi

776

[Special] Chance Discovery and Social Network I Hierarchical Visualization for Chance Discovery . . . . . . . . . . . . . . . . . . . . . . Brett Bojduj and Clark S. Turner

786

XVIII

Table of Contents

Episodic Memory for Ubiquitous Multimedia Contents Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kyung-Joong Kim, Myung-Chul Jung, and Sung-Bae Cho

796

Catalyst Personality for Fostering Communication Among Groups with Opposing Preference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yoshiharu Maeno, Yukio Ohsawa, and Takaichi Ito

806

Education On Using Learning Automata to Model a Student’s Behavior in a Tutorial-like System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Khaled Hashem and B. John Oommen

813

Test-Sheet Composition Using Immune Algorithm for E-Learning Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chin-Ling Lee, Chih-Hui Huang, and Cheng-Jian Lin

823

PDA Plant Search System Based on the Characteristics of Leaves Using Fuzzy Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shu-Chen Cheng, Jhen-Jie Jhou, and Bing-Hong Liou

834

Machine Learning II Stochastic Point Location in Non-stationary Environments and Its Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B. John Oommen, Sang-Woon Kim, Mathew Samuel, and Ole-Christoffer Granmo

845

Quick Adaptation to Changing Concepts by Sensitive Detection . . . . . . . Yoshiaki Yasumura, Naho Kitani, and Kuniaki Uehara

855

ACIK: Association Classifier Based on Itemset Kernel . . . . . . . . . . . . . . . . Yang Zhang, Yongge Liu, Xu Jing, and Jianfeng Yan

865

[Special] Chance Discovery and Social Network II Risk Discovery Based on Recommendation Flow Analysis on Social Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jason J. Jung and Geun-Sik Jo

876

Using Conceptual Scenario Diagrams and Integrated Scenario Map to Detect the Financial Trend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chao-Fu Hong, Tzu-Fu Chiu, Yu-Ting Chiu, and Mu-Hua Lin

886

Chance Discovery in Credit Risk Management . . . . . . . . . . . . . . . . . . . . . . . Shinichi Goda and Yukio Ohsawa

896

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Speech The Design of Phoneme Grouping for Coarse Phoneme Recognition . . . . Kazuhiro Nakadai, Ryota Sumiya, Mikio Nakano, Koichi Ichige, Yasuo Hirose, and Hiroshi Tsujino An Improved Voice Activity Detection Algorithm for GSM Adaptive Multi-Rate Speech Codec Based on Wavelet and Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Shi-Huang Chen, Yaotsu Chang, and T.K. Truong The PICA Framework for Performance Analysis of Pattern Recognition Systems and Its Application in Broadcast News Segmentation . . . . . . . . . Xiangdong Wang, Meiyin Li, Shouxun Lin, Yueliang Qian, and Qun Liu

905

915

925

[Special] E-commerce I The Theory of Maximal Social Welfare Feasible Coalition . . . . . . . . . . . . . Laor Boongasame, Veera Boonjing, and Ho-fung Leung

935

Recommender Agent Based on Social Network . . . . . . . . . . . . . . . . . . . . . . . Heng-Li Yang and Hsiao-Fang Yang

943

A New Pooled Buying Method Based on Risk Management . . . . . . . . . . . Tokuro Matsuo

953

The Representation of e-Contracts as Default Theories . . . . . . . . . . . . . . . . Georgios K. Giannikis and Aspassia Daskalopulu

963

Heuristic Search I Competitive Ant Colony Optimisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marcus Randall

974

Optimization of Dynamic Combinatorial Optimization Problems Through Truth Maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Brett Bojduj, Dennis Taylor, and Franz Kurfess

984

A Microcanonical Optimization Algorithm for BDD Minimization Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sang-Young Cho, Minna Lee, and Yoojin Chung

992

A Lot Size Model for Deteriorating Inventory with Back-Order Cancellation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1002 Peng-Sheng You and Yi-Chih Hsieh

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Application System An Intermodal Transport Network Planning Algorithm Using Dynamic Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1012 Jae Hyung Cho, Hyun Soo Kim, Hyung Rim Choi, Nam Kyu Park, and Moo Hong Kang Immune Inspired Optimizer of Combustion Process in Power Boiler . . . . 1022 ´ Konrad Swirski and Konrad Wojdan Dynamic Search Spaces for Coordinated Autonomous Marine Search and Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1032 Benjamin Lavis and Tomonari Furukawa Composite Endoscope Images from Massive Inner Intestine Photos . . . . . 1042 Eunjung Kim, Kwan-Hee Yoo, Je-Hoon Lee, Yong-Dae Kim, and Younggap You

[Special] E-commerce II Using Trust in Collaborative Filtering Recommendation . . . . . . . . . . . . . . 1052 Chein-Shung Hwang and Yu-Pin Chen AdaptRank: A Hybrid Method for Improving Recommendation Recall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1061 Maciej Kiewra and Ngoc Thanh Nguyen Combinatorial Auction with Minimal Resource Requirements . . . . . . . . . . 1072 Fu-Shiung Hsieh

Agent-Based System Effectiveness of Autonomous Network Monitoring Based on Intelligent-Agent-Mediated Status Information . . . . . . . . . . . . . . . . . . . . . . . 1078 Susumu Konno, Sameer Abar, Yukio Iwaya, and Tetsuo Kinoshita Design and Implementation of Interactive Design Environment of Agent System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1088 Takahiro Uchiya, Takahide Maemura, Xiaolu Li, and Tetsuo Kinoshita An Agent-Based Approach to Knapsack Optimization Problems . . . . . . . 1098 Sergey Polyakovsky and Rym M’Hallah

Heuristic Search II Constraint-Based Approach for Steelmaking–Continuous Casting Rescheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1108 Tieke Li and Dongfen Guo

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Study on Loop Problem in Opening Database for Chinese Chess Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1118 Shi-Jim Yen, Tai-Ning Yang, Jr-Chang Chen, and Shun-Chin Hsu Job Shop Scheduling Optimization Using Multi-modal Immune Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1127 Guan-Chun Luh and Chung-Huei Chueh Simulated Annealing Algorithm for Solving Network Expanded Problem in Wireless ATM Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1138 Der-Rong Din

Other Applications An Intrusion Detection Based on Support Vector Machines with a Voting Weight Schema . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1148 Rung-Ching Chen and Su-Ping Chen An Ontology-Supported and Fully-Automatic Annotation Technology for Semantic Portals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1158 Sheng-Yuan Yang Development and Evaluation of an Intelligent Colour Planning Support System for Townscapes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1169 Yuichiro Kinoshita, Eric W. Cooper, and Katsuari Kamei A Testing Device for the Human Ability to Utilise Beneficial Environmental Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1180 Blagovest Vladimirov, Hiromi Mochiyama, and Hideo Fujimoto Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1191

Towards New Content Services by Fusion of Web and Broadcasting Contents Katsumi Tanaka Department of Social Informatics, Graduate School of Informatics, Kyoto University [email protected] http://www.dl.kuis.kyoto-u.ac.jp

Abstract. We describe the research on fusion of Web and TV broadcasting contents conducted by Kyoto University 21st COE program and NICT communication/broadcasting contents fusion project. Despite much talk about the fusion of broadcasting and the Internet, no technology has been established for fusing web and TV program content. We proposed several ways to acquire information from diverse information sources of different media types, especially from Web and TV broadcasting. A notable difference between Web contents and TV program contents is that the former is a document-based information media and the latter is a time-based continuous information media, which leads to the difference of information accessing methods. Conventional “Web browsing” is an active manner of accessing information. On the other hand, conventional “TV watching” is a passive way of accessing information. In order to search, integrate and view the information of Web and TV, we explored (1) media conversion between Web and TV contents, (2) watching TV with live chats, (3) dynamic TV-content augmentation by Web, and (4) searching for TV contents with Web. Keywords: World Wide Web, Broadcasting Contents, Browsing, Search.

1 Introduction The Internet use is now very widespread, and content such as movies can be easily downloaded from the Internet at home. The increasing popularity of the Internet has coincided with a major change in the broadcasting environment as digital broadcasting has been introduced and data broadcasting services have become available. With these advances, the fusion of broadcasting and the Internet has become a widely discussed topic. A notable difference between Web contents and TV program contents is that the former is a document-based information media and the latter is a time-based continuous information media, which leads to the difference of information accessing methods. Conventional Web “browsing” is an active manner of accessing information. On the other hand, conventional TV “watching” is a passive way of accessing information. In our research, first, we focused on media conversion between these two media. By the media conversion technologies, users will be able to access information of these two media in their favorite style. For example, it will H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 1–11, 2007. © Springer-Verlag Berlin Heidelberg 2007

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become possible to “watch and listen to Web” in the same way they watch TV, or to “browse TV programs” as if they were browsing the Web. Recently, Hasegawa et al (E.Hasegawa and Y. Tanaka, NEC tech. report Vol.57, No.6, pp.10-13, 2004) reported that (1) 72% of Japanese people were watching TV concurrently with Web, (2) 52% of Japanese people, who watch TV, accessed Internet just after TV watching, and (3) the number of people who chat while watching TV, was also increasing. In the near future, the fusion of TV and Web media will make the boundaries between them blurred. It will become possible, for example, for consumers to create or annotate TV program contents, which at present are created only by broadcasting companies. This means that content authors, who will include both professionals and consumers, will be able to send information using their favorite media, content style, and environment. As for this, in this paper, we propose a way of watching TV together with user live chats. Furthermore, it will become possible to obtain TV program content and web content simultaneously; that is, the two forms of content will be blended and their boundaries between them will disappear. With the improvement in the performance of various home appliances, TV content can be recorded or stored by users in various places in their immediate environment. For example, 1-TB hard-disk (HDD) recorders on the market can record more than 1700 hours of video at a certain quality. It is reported that the HDD recorders brought the increase of Japanese TV viewing time (2 hours 9 minutes increase per a day). We regard the fusion of TV and web content as a key factor that will shape next-generation systems for content delivery. This will lead to the change of TV viewing. We propose a new way to watch TV which is augmented by Web. Also, we propose a new search engine that can find TV contents together with Web.

2 Media Conversion from Web to TV We developed three different prototype systems [1-6]: u-Pav which reads out the entire text of web content and presents image animation, Web2TV which reads out the entire text of web content and presents character agent animation, and Web2Talkshow which presents keyword-based dialogue and character agent animation. These prototype systems enable users to watch web content in the same way as they watch TV. The u-Pav (Ubiquitous Passive Viewer) system is based on using the text read-out type of the audio component and the image animation type of the visual component. We developed u-Pav for two purposes: (1) to adapt fusion content to a ubiquitous environment, and (2) to communicate the entire content of selected web pages to users accurately and intuitively. The audio component of u-Pav is text, which is articulated using synthesized speech. For the visual component, the title and lines are shown through a ticker, and keywords and images are animated. The program synchronizes the tickers, the animations, and the speech. Figure 1 shows a display image from u-Pav. The u-Pav can be displayed on a mobile phone screen simply by changing the visual component. The system was designed for use in a business environment. We developed u-Pav using Flash because Flash content can be displayed on mobile phones in Japan.

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As shown in Figure 2, our Web2TV prototype system presents audio components using text read-out types and visual components using character agent animation types. Web2TV looks like a headline news program. Character agents are used to read-out the web content and the system presents images synchronized with the character agent reading out the text. The audio component consists of the character agents’ lines, which are the entire web page text. The visual component consists of the camera work, lighting, studio set, and the character agents and their actions. In both of u-Pav and Web2TV, one notable technique is the extraction of images and its corresponding text from Web by HTML page analysis, both of which are synchronized during replay. Web2Talkshow transforms declarative sentences of a Web page into humorous dialogue based on keywords extracted from the page (see Figure 3). In Japan, there is a traditional form of standup comedy called “manzai”. “Manzai” typically consists of two or three comedians participating in a humorous dialogue, rather like American “stand-up comedy”, or Chinese “xiang sheng”. In the case of two people, one plays the “straight man”, and the other plays the “fool”. We use this “manzai” style in Web2Talkshow. After parsing sentences extracted from a page, the system generates a topic structure consisting of subject terms and detailing terms based on term frequencies. Several QA dialogue templates of “manzai” style are prepared, and the QA dialogue is generated by filling the templates with the topic structure terms. The comparison and the evaluation of the u-Pav, Web2TV and Web2Talkshow is shown in [3].

Fig. 1. uPav: TV-style Web watching

Fig. 2. Web2TV: TV-style Web watching

Fig. 3. Web2Talkshow: TV-style Web watching

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3 Media Conversion from TV to Web 3.1 TV2Web and Webified Video [7-11] Closed captioning (CC) originally allows deaf and hard of hearing/hearing-impaired people and others to read a transcript or dialogue of the audio portion of a video, film, or other presentation. In Japan, totally 40% TV programs of major private broadcasting companies are associated with CCs. NHK is required to accomplish 100% CCs until 2007. Except live programs, more than 50% TV programs have their CCs in 2004. Closed caption data are regarded as a valuable metadata of TV program contents. We have developed a system, called TV2Web and Webified Video, that automatically converts TV video streams with closed caption into a Web-page like content (see Figure 4). The generated Web pages include thumbnails of video units and text data generated from their closed caption data. Users can “browse TV contents” on a normal Web browser. With zooming metaphors, TV2Web can seamlessly alter the level of detail (LOD) of the content being viewed. They can search for favorite TV scenes faster than with analog video equipment. Our experimental result shows 35% improvement for scene search tasks of video streams.

Fig. 4. TV2Web: Browsing TV contents

3.2 Zooming Cross-Media and a Zooming Description Language ZDL [10] Based on the idea of TV2Web and Webified Video, we proposed the “zooming crossmedia” concept that uses zooming to achieve both changes in the level of detail and transitions between media, for contents containing varied media. We proposed a zooming description language (ZDL) based on XML. Unlike existing zooming interfaces, ZDL codes the zooming operation and behavior on the content side. Because ZDL adopts XML coding, we can locate “zooming” as the third interface in the Web document environment after “scrolling” and “anchor clicking.”

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(a)

(c) (b)

Fig. 5. “Zooming cross-media” applied to multimedia meeting minutes

We applied the idea of the “zooming cross-media” to generation of multimedia meeting minutes). Fig. 5 (a) shows an initial state and the colored part is a selected text. Fig. 5 (b) and (c) show zoomed conditions. As the zooming operation proceeds, a detail text part (the lower part of the selected part) and a still/video image appear ((b) and (c) show the still and the video image, respectively). The still image and video image are a snapshot picture and a recorded video that were captured when the discussion corresponding to the selected text occurred. We also applied the idea to implement a browser for search results, where the both of Web pages and video contents are searched for (see Figure 6). Zooming-in a Web page shows a thumbnail image of the page, and zooming-in a TV program contents shows a video thumbnail.

Fig. 6. “Zooming cross-media” applied to browsing search results

The zooming operation and behavior is independently coded from the content structure in ZDL. With ZDL, it is possible to (1) control the zooming of each “zoom object” making up the con-tents, (2) control the degree of zooming by introducing a “zoom rate” parameter, and (3) relate objects mutually and specify zooming propagation between related objects. Zoom object is a target of zooming operation, and consists of text, images, video, sound, and other media-objects making up the contents. A tag expresses

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a template of zoom objects. The “name” attribute specifies the template name and an attribute “match” specifies the base position of the contained media-objects in the content structure code. The media-objects are specified by , , etc. The “select” attribute specifies a relative position of each media-object, with respect to the base (reference) position specified through the “match” attribute. The user selects the desired zooming target and performs the zooming operation (e.g., by pointing to the zooming target by a mouse); however, ZDL does not deal concretely with how zooming targets are selected. A zoom rate is uniquely defined in a zoom object, and is a continuous value (from 0.0 to 1.0) that specifies the degree of zooming. An attribute “zoomFactor” specifies it. Using the , and similar tags, the display state of each zooming object with respect to a given zooming rate is coded. Based on this, it is possible to code the change in the LODs of media-objects, as well as the media transition. Further, the “initZoomFactor” attribute inside the tag represents the initial zooming rate used to initially display the contents. Zoom rate is determined by the zooming operation performed by the user (e.g., the relationship between the movement of the mouse and the zooming rate); however, the ZDL does not deal concretely with how zooming operations are performed. Zoom propagation means that when a zoom object (O) is zoomed, other related objects are also zoomed in conjunction with the object (O). For example, when an upper level object in the content structure is zoomed, lower level objects are also zoomed. A <propagation/> tag expresses this behavior. An attribute “destination” specifies a template name of destination objects and an attribute “select” specifies the position of their media-objects in the content structure. An attribute “relation” specifies the ratio between two changing rates of the zoom rate of the source and the destination objects.

4 Watching TV with Live Chats [12][13] Video indexing is one of the fundamental techniques for achieving such functions. Several conventional indexing methods have been proposed using visual features such as color, camera motion, human faces, texts obtained from closed captions, and classes and volumes of audio information. However, because these methods work based on data provided by broadcast stations, the indices basically reflect only the intentions of TV programmers and stations. Therefore, conventional methods cannot incorporate factors such as the viewpoints and responses of other viewers of a TV program into the functions of scene search, summary presentation, and digest viewing. The popularity of live chats on the Web is growing. Live chat communities on the Internet are virtual communities where viewers of a TV program congregate, and post messages in real time about their impressions of the program or about the program itself. By engaging in the chats while simultaneously viewing TV programs, users can feel a sense of unity by sharing emotions with other viewers. We proposed a method of generating views of TV content from the perspectives of viewers’ using live chats on the Web. In the method, metadata related to viewers’ perspectives, such as important scenes in a program or responses by a particular

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viewer are efficiently extracted through a combination of statistical analysis of chats data and pattern matching. Then, from the extracted metadata, scenes are chronologically listed (see Figure 7(a)) or ranked (see Figure 7(b)) based on the number of viewer response or the responses of all viewers who have preferences that are similar to those of a particular viewer. In this way, new views of TV content, such as chronological views and ranking views, are created. This method provides a new way of viewing TV that reflects the direct responses and perspectives of viewers.

(a)

(b) Fig. 7. TV viewing with live-chats

5 Dynamic TV-Content Augmentation by Complementary Web Pages [14-18] A great deal of technology has been developed to help people access the information they require. With advances in the availability of information, information-seeking activities are becoming more sophisticated. This means that information technology must move to the next stage, i.e., enable users to acquire information from multiple perspectives to satisfy diverse needs. For instance, with the spread of digital broadcasting and broadband Internet connection services, infrastructure for the integration of TV programs and the Internet has been developed that enables users to acquire information from different media at the same time to improve information quality and the level of detail. We proposed a novel content-based join model for data streams (closed captions of videos or TV programs) and Web pages based on the concept of topic structures. We then proposed a mechanism based on this model for retrieving complementary Web pages to augment the content of video or television programs. One of the most notable features of this complementary retrieval mechanism is that the retrieved information is not just similar to the video or TV program, but also provides additional information. We developed an application system called WebTelop, which augments TV programs in real time by retrieving complementary Web pages.

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To extract the topic structure from a TV scene with closed caption, we define two kinds of co-occurrence relationship: the undirected term co-occurrence ratio and the directed term co-occurrence ratio. We define the notion of “subject degree” to determine whether a keyword has a high probability of being a subject term. The subject degree of word is defined by its directed term co-occurrence ratio with other keywords, and its term frequency. That is, if a keyword has high ratios of directed cooccurrence with other keywords within a topic and its term frequency is higher than that of the other keywords, it is considered the subject term. We also defined the “content degree” to determine the content terms of a topic based on the undirected cooccurrence relationship. The content degree of a keyword within a topic is defined as the sum of its undirected term co-occurrence with the subject terms. The retrieval of complementary information consists of three phases: extract the topic structure from closed caption data, generate a query using the extracted topic structure to extend the topic structure, and execute the query and rank the retrieved results. Figure 8 shows a screen image of our WebTelop. Figure 9 shows the Webtelop function incorporated into a multimedia search-result browsing interface, where complementary Web pages are automatically retrieved while viewing a TV program.

Fig. 8. WebTelop: Automatic search of complementary Web pages by TV closed caption

Fig. 9. Application of WebTelop to integrated search of Web and TV

6 Searching for TV with Web [19] As a first step towards achieving such a next-generation search engine, we propose a search engine that handles Web content (with hyperlinks) and TV programs (without them), that performs integrated searching of this content, and that performs chain searches for content related to each search result if needed. An integrated search is achieved by generating integrated indices based on keywords obtained from TV programs and Web content and by ranking the content in the indices. Chain searches for related content are achieved by ranking by calculating the similarities between the content in a search result and the other content in the integrated indices. Our search engine can:

Towards New Content Services by Fusion of Web and Broadcasting Contents z z z

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simultaneously search for both Web content and TV programs that match a given keyword query, search for TV programs having content related to the Web content being browsed right now, search for Web content having content related to the TV program being watched right now.

Preliminary testing of a prototype system (see Figure 9) validated the approach used in our method. The integrated search of Web content and recorded TV programs is performed based on keyword queries made by the user. Queries of one keyword or more are transformed into a common form using the indices in the integrated index DB. Web content and recorded TV programs are retrieved and ranked in an integrated manner in descending order based on the calculated similarities (integrated ranking). While viewing a Web page or recorded TV program in the integrated search result, users may want to search for related Web pages or recorded TV programs by making the viewed content itself a new query. In this case, a search similar to the integrated search is done by calculating the integrated similarity. This enables chains of searches by making the viewed content the new query (see Figure 8). The interface for displaying the search results works as follows: first, the integrated search results are shown in a list, and then, when a particular result is selected by the user, more details on the result and the top-ranked related content obtained in a chain search will appear according to how the user zooms. First, an integrated search is performed using a given query, followed by a chain search based on each content item in the search result. The result of the integrated search and chain search, written in XHTML, is transformed using a description language ZDL that can control the display appearance using a zooming metaphor, outputting the data in XHTML+JavaScript, which can be displayed at different levels of details according to the user’s interest. The output data can be browsed via the zooming operation using a general browser. One more important remained problem of the integrated search for Web and TV program contents is how to rank the search results. As for Web pages, there are already established ranking methods such as Google’s PageRank. PageRank assumes the existence of hyperlinks for pages. On the other hand, TV program contents do not have any hyperlinks. When searching for TV programs and Web pages, we need a ranking algorithm that can handle not only Web pages but also TV program contents. Recently, Kabutoya et al. [20][21] proposed a way to provide a virtual PageRank evaluation to contents without hyperlinks. This is to simulate PageRank algorithm for contents without hyperlinks based on the similarity analysis of TV program contents and Web pages.

7 Concluding Remark In this paper, we describe our research activities of the research on fusion of the Web and TV broadcasting [22]. Especially, we reported here our technologies for media conversion between TV and Web contents, watching TV with live chats, dynamic TV-content augmentation by complementary Web pages, and a search engine for TV and Web contents.

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Acknowledgment This research was supported in part by Informatics Research Center for Development of Knowledge Society Infrastructure (Kyoto University COE program of the Ministry of Education, Culture, Sports, Science and Technology, Japan) and the research of NICT (National Institute of Information and Communications Technology) Media Interaction Group. The author would like to thank all the COE members, members in Tanaka Lab., Kyoto University and colleagues of NICT Media Interaction Group for their collaboration.

References 1. Tanaka, K., Nadamoto, A., Kusahara, M., Hattori, T., Kondo, H., Sumiya, K.: Back to the TV: Information Visualization Interfaces based on TV-Program Metaphors. In: Proc. of IEEE International Conference on Multimedia and Expo(III), pp. 1229–1232 (2000) 2. Nadamoto, A., Hayashi, M., Tanaka, K.: Web2Talkshow:Transforming Web Content into TV-program-like Content Based on the Creation of Dialogue. In: Proc. of the 14th International World Wide Web Conference (WWW2005), pp. 1144–1145 (May 2005) 3. Nadamoto, A., Tanaka, K.: Complementing your TV-viewing by web content automatically-transformed into TV-program-type content. In: Proc. of the 13th Annual ACM International Conference on Multimedia (ACM Multimedia2005), pp. 41–50 (November 2005) 4. Nadamoto, A., Jatowt, A., Hayashi, M., Tanaka, K.: Web2Talkshow: Web content Transformed into Humorous Dialogue-based TV-program-like Conte. In: Maybury, M., Stock, O., Wahlster, W. (eds.) INTETAIN 2005. LNCS (LNAI), vol. 3814, pp. 253–258. Springer, Heidelberg (2005) 5. Uwada, H., Nadamoto, A., Kumamoto, T., Hamabe, T., Yokozawa, M., Tanaka, K.: Automated Content Transformation with Adjustment for Visual Presentation Related to Terminal Types. In: Zhou, X., Li, J., Shen, H.T., Kitsuregawa, M., Zhang, Y. (eds.) APWeb 2006. LNCS, vol. 3841, pp. 1214–1217. Springer, Heidelberg (2006) 6. Nadamoto, A., Kumamoto, T., Uwada, H., Hamabe, T., Yokozawa, M., Tanaka, K.: uPaV: Automatic Transformation of Web Content into TV-like Video Content for Ubiquitous Environment. In: Proc. of the IEEE 7th International Conference on Mobile Data Management (MDM’06), p. 61 (May 2006) 7. Sumiya, K., Munisamy, M., Tanaka, K.: TV2Web: Generating and Browsing Web with Multiple LOD from Video Streams and their Metadata. In: Proc. International Conference on Informatics Research for Development of Knowledge Society Infrastructure (ICKS’04), pp. 158–167. IEEE Computer Society Digital Library, Los Alamitos (2004) 8. Sumiya, K., Munisamy, M., Tanaka, K.: TV2Web: Generating and Browsing Web with multiple LOD from Video Streams and Their metadata. In: Proc. of the 13th International World Wide Web Conference (WWW2004), pp. 398–399 (May 2004) 9. Miyamori, H., Tanaka, K.: Webified Video: Media Conversion from TV Program to Web Content and their Integrated Viewing Method. In: Proc. of the 14th International World Wide Web Conference (WWW2005), pp. 946–947 (May 2005) 10. Araki, T., Miyamori, H., Minakuchi, M., Kato, A., Stejic, Z., Ogawa, Y., Tanaka, K.: Zooming Cross-Media: A Zooming Description Language Coding LOD Control and Media Transition. In: Andersen, K.V., Debenham, J., Wagner, R. (eds.) DEXA 2005. LNCS, vol. 3588, pp. 260–269. Springer, Heidelberg (2005)

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11. Miyamori, H., Tanaka, K.: Webified Video: Media Conversion from TV Programs to Web Content for Cross-Media Information Integration. In: Andersen, K.V., Debenham, J., Wagner, R. (eds.) DEXA 2005. LNCS, vol. 3588, pp. 176–185. Springer, Heidelberg (2005) 12. Miyamori, H., Nakamura, S., Tanaka, K.: Personal TV Viewing by Using Live Chat as Metadata. In: Proc. of the 14th International World Wide Web Conference (WWW2005), pp. 948–949 (May 2005) 13. Miyamori, H., Nakamura, S., Tanaka, K.: Generation of Views of TV Content Using TV Viewers’ Perspectives Expressed in Live Chats on the Web. In: Proc. of the 13th Annual ACM International Conference on Multimedia (ACM Multimedia2005), pp. 853–861 (November 2005) 14. Ma, Q., Tanaka, K.: WebTelop: Dynamic TV-Content augmentation by Using Web Pages. In: Proc. of IEEE International Conference on Multimedia and Expo (ICME2003), vol. 2, pp. 173–176 (2003) 15. Ma, Q., Tanaka, K.: Topic-Structure Based Complementary Information Retrieval for Information Augmentation. In: Yu, J.X., Lin, X., Lu, H., Zhang, Y. (eds.) APWeb 2004. LNCS, vol. 3007, Springer, Heidelberg (2004) 16. Miyamori, H., Ma, Q., Tanaka, K.: WA-TV: Webifying and Augmenting Broadcast Content for Next-Generation Storage TV. In: Proc. of IEEE International Conference on Multimedia & Expo (ICME2005) (July 2005) 17. Ma, Q., Tanaka, K.: Context-Sensitive Complementary Information Retrieval for Text Stream. In: Andersen, K.V., Debenham, J., Wagner, R. (eds.) DEXA 2005. LNCS, vol. 3588, pp. 471–481. Springer, Heidelberg (2005) 18. Ma, Q., Tanaka, K.: Topic-Structure-Based Complementary Information Retrieval and Its Application. ACM Transactions on Asian Language Information Processing 4(4), 476–503 (2005) 19. Miyamori, H., Stejic, Z., Araki, T., Minakuchi, M., Ma, Q., Tanaka, K.: Towards Integration Services for Heterogeneous Resources: An Integrated Search Engine for Web Content and TV Programs. In: Proc. of the 2nd International Conference on Semantics, Knowledge, and Grid (SKG’06), p. 16. IEEE Computer Science Digital Library, Los Alamitos (2006) 20. Kabutoya, Y., Yumoto, T., Oyama, S., Tajima, K., Tanaka, K.: Quality Estimation of Local Contents Based on PageRank Values of Web Pages. In: ICDE Workshops 2006, p. 134 (2006) 21. Kabutoya, Y., Yumoto, T., Oyama, S., Tajima, K., Tanaka, K.: Comparative Quality Evaluation of TV Contents Based on Web Analysis. In: ICDE Workshops 2007 (April 2007) 22. Tanaka, K.: Content Integration from Web and Broadcast Information Sources. In: Proc. International Conference on Informatics Research for Development of Knowledge Society Infrastructure (ICKS’04), pp. 99–106. IEEE Computer Society Digital Library, Los Alamitos (2004)

Pattern Discovery from Graph-Structured Data - A Data Mining Perspective Hiroshi Motoda Asian Office of Aerospace Research & Development Air Force Office of Scientific Research, Tokyo, Japan [email protected] Abstract. Mining from graph-structured data has its root in concept formation. Recent advancement of data mining techniques has broadened its applicability. Graph mining faces with subgraph isomorphism which is known to be NP-complete. Two contrasting approaches of our work on extracting frequent subgraphs are revisited, one using complete search (AGM) and the other using heuristic search (GBI). Both use canonical labelling to deal with subgraph isomorphism. AGM represents a graph by its adjacency matrix and employs an Apriori-like bottom up search algorithm using anti-monotonicity of frequency. It can handle both connected and dis-connected graphs, and has been extended to handle a tree data and a sequential data by incorporating a different bias to each in joining operators. It has also been extended to incorporate taxonomy in labels to extract generalized subgraphs. GBI employs a notion of chunking, which recursively chunks two adjoining nodes, thus generating fairly large subgraphs at an early stage of search. The recent improved version extends it to employ pseudo-chunking which is called chunkingless chunking, enabling to extract overlapping subgraphs. It can impose two kinds of constraints to accelerate search, one to include one or more of the designated subgraphs and the other to exclude all of the designated subgraphs. It has been extended to extract paths and trees from a graph data by placing a restriction on pseudo-chunking operations. GBI can further be used as a feature constructor in decision tree building. The paper explains how both GBI and AGM with their extended versions can be applied to solve various data mining problems which are difficult to solve by other methods.

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Introduction

Recent advancement of data mining techniques has made it possible to mine from complex structured data. Since structure is represented by proper relations and a graph can easily represent relations, knowledge discovery from graph-structured data (graph mining) poses a general problem for mining from structured data. Some examples amenable to graph mining are finding functional components from their behavior, finding typical web browsing patterns, identifying typical substructures of chemical compounds, finding subsequences of DNA typical to some functions and discovering diagnostic rules from patient history records. The first example above is also called concept formation and has been a subarea of artificial intelligence research since many years ago. One such work is H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 12–22, 2007. c Springer-Verlag Berlin Heidelberg 2007 

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Lower intermediate concept

Base level concept

Upper intermediate concept

Fig. 1. Hierarchical functional abstraction of a complex object

Initial description (physical level)

abstraction

Top level concept

Generated description (logical level)

by Yoshida and Motoda[11], where a concept is defined as something that minimizes inference load and the problem is finding a mapping that satisfies this requirement. This can be amenable to graph mining problem. The results of qualitative simulation of a digital circuit was mapped to a set of directed graph from which hierarchical concepts such as “pull down transistor”, “exclusive OR” and “carry chain” were extracted. The idea behind the concept formation from a graph-structure data and its application to a digital circuit is schematically shown in Figs. 1 and 2. Similar work is reported in [1]. output

Reinterpretation of the results

input1

input2

input3

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output

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Extraction

Conversion to graphs

input1

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Fig. 2. Concept formation for a digital circuit

Graph mining is based on finding some typicality from a vast amount of graphstructured data. What makes it typical depends on each domain and each task. Most often frequency which has a good property of anti-monotonicity is used to discover typical patterns. Some measure such as information gain or χ2 are also used but since they are not monotonic with respect to graph subsumption, special care must be taken. Graph mining inevitably faces with subgraph isomorphism which is known to be NP-complete. For example, three subgraphs in Fig. 3 are all isomorphic and it is not easy to find all of them in a huge set of graphs. Two contrasting approaches have been taken to handle this problem, one searching all possible space efficiently devising a good data structure with an appropriate indexing and the other avoiding exhaustive search using Fig. 3. Three isomorphic graphs a greedy algorithm with good heuristics. In this paper, our own work on these two approaches is revisited, AGM family for the former and GBI family for the latter, and how these are applied to solve difficult problems which are not easily solved my other approaches is explained. Both approaches use canonical labeling to handle subgraph isomorphism.

2

AGM Family - Algorithm and Its Applications

AGM [4,3] represents a graph by its adjacency matrix. Labels of nodes and edges are assigned natural numbers. The adjacency matrix is defined as follows. First

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node labels are ordered according to their values and grouped according to this order, and a square matrix is formed. An element of the matrix is the edge label if there is an edge between the corresponding node pair and 0 if there is no edge between them. Since the same graph can be represented by multiple adjacency matrices because any permutation of the raw and the column within the same node label represents the same graph, each matrix is given a code and a code which gives a minimum (maximum) is defined as the canonical label. A code consists of two parts, 3 1 1 2 3 3 1 0 1 0 1 0 Grey=1 the first part is the code repre1 2 1 1 0 1 1 0 Dark grey =2 5 X = 2 0 1 0 0 1 senting the node label orderVertex ID 5 Black=3 3 1 1 0 0 1 4 G>D>B ing and the second part is the 3 0 0 1 1 0 Code(X )=112331011100011 code consisting of the vertically scanned matrix elements. 1 1 2 3 3 1 1 2 3 3 1 0 1 1 0 1 1 0 1 1 1 0 Thus, if the canonical label of 1 1 0 0 1 0 1 1 0 0 1 0 2 1 0 0 1 0 2 1 0 0 0 1 two graphs are the same, they 3 0 0 1 0 1 3 1 1 0 0 1 3 1 1 0 1 0 are identified as isomorphic. 3 0 0 1 1 0 CODE(X )=112331101100011 CODE(X )=112331100111001 > An example is given in Fig. 4. 1 1 2 3 3 1 1 2 3 3 The graph has 5 nodes with 3 1 0 1 0 0 1 1 0 1 0 1 0 1 1 0 1 0 1 1 1 0 1 1 0 node labels and no edge labels. 2 0 1 0 1 0 2 0 1 0 0 1 3 0 0 1 0 1 3 1 1 0 0 1 The node labels are ordered as 3 1 1 0 1 0 3 0 0 1 1 0 grey (=1), dark-grey (=2) and > CODE(X )=112331011100011 > CODE(X )=112331010011101 black (=3). The first part is Fig. 4. Adjacency matrix and canonical label 11233 and the second part is 1011100011. The four adjacency matrices below represent the same graph. These are different only in the node numbering within the same label. The canonical label is 112331010011101 if the minimum is chosen. Once we have defined a canon1 1 1 1 2 3 1st matrix 2nd matrix ical label, candidate subgraph can 1 1 1 1 3 1 1 1 1 2 0 1 1 0 1 1 0 1 1 0 1 0 1 1 0 1 be generated and searched using an 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 > 1 0 0 0 0 0 1 0 0 0 0 Apriori-like bottom up search algo1 0 0 0 0 0 0 0 0 0 0 (< ) 0 0 0 0 0 0 0 0 0 0 rithm. In Apriori an itemset Pk of 1 0 0 0 0 * 1 0 0 0 0 1 0 0 0 0 1 0 0 0 * 0 size k can be generated by 2 freCanonical form *=1 *=0 5 5 5 6 5 6 2 2 quent itemsets of size k − 1 that 1 2 2 1 1 4 4 3 share the same k − 2 items. This 3 4 3 4 3 is called a join operation. Then Fig. 5. Join operation of AGM it verifies that all the subsets of size k − 1 in Pk are frequent or not, and if not it discards Pk . Finally the support of Pk is checked to see if it satisfies the min. support condition. The similar procedure can be applied to graphs. Two graphs of size k − 1 can be joined to generate a graph of size k. Figure 5 shows an example when k = 5. Here, the first matrix is always a canonical form (matrix with canonical label). What is different is that the generated matrix has missing elements. All the possible alternatives must be considered. If we have β labels for edges, there are β + 1 adjacency matrices for undirected graph and its square for directed graph. Thus, the number of subgraphs rapidly increases with size. AGM 5

5

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computation time [sec]

can handle general graphs, both connected and disconnected, both directed and undirected, and both labeled and unlabeled. Since AGM is most generic, subgraph restricting the types of sub- frequent frequent connected subgraph frequent tree graphs is straightforward. It Output frequent ordered tree frequent is easy to place a bias path, sequence in join operation. B-AGM, additional path, connected ordered ・・・ specific bias subgraph tree biased AGM, can handle sequence subgraph tree normalize connected subgraphs, tree, Generate-Candidate B-AGM Count join subgraph check canonicalize framework canonical coding ordered tree, path and sequence [5]. The conceptual Ordered Path, - - - - - Input Tree minimum support Graph scheme of the AGM family Tree Sequence is depicted in Fig. 6. Performance of AGM was Fig. 6. Mining Various substructures by B-AGM Framework evaluated using PTE dataset and is shown in Figure 7. This is a small dataset (the average graph size is 27). The connected version of B-AGM, AcGM favorably compares with the other later developed graph mining algorithms[10,6]. All do the complete search. If we limit the search to induced subgraphs, B-AGM is most efficient. AGM family has been 10000 applied to many problems, B-AGM (Connected Graph) B-AGM (Induced Connected Graph) mostly to chemical com1000 FSG (Connected Graph) pound analysis such as mugSpan (Connected Graph) tagenicity, HIV and 100 dopamine, and a few others such as Web browsing 10 history analysis and consumer behavior analysis. In 1 1 2 3 4 5 6 7 8 9 10 the analysis of mutagenicity minimum support [%] of amino acid compounds, AGM was able to find that Fig. 7. Performance evaluation of AcGM (PTE data, Average size of graphs=27) compounds that have hydrogen next to the nitro substituent in a benzene ring can be active. The results was obtained by using only the topological information of graphs. Later analysis of three dimensional structure of these compounds revealed that in case of hydrogen there is no steric hindrance that destroys the coplanarity to a benzene ring but a more complicated one does destroy the coplanarity. This explains the mining results. When applied to HIV data, AGM was able to find a subgraph which is very close to what is called azido-thymidine (AZT), a well known anti-HIV medicine. The found subgraph is shown in Fig. 8. This is a three class problem and the task is to find all the frequent subgraphs that are greater than the minimum support in the active compounds and less than the maximum support in the inactive compounds. AGM was extended to handle taxonomy in node labels. Use of taxonomy makes it possible to find more abstract concepts even if there are not enough

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number of frequent subgraphs at the base level description. The problem is conceptually easy but overgeneralization must be prevented. It is possible to extract the least general subgraph by discarding more general subgraphs that have the same total occurrence where multiple count is allowed if there are more than one occurrence of a subgraph in a graph. This technique was applied to PTE dataset and AGM O found more discriminative H N N N O NH subgraphs together with the N OH O taxonomy itself. AGM was H C further extended to handle (b) Azidothymidine (AZT) O three dimensional structure N N N Anti-HIV medicine N N O without modifying the alO O HO O gorithm (3D-AGM). Edge H OH C N length was discretized into O (a) discovered pattern N OH O a finite number and was Active : 64 / 422 HC Inactive : 16 / 42,265 given different labels. With (c) Thymine this approach, when applied to classification of dopamine Fig. 8. Discovered subgraph by AcGM for HIV data antagonists, 3D-AGM was able to find more discriminative substructures than the standard AGM. An interesting application of AGM is consumer behavior analysis. Each time a person goes to a grocery store, items purchased together are recorded and this can be used for later analysis. The purchase history can be mapped to a directed graph. When applied to a data for a beer market, AGM discovered that a particular brand ”Asahi super dry” is sold together with fresh fish and fruit. This hypothesis was tested in collaboration with the grocery store by rearranging the items so that these three are placed close. Interestingly the sales of beer went up more than double and fish sales went up also by 12% during the promotion [9]. +

+

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+

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GBI Family - Algorithm and Its Applications

GBI [7] employs a notion of chunking, which recursively chunks two adjoining nodes, thus generating fairly large subgraphs at an early stage of search. GBI also uses canonical labeling to solve the graph isomorphism problem. Due to the nature of chunking, GBI is able to handle only connected graphs. Instead, GBI can use any criterion that is based on the frequency of paired nodes. However, for finding a subgraph that is of interest any of its subgraphs must be of interest because of the nature of repeated chunking. The frequency measure satisfies this monotonicity property. However, if the criterion chosen does not satisfy this property, repeated chunking may not lead to finding good subgraphs even though the best pair based on the criterion is selected at each iteration. To resolve this issue GBI uses two criteria, one based on frequency measures for chunking and the other for finding discriminative subgraphs after chunking. The latter criterion does not necessarily exhibit the monotonicity property. Any function that is discriminative can be used, such as Information Gain, Gain Ratio, Gini Index and others.

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The original GBI contracts graphs after chunking and thus the size of the graphs progressively becomes smaller as chunking proceeds and thus the computational complexity is almost quadratic to the graph size. The basic algorithm is given in Fig. 9. However, the biggest problem GBI(G) with this approach is that Enumerate all the pairs Pall in G it cannot find overlapping Select a subset P of pairs from Pall (all the pairs in G) based on typicality criterion subgraphs. Later version inSelect a pair from Pall based on chunking troduced a beam search to criterion alleviate this problem (BChunk the selected pair into one node c GBI), but it was not enough. Gc := contracted graph of G The recent improved version while termination condition not reached Cl-GBI extends it to emP := P ∪ GBI(Gc ) ploy pseudo-chunking which return P is called chunkingless chunking, enabling to extract overFig. 9. Algorithm of GBI lapping subgraphs [8]. In Cl-GBI, the selected Pseudo-node pairs are registered as new 1 1 1 1 PseudoPseudonodes and assigned new la- 6 1 1 3 3 3 3 Chunking 6 Chunking 2 2 2 2 4 bels but are never chunked 4 4 3 4 3 1 1 and the graphs are never 3 3 6 6 5 5 2 2 “contracted” nor copied into respective states as in B1 2 1 3 3 4 1 3 2 1 2 1 2 3 1 GBI. In the presence of the 2 3 3 Pair to be Pair to be chunked 2 3 chunked 2 pseudo nodes (i.e., newly 3 4 2 1 assigned-label nodes), the 4 3 2 frequencies of pairs consisting of at least one Fig. 10. Pseudo-chunking of Cl-GBI new pseudo node are counted. The other is either one of the pseudo nodes including those already created in the previous steps or an original node. The most frequent pairs with the number equal to the beam width specified in advance are selected among the remaining pairs and the new pairs which have just been counted for their frequencies. These steps are repeated for a predetermined number of times, each of which is referred to as a level. Those pairs that satisfy a typicality criterion (e.g., pairs whose information gain exceeds a given threshold) among all the extracted pairs are the output of the algorithm. A frequency threshold is used to reduce the number of pairs being considered to be typical patterns. Another possible method to reduce the number of pairs is to eliminate those pairs whose typicality measure is low even if their frequency count is above the frequency threshold. The two parameters, beam width and number of levels, control the search space. The frequency threshold is another important parameter. The algorithm of Cl-GBI is depicted in Fig. 10. In contrast to GBI and B-GBI graph size remains the same due to pseudo-chunking, and thus, the number of pairs to pseudo-chunk progressively

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increases (computational complexity is now exponential to the size). It searches a much larger portion of subgraphs and in fact, search can be complete by setting the beam width and the level large enough. Like B-AGM, the subgraphs to be searched can be limited to paths, acyclic subgraphs and subgraphs (both induced and general) by placing appropriate constraints when chunking the pairs. Performance of Cl-GBI Table 1. Performance evaluation of Cl-GBI (PTE was evaluated using the Data) same PTE dataset as in General subgraphs AGM. The results are shown Frequency threshold (í) 30% 20% 10% in Table 1. The number No. of freq. patterns 68 190 844 of beam width and the Beam width (b) 10 10 10 No. of levels (N) needed 12 18 84 number of levels to find all the subgraphs are shown. Induced subgraphs The subgraphs found are Frequency threshold (í) 30% 20% 10% confirmed to be the same as No. of freq. patterns 49 139 537 what were found by AcGM. Beam width (b) 10 10 10 Number of levels (N) needed 4 7 18 GBI family does not aim to find all the possible subgraphs but rather it attempts to find reasonably good subgraphs at an early stage in the search without searching all the space. To accomplish this, various techniques are introduced to improve its search efficiency. Two of them are mentioned here. The first one is to impose constraints to restrict search. Two kinds of constraints can be conceived. One is to extract only patterns that are of interest to domain experts or related to domain knowledge, and the other is to exclude extracting patterns uninteresting to domain experts or unrelated to domain knowledge. Both are called INpattern constraint and EXpattern constraint. Their implementation is straightforward. However, subgraph isomorphism checking is needed to check these constraints, and thus the number of checking must be reduced as much as possible. For this, necessary conditions for two subgraphs to be isomorphic, e.g. degree of node, node and edge labels, etc. are used. If these conditions are not satisfied, there is no need to compare the two subgraphs. The second one is to use pruning. Frequency satisfies Fig. 11. Using Cl-GBI as a feature constructor in deanti-monotonicity but infor- cision tree building mation gain, and χ2 do not satisfy this constraint. However, they have a nice property of being convex with respect to the arguments. In this case it is possible to set an upperbound to them. Suppose we have a subgraph P and want to chunk it with other subgraph, generating a larger subgraph Q. The information gain of Q is bounded

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by the maximum of these extreme cases where the input instances containing Q only consists of a single class. If the upperbound for Q is less than or equal to the so far found best information gain or χ2 , then there is no need to pseudo chunk P . If the final task is clasDT-GBI(D) sification, graph mining has Create a node DT for D to be combined with clasif termination condition reached sifier construction. For this return DT task, GBI family can be else used as a feature construcP := GBI(D) (with the number of chunking specified) tor in decision tree buildSelect a pair p from P ing [2] (See Fig. 11). DeDivide D into Dy (with p) and Dn (withcision tree constructed this out p) way was named DT-GBI or Chunk the pair p into one node c DT-ClGBI. Each node has Dyc := contracted data of Dy a subgraph for which an infor Di := Dyc , Dn DTi := DT-GBI(Di ) put data is tested. Cl-GBI Augment DT by attaching DTi as its is run recursively reusing child along yes(no) branch whatever can be inherited return DT from the previous runs. The beam width and depth level Fig. 12. Algorithm of DT-ClGBI are parameters that can be set at each tree level. The algorithm of DT-ClGBI is given in Fig. 12. The performance of DT-ClGBI was evaluated by a synthetic dataset. Directed graphs are randomly generated and equally divided into two classes, active and inactive. Four kinds of basic patterns were embedded in class active with equal probability. The average size of the graphs is changed from 30 to 50.

Fig. 13. An example of input graph classified as positive by DT-ClGBI

Fig. 14. Test pattern found in the input graph in Fig.13

The expectation was that these basic patterns appear in the test nodes of the decision tree, but the result was not exactly what was expected. The patterns chosen at each node were subgraphs of the basic patterns. Figure 13 shows one of the decision trees obtained and an input graph that failed in the first and the

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second tests but passed the third test and classified as positive. A graph of size 50 is not a large graph, but for humans it is complicated enough and not easy to check if it includes the subgraph used for the test. Both DT-GBI and DTClGBI were applied to a Date ALB CHE D-BIL GOT GOT_SD GPT medical dataset of chronic Date ALB CHE D-BIL GOT GOT_SD GPT Raw Data Date ALB CHE D-BIL GOT GOT_SD GPT hepatitis B and C which has Conversion to table format 19820714 N L L N 1 N 19820714 N L L H 1 H Feature selection 19820912 N NL L H L H H 2 1 H 19820714 19820912 N L L H 2 been collected at the medical Deletion of unnecessary 19830311 N N 3 H2 19820912 L H L L H HH 19830311 N 1 H symbols 19830311 N L H 1 H department of Chiba UniverAveraging (e.g. one month) Discretization sity over 20 years from 1982 Conversion to graphs Artificial link to 2001. It is a large un8 months N N later cleansed time series data and H ALB 2 ALB L N GPT 3 N L has inconsistent measureCHE GPT CHE GOT_SD GOT_SD ALB ments and many missing val･･･ ･･･ ･･･ 2 months 6 months D-BIL later D-BIL later ues. It was hoped that graph GOT-SD L D-BIL GOT GOT GOT H H 1 H mining can extract typical Value N H Test correlation among different tests across time. Figure 15 Fig. 15. Data preprocessing and conversion to a dishows how the time series rected graph data of each patient is converted to a directed graph after steps of preprocessing. The star shaped graph represents the various test data that are averaged over Pattern 211 LC=38, n-LC=58 two months at a particu(5, 7) N lar time point. These are Y Pattern 212 connected sequentially and LC=6,LC n-LC=0 LC=32, n-LC=58 (3, 0) (2, 7) artificial links are added N Y up to two years in fuLC Pattern 213 ture to represent possible diLC=6, n-LC=0 LC=26, n-LC=58 (1, 7) (1, 0) rect effects of the past to Y N the future. The main task Pattern 214 Pattern 215 LC=10, n-LC=8 LC=16, n-LC=46 is classifying patients with (1, 4) (1, 7) different fibrosis stages. The Y N Y N non-LC non-LC LC LC data used was 500 days LC=2, n-LC=8 LC=8, n-LC=0 LC=10, n-LC=3 LC=6, n-LC=43 (1, 3) (0, 1) (0, 1) (0, 2) before and after the first biopsy. Feature selection resulted in 32 tests (measure- Fig. 16. Optimal decision tree to classify fibrosis ments) as the useful at- progress tributes. and the average size of the graphs was about 300. For each experiment 10 fold cross validation was run 10 times. Beam width was set to 15 and the number of levels to 20 at every node of the tree. The average error of distinguishing between F4 stage (liver cirrhosis) and F0+F1 stages (almost healthy) is 12.5% and its standard deviation was 2.12%. The average error of distinguishing between F4 stage and …

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F3+F2 stages (not cirrhosis yet but close to it), which is a more difficult task, was 23.5% with the standard deviation of 2.39%. One of the decision trees is shown in Fig. 16. The patterns are fairly complex and these were exactly what we were expecting. However, unfortunately the medical doctors could not interpret these patterns. They thought the techniques interesting but did not accept the results blindly. Detailed analysis revealed that there are groups of patients whose data behave strangely. This lead to a two stage analysis in which first these abnormal patients were separated from the rest and then a classifier was constructed for each group. The weighted average error of normal and abnormal patients reduced considerably to 7.3% and the tree became very simple and easily interpretable.

4

Conclusion

The paper discussed the use of graph-structured data in data mining perspective. Graph mining is an important area for extracting useful knowledge from structured data. Many interesting and difficult problems can be solved with this approach. However, graph mining is a computationally heavy task. Good algorithm and good data structure are needed. However, what is more important is a right problem setting to produce human understandable solutions. Human’s cognitive capability is limited.

References 1. Cook, D.J., Holder, L.B.: Graph-based data mining. IEEE Intelligent Systems 15(2), 32–41 (2000) 2. Geamsakul, W., Yoshida, T., Ohara, K., Motoda, H., Yokoi, H., Takabayashi, K.: Constructing a decision tree for graph-structured data and its applications. Journal of Fundamenta Informatiae, Special issue on Advances in Mining Graphs, Trees and Sequence 66(1-2), 131–160 (2005) 3. Inokuchi, A., Washio, T., Motoda, H.: Complete mining of frequent patterns from graphs: Mining graph data machine learning, 50(3), 321-354 (2003) 4. Inokuchi, A., Washio, T., Motoda, H.: An apriori-based algorithm for mining frequent substructures from graph data. In: Proc. of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, pp. 13–23 (2000) 5. Inokuchi, A., Washio, T., Motoda, H.: General framework for mining frequent subgraphs from labeled graphs. Journal of Fundamenta Informatiae, Special issue on Advances in Mining Graphs, Trees and Sequence 66(1-2), 53–82 (2005) 6. Kuramochi, M., Karypis, G.: An efficient algorithm for discovering frequent subgraphs. IEEE Trans. Knowledge and Data Engineering 16(9), 1038–1051 (2004) 7. Matsuda, T., Motoda, H., Washio, T.: Graph-based induction and its applications. Advanced Engineering Informatics 16(2), 135–143 (2002) 8. Nguyen, P.C., Ohara, K., Motoda, H., Washio, T.: Cl-gbi: A novel approach for extracting typical patterns from graph-structured data. In: Proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining (2005)

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9. Yada, K., Motoda, H., Washio, T., Miyawaki, A.: Consumer behavior analysis by graph mining technique. New Mathematics and Natural Computation 2(1), 59–68 (2005) 10. Yan, X., Han, J.: gspan: Graph-based structure pattern mining. In: Proc. of the 2nd IEEE International Conference on Data Mining, pp. 721–724 (2002) 11. Yoshida, K., Motoda, H.: Clip: Concept learning from inference pattern. Journal of Artificial Intelligence 75(1), 63–92 (1995)

A Collocation-Based WSD Model: RFR-SUM Weiguang Qu1,2 , Zhifang Sui1 , Genlin Ji2 , Shiwen Yu1 , and Junsheng Zhou2 1

2

Institute of Computational Linguistics, Peking Univ., Beijing 100871, China {wgqu, szf, yusw}@pku.edu.cn Department of Computer Science, Nanjing Normal Univ., Nanjing 210097, China {wgqu, glji, zhoujunsheng}@njnu.edu.cn

Abstract. In this paper, the concept of Relative Frequency Ratio (RFR) is presented to evaluate the strength of collocation. Based on RFR, a WSD Model RFR-SUM is put forward to disambiguate polysemous Chinese word sense. It selects 9 frequently used polysemous words as examples, and achieves the average precision up to 92.50% in open test. It has compared the model with Na¨ıve Bayesian Model and Maximum Entropy Model. The results show that the precision by RFR-SUM Model is 5.95% and 4.48% higher than that of Na¨ıve Bayesian Model and Maximum Entropy Model respectively. It also tries to prune RFR lists. The results reveal that leaving only 5% important collocation information can keep almost the same precision. At the same time, the speed is 20 times higher.

1

Introduction

Word sense disambiguation (WSD) is a central open problem at the lexical level of Natural Language Processing (NLP). Word sense multiplicity is a very general characteristic of natural language. For example, the polysemous word ratio in Modern Chinese Dictionary is 14.8%. In Chinese corpus, the occurrence frequency of polysemous words is around 42%[1]. Ambiguous words pose continuing problems for NLP applications. They can lead to irrelevant document retrieval in Information Retrieval systems and inaccurate translation in Machine Translation systems. For example, the Chinese word ”黄色” has two different senses, one of which can be translated into English as ”yellow” in ”黄色香蕉”(yellow banana), and another as ”pornographic” in ”黄色小说”(pornographic novel). The aim of WSD is to assign an appropriate sense to an occurrence of word in a given context automatically. Various approaches have been proposed to deal with the WSD problem, including rule-based approaches, knowledge or dictionary based approaches, corpusbased approaches, and hybrid approaches[2]. Among these approaches, the corpusbased supervised machine learning method is the most successful way, where contextual features have been used mainly to distinguish ambiguous words[3]. The learning algorithms applied include: decision tree, decision list, neural networks, Na¨ıve Bayes, boosting, Winnow, SVM, VSM, maximum entropy. The assumption behind these methods is that it is usually possible to determine the sense H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 23–32, 2007. c Springer-Verlag Berlin Heidelberg 2007


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of an ambiguous word by referring to its context, and thus all of the methods build a classifier using features representing context information[4]. Recently, many researchers realize the importance of collocation and use collocation as WSD resources[3, 5, 6]. In [7], Li uses Na¨ıve Bayesian Classifier as the core algorithm. The primary purpose of using collocation is to improve precision. In [6], Hoa uses maximum entropy approach on WSD and introduces collocation features, syntactic features and semantic features in maximum entropy model. The experiment shows that adding more features from richer levels of linguistic annotation yielded no significant improvement over using only collocation features in Chinese WSD. Since manual collection of collocation resource is expensive, it is desirable to develop automatic collocation extraction tools. Although some progress has been made in the aspects of automatic collocation extraction[8, 9], the problem of manual selection or collocation proofreading remains unsolved in practice. In fact, the meaning of a sentence is realized by the composition of every word with a particular order. This means that every word in the sentence other than a particular collocation alone makes contribution to WSD. In this paper, we propose relative frequency ratio (RFR) of word to evaluate the collocation strength of words in context of each sense, and put forward a model called RFR-SUM, which sums up the RFR value of the words in context of each sense, and makes decision base on the RFR summation. More details will be described in section 2. The main work and innovations in this paper are as follows: 1. Taking Relative Frequency Ratio (RFR) as the measurement of collocation strength, we solve the problem of manual selection or collocation proofreading. 2. Based on RFR, we suggest a collocation-based WSD model: RFR-SUM which sums up the RFR value of the words in context of each sense, and makes decision base on the RFR summation. We compare the model with Na¨ıve Bayesian Model and Maximum Entropy Model. The experimental results verify its feasibility. 3. We try to prune RFR lists. The results reveal that we can get almost the same precision by using only 5% important collocation information. At the same time, the speed is 20 times higher. The rest of this paper is organized as follows. Section 2 describes the RFR and RFR-SUM Model. Section 3 reports the experiments and the analysis of our result. Section 4 is the conclusions and future work.

2 2.1

Relative Frequency Ratio (RFR) and RFR-SUM Model Collocation Strength and Relative Frequency Ratio (RFR)

There are actually different definitions of the notion of collocation. Some authors in the computational linguistics and statistical literature define a collocation as two or more consecutive words with a special behavior. But in most linguistically oriented research, a phrase can be a collocation even if it is not consecutive, as

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in the example ”knock…door”. Some authors have generalized the notion of collocation even further and included cases of words that are strongly occurred in a common grammatical unit and with a particular order, case like ”doctornurse” or ”plant-airport”[10]. In fact, collocation in this definition is more useful for WSD. If ”doctor” occurs with ”nurse”, it probably denotes doctor vocation. If with ”master” or ”bachelor”, it may have the meaning of Ph.D. degree. Many researchers realize the importance of collocation, but they think that some words have low discrimination power such as prepositions, conjunctions, articles and pronouns. They don’t directly contribute to the content. Therefore, these words are listed in the stop-list and deleted from set of context words before WSD[1]. But in our investigation, even function words can also contribute to WSD[11]. Taken ”的 (of)” as an example. If ”的” occurs consecutively behind ”黄色”, i.e. ”黄色的”, then ”黄色” would almost have the sense of yellow color. We count the distribution of ”的” in the sentences with word ”黄色” in 23 years’ People Daily News (PDN) corpus at two different senses. There are 2137 sentences for yellow color sense and 893 sentences for pornographic sense. We align the sentences by ”黄色” and count the distribution of ”黄色” at different position. For normalizing the difference of sentence amount between the two senses, the frequency of ”的” at different positions for pornographic sense multiplies 2137/893. The result is in Figure 1. The figure shows that when ”的” occurs consecutively behind ”黄色”, the sense will be yellow color at a very high probability.

Fig. 1. ”的”’s distribution in sentences with ”黄色” and aligned by ”黄色”

Based on the above-mentioned facts, we believe that every word in context makes contribution to WSD in one way or another. Here we define collocation

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as co-occurrence of words in context of target word. We introduce Relative Frequency Ratio (RFR) to evaluate the collocation strength. Let the context with ambiguous word A be: W−k W−(k−1)... W−2 W−1 AW1 W2... W(s−1) Ws

(1)

where, the negative sign in subscript denotes the word in left context; −k denotes that left context selects k words; s denotes that right context selects s words. The sentences with polysemous word are classified by linguists, thus form t sets, each of which contains one sense. Here, t expresses that the word has t different senses: A1 , A2 , . . . , At . For every set m(m = 1, 2, . . . , t), we first align every sentence according to word Am , then calculate the frequency of every word in position i(−k ≤ i ≤ s) to word Am , which is called local word frequency for this word at position i to Am , denoted as LocFrqm,i (word) In a very large corpus (named corpus bank), we count the frequency of every word occurred in the corpus, thus get word frequency for each word, called global word frequency, denoted as GlobFrq(word). Denote the RFR of this word at position i as fm,i (word) and define fm,i (word) as: fm,i (word) = LocF rqm,i (word)/GlobF rq(word)

(2)

Hence we get RFR matrix as below: Fm,−k Fm,−(k−1)... Fm,−2 Fm,−1 Am Fm,1 Fm,2... Fm,(s−1) Fm,s

(3)

Fm,i = (fm,i (word1 ), fm,i (word2 ), . . . , fm,i (wordn ))T

(4)

where, n denotes that there are n occurrence words at position i for sense Am . RFR reflects the extent of attraction between the word and word Am at position i. It can be used to evaluate collocation strength. For simplicity, we take the left k words as left window and the right s words as right window. Then the RFR matrix is simplified as: Fm,lef t Am Fm,right

(5)

where, Fm,i = (fm,i (word1 ), fm,i (word2 ), . . . , fm,i (wordn ))T , (i = lef t, right) and fm,lef t (wordj ) =

−k X

(6)

fm,i (wordj ), f or lef t − window

(7)

fm,i (wordj ), f or right − window

(8)

i=−1

fm,right (wordj ) =

s X i=1

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By means of above mentioned method, we get two RFR lists for each sense of polysemous word in left and right window. This is in fact the training process of WSD. For word ”黄色”, Table 1 and Table 2 show the top 10 words with high RFR value for both senses in left window and right window respectively after we prune 95% lower frequency words from RFR lists (here,k = s = 2). We can see that the words with high RFR value are almost the collocations for their corresponding sense. In fact, RFR value can be used for collocation extraction[12]. Table 1. Top 10 words with high RFR value for yellow sense Left window word RFR 橘(orange) 0.10714 褐(brown) 0.06061 淡(light) 0.05051 乳(milky) 0.01687 鹅(soft) 0.01553 呈(show) 0.01363 浅(light) 0.01268 身穿(wear) 0.00824 金(gold) 0.00823 片片(piece) 0.00788

Frq 24 32 267 38 49 116 121 21 892 18

right window word 葡萄球菌(Staphylococcus) 袈裟(cassock) 油菜花(rape flower) 麦浪 (wheat field) 棕色 (brown) 琉璃瓦 (glazed tile) 五角星 (pentacle star) 斑点(spot) 军服(uniform) 粉末(powder)

RFR 0.48000 0.08209 0.05641 0.04734 0.04000 0.03583 0.03217 0.03040 0.01852 0.01678

Frq 36 22 11 24 18 11 12 10 19 18

Table 2. Top 10 words with high RFR value for pornographic sense Left window word RFR 查禁(ban) 0.01494 收缴(capture) 0.00664 淫秽(bawdy) 0.00555 贩卖(vend) 0.00454 播放(play) 0.00415 书刊(books) 0.00326 取缔(ban) 0.00262 抵制(reject) 0.00108 传播(spread) 0.00100 出售(sale) 0.00049

2.2

Frq 13 17 12 24 13 18 13 17 17 12

right window word RFR 书刊(books) 0.02735 录像(video) 0.02503 录像带(videotape) 0.02230 淫秽(bawdy) 0.02220 出版物(publication) 0.00956 刊(periodical) 0.00706 歌曲(song) 0.00520 小说(novel) 0.00298 书籍(book) 0.00242 音乐(music) 0.00170

Frq 151 70 31 48 42 40 92 60 38 69

The RFR-SUM Model

After the training process, we can make use of the RFR matrix to disambiguate the word sense. Given a certain sentence as (1), for every sense Am (m = 1, 2, . . . , t), search for the RFR of Wi (−k ≤ i ≤ s) from column i of RFR matrix and get fm,i (Wi ).

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If the word Wi is not in column i, then fm,i (Wi ) = 0. Thus we get: SUMm =

s X

fm,i (Wi )

(9)

i=−k

In the simplified model, SUMm =

−k X

fm,lef t (Wi ) +

i=−1

s X

fm,right (Wi )

(10)

i=1

Among m(m = 1, 2, . . . , t), the sense that maximizes the SUMm will be assigned as the word sense. From the RFR-SUM model, we can see that the model takes the positional information in context and their RFR value into consideration, then sums up all the RFR values in context. This is similar to what human integrates all his knowledge he has ever learnt to make the decision. Table 3 gives some examples for the sum of RFR values in sentences. The three sentences are very similar, but the RFR-SUM Model can discriminate them easily. Especially in the last sentence, where the context words but ”的” are not occurred in training data, the functional words ”的” can give a strong hint to make the true decision at a very high probability. This property is very useful in open test. Table 3. Sentences and their RFR sum value 充斥|fill 0.00070 0.00000 身穿|wear 0.00000 0.00824 眺望|watch 0.00000 0.00000

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黄色 bawdy yellow 黄色 bawdy yellow 黄色 bawdy yellow

的|of 内容|content sum 0.00001 0.00012 0.00083 0.00006 0.00000 0.00006 的|of 军服|costume 0.00001 0.00000 0.00001 0.00006 0.01852 0.02682 的|of 山峦|mountain 0.00001 0.00000 0.00001 0.00006 0.00000 0.00006

Experimental Results

We select frequently used polysemous Chinese words ” 高度”, ”出发”, ”保守”, ”黄色”, ”合计”, ”地方”, ”黄金”, which have two senses, and ”分子”, ”材料”, which have three senses, as WSD examples. We employ 54 years’ PDN corpus from 1946 to 1999 as corpus bank to count global word frequency, thus the global word frequency is more stable. The training data is selected from PDN corpus range from year 1991 to 1996. The testing data is from 1997 PDN corpus.

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WSD Algorithm

We use the RFR-SUM Model to disambiguate polysemous words. Training process is described below: Step.1 construct global word frequency list using 54 years’ PDN corpus. Step.2 select PDN corpus from year 1991 to 1996, processed by word segmentation and POS tagging without human proofreading. Here, we use ICTCLAS, available at http://www.nlp.org.cn. Step.3 pick up sentences with polysemous word to get training data. Linguist classifies the sentences according to different word senses to form high frequency sense data, which occurs more in corpus, and low frequency sense data, which occurs less in corpus. Step.4 for the high frequency sense data and low frequency sense data, calculate their word frequency in left window and right window respectively, thus formed 4 lists of local word frequency from different sense and different window. Step.5 for each word w in the above-mentioned lists, calculate the RFR to form RFR lists. Thus four lists are formed for every sense at left and right window. For word Wi , the RFR value are denoted as: fHF,lef t (Wi ), fHF,right (Wi ), fLF,lef t (Wi ) and fLF,right (Wi ) respectively. The WSD process is very simple after the four lists are formed. Sum up the RFR value of all the words in the context for each word sense separately and make simple decision. The process is described as follows: begin sumHF=0, sumLF=0; for (i = −1; i ≥ L; i − −) //L is left window size { sumHF+ = fHF,lef t (W i); sumLF+ = fLF,lef t (W i); } for(i = 1; i ≤ R; i + +)//R is right window size { sumHF+ = fHF,right (W i); sumLF+ = fLF,right (W i); } if sumHF ∗ α ≥ sumLF then sense=HF; else sense=LF. end In order to eliminate the effect caused by the amount difference of examples for high frequency sense and low frequency sense, here we introduce a coefficient α to balance the difference, 0 < α ≤ 1. The value of α can be determined by maximizing the accuracy of close test data. Precision is introduced to evaluate the WSD performance, which is defined as:

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precision =

Number of words assigned a sense correctly Number of polysemous words in data set

(11)

We set the baseline as the precision when we assign all the word senses with the high frequency sense. Table 4 lists the example words, their senses, the numbers of test data, and baselines. Table 4. Experimental words, their example numbers, and baselines

word sense 高度 altitude/ high degree 出发 depart/focus of attention 保守 keep/ conservative 黄色 pornographic/ yellow 合计 Think of/ add up to 地方 local/ place 黄金 Precious/ gold 分子 Numerator/molecule /person belong to a class 材料 Stuff/facts,information /substance

close test Num. of examples baseline% 100/430 81.1 80/555 87.4 102/1158 91.9 893/2136 70.5 83/187 69.3 387/591 60.4 648/2822 81.3 7/454/502 52.1

open test Num. of examples Baseline% 63/207 76.7 30/183 85.9 20/76 79.2 50/143 74.1 13/32 71.1 83/115 58.1 96/343 78.1 5/14/192 91.0

44/1975/ 2526

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In order to show the RFR-SUM performance, we compare the accuracy with Na¨ıve Bayesian Model and Maximum Entropy Model, which are very common in WSD. More details about these two models are described in [13]. We select the same features with RFR-SUM model. The results are listed in Table 5. It shows that the precision by RFR-SUM Model is 5.95% and 4.48% higher than that of Na¨ıve Bayesian Model and Maximum Entropy Model respectively.

Table 5. Experimental Results

高度 出发 保守 黄色 合计 地方 黄金 分子 材料 avg

Na¨ıve close% 100 100 100 99.69 100 99.29 99.60 100 99.40 99.78

Bayes open% 88.5 85.5 93.7 90.15 86.67 75.25 89.40 91.00 78.74 86.55

Maximum Entropy close% open% 100 90.15 100 89.00 100 95.80 99.94 95.33 100 91.11 99.90 75.25 100 91.24 100 80.09 98.73 84.25 99.84 88.02

RFR-SUM close% open% 99.81 93.00 99.84 90.00 99.92 96.87 99.18 96.37 100 91.11 98.88 89.39 98.07 95.89 98.52 90.50 99.56 89.37 98.73 92.50

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The Pruning of RFR List

Zipf’s law tells us that most of the words in corpus are sparsely occurred and only a small amount of words happen at high frequency. In RFR lists, there are more than 50% words occurring only once. These words contribute less to WSD especially when in open test. We try to optimize the RFR lists, and prune words with less frequency. We remove words with less frequency from 10% to 90% step by 10%, the precision is unchangeable. Figure 2 shows that of from 91% to 100% step by 1%. The figures reveal that we can get almost the same precision by using only 5% important collocation information. At the same time, the speed is 20 times higher. The algorithm’s running time is O(n), where n is the number of words in RFR lists.

Fig. 2. Relation between reduction ratio and accuracy (91%-100% step 1%)

The result is very important. It guarantees that the speed is 20 times higher with less precision loss, thus makes the RFR-SUM model more valuable in practice.

4

Conclusions and Future Work

This paper presents a collocation-based WSD model: RFR-SUM, which introduces RFR to measure the collocation strength and sums up the RFR values of contextual words to make decision. The experimental results are inspiring. We also prune RFR lists. Experiment shows that we can get almost the same precision by using only 5% important collocation information. At the same item, the speed is 20 times higher. In this paper, we only use the model in WSD task. We believe that the model can also tackle other lexical level ambiguity such as covering ambiguity resolution and overlapping ambiguity resolution in Chinese word segmentation,

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named entity recognition, and so on. We also believe that the model is language independent. We will try to use the model in English WSD soon. Acknowledgments. This paper is supported by 973 Natural Basic Research Program of China (Grant No. 2004CB318102), the National Natural Science Foundation of China (Grant No. 60503071, 60675035), China Postdoctoral Science Foundation(Grant No.20060400027), and Jiangsu Province Social Science Foundation (Grant No.06JSBYY001). My thanks go to the anonymous reviewers for their suggestive comments.

References 1. Zhang, Y., Gong, L., Wang, Y.: Chinese Word Sense Disambiguation Using HowNet. In: Wang, L., Chen, K., Ong, Y.S. (eds.) ICNC 2005. LNCS, vol. 3610, pp. 925–932. Springer, Heidelberg (2005) 2. Ide, N., Veronis, J.: Introduction to the Special Issue on Word Sense Disambiguation: The State of the Art. Computational Linguistics 24(1), 1–40 (1998) 3. Ng, H.T., Wang, B., Chan, Y.S.: Exploiting Parallel Texts for Word Sense Disambiguation: An Empirical Study. In: Dignum, F.P.M. (ed.) ACL 2003. LNCS (LNAI), vol. 2922, pp. 455–462. Springer, Heidelberg (2004) 4. Li, H., Li, C.: Word Translation Disambiguation Using Bilingual Bootstrapping. Computational Linguistics 30(1), 1–22 (2004) 5. Changqin, Q., Tingting, H., et al.: Chinese WSD based on Selecting the Best Seeds from Collocations. Journal of Chinese Information Processing (in Chinese) 19(1), 30–35 (2005) 6. Dang, H.T., Chia, C.-y., et al.: Simple Features for Chinese Word Sense Disambiguation. In: Proc. Of COLING-2002, Philadelphia, USA, pp. 769–772 (2002) 7. Li, W., Lu, Q., Li, W.: Integrating Collocation Features in Chinese Word Sense Disambiguation. In: Proceedings of the Fourth Sighan Workshop on Chinese Language Processing, pp. 87–94, Jeju, Korea (2005) 8. Smadja, F.: Retrieving Collocations from Text: Xtract. Computational Linguistics 19(1), 143–177 (1993) 9. Lin, D.: Extracting Collocations from Text Corpora. In: Proceedings of COLLING/ACL-98 Workshop on Computational Terminology, Montreal, Canada. pp. 57–63 (1998) 10. Manning, C.D., Schutze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999) 11. Qu, W.: Generalized Collocation and Context-based Computational Model, Ph. D Dissertation, Nanjing Normal University (2005) 12. Qu, W.: A Frame-based Approach to Chinese Collocation Automatic Extracting. Computer Engineering (in Chinese) 30(23), 22–24, 195 (2004) 13. Wang, Z., Wang, H., Duan, H., Han, S., Yu, S.: Chinese Noun Phrase Metaphor Recognition with Maximum Entropy Approach. In: Proceedings of the Seventh International Conference on Intelligent Text Processing and Computational Linguistics. pp. 235–244, Mexico (2006)

A Simple Probability Based Term Weighting Scheme for Automated Text Classification Ying Liu1 and Han Tong Loh2 1 Department of Industrial and Systems Engineering The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China [email protected] 2 Department of Mechanical Engineering National University of Singapore, 21 Lower Kent Ridge Road, Singapore 119077 [email protected]

Abstract. In the automated text classification, tfidf is often considered as the default term weighting scheme and has been widely reported in literature. However, tfidf does not directly reflect terms’ category membership. Inspired by the analysis of various feature selection methods, we propose a simple probability based term weighting scheme which directly utilizes two critical information ratios, i.e. relevance indicators. These relevance indicators are nicely supported by probability estimates which embody the category membership. Our experimental study based on two data sets, including Reuters-21578, demonstrates that the proposed probability based term weighting scheme outperforms tfidf significantly using Bayesian classifier and Support Vector Machines (SVM).

1 Introduction Text classification (TC) is such a task to categorize documents into predefined thematic categories. In particular, it aims to find the mapping ξ, from a set of documents D: {d1, …, di} to a set of thematic categories C: {C1, …, Cj}, i.e. ξ : D Æ C. In its current practice, which is dominated by supervised learning, the construction of a text classifier is often conducted in two main phases [2, 14]: 1. Document indexing – the creation of numeric representations of documents • Term selection – to select a subset of terms from all terms occurring in the collection to represent the documents in a better way, either to facilitate computing or to achieve best effectiveness in classification. • Term weighting – to assign a numeric value to each term in order to weight its contribution which helps a document stand out from others. 2. Classifier induction – the building of a classifier by learning from the numeric representations of documents For term weighting, tfidf is often considered as the default scheme and hence has been widely reported in literature. However, tfidf does not really reflect terms’ category membership which is what classification supposes to rely on. In this paper, we propose a novel term weighting scheme based on our inspiration from the existing feature selection methods. The classic tfidf scheme is briefly reviewed in Section 2. H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 33–43, 2007. © Springer-Verlag Berlin Heidelberg 2007

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Different feature selection methods are analyzed in Section 3. This is where we note four fundamental information elements. Our term weighting scheme is described in Section 4. Experimental results are reported in Section 5. Section 6 concludes.

2 Term Weighting Scheme In information retrieval and machine learning, term weighting has long been formulated in a form as term frequency times inverse documents frequency, i.e. tfidf [1, 12, 13, 16]. The more popular “ltc” form [1, 12, 13] is given by,

tfidf (ti , d j ) = tf (ti , d j ) × log(

N ) N (ti )

(1)

and its normalized version is wi , j =

tfidf (ti , d j )

∑

|T |

(2)

tfidf (tk , d j ) 2 k =1

where N and |T| denote the total number of documents and unique terms contained in the collection respectively, and N(ti) represents the number of documents in the collection in which term ti occurs at least once, and ⎧1 + log(n(ti , d j )), if n(ti , d j ) > 0 tf (ti , d j ) = ⎨ ⎩0, otherwise where n(ti,dj) is the number of times that the term ti occurs in document dj. In practice, the summation in equation (2) is only concerned about the terms occurred in document dj. The significance of the classic term weighting schemes in equitation (1) and (2) is that they have embodied three fundamental assumptions of term frequency distribution in a collection of documents [2, 14]. These assumptions are: • Rare terms are no less important than frequent terms – idf assumption • Multiple appearance of a term in a document are no less important than single appearance – tf assumption • For the same quantity of term matching, long documents are no more important than short documents – normalization assumption

Because of these, the “ltc” and its normalized form have been extensively studied by many researchers and show its good performance over a number of different data sets [14]. Therefore, they have become the default choice in TC.

3 Inspiration from Feature Selection Feature selection serves as a key procedure to reduce the dimensionality of input space in order to save computation cost. It has been integrated as a default step for many learning algorithms, like artificial neuron network, k-nearest neighbors, decision Tree,

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etc. In the research community of machine learning, the computation constraints imposed by the high dimensions of input data space and the richness of information available to maximally identify each individual object is a well known tradeoff. The ability of feature selection to capture the salient information by selecting the most important attributes, and thus making the computing tasks tractable has been shown in information retrieval and machine learning research [4, 9, 11, 20]. Furthermore, feature selection is also beneficial since it tends to reduce the over-fitting problem, in which the trained objects are tuned to fit very well the data upon which they have been built, but performs poorly when applied to unseen data [14]. In TC, several feature selection methods have been intensively studied to distill the important terms while still keeping the dimensions small. Table 1 shows the main functions of several popular feature selection methods. These methods are evolved either from the information theory or from the linear algebra literature [14, 20]. Table 1. Several feature selection methods, and their functions, where tk denotes a term; ci stands for a category; P(tk,ci) denotes the probability of documents from category ci where term tk occurs at least once; P (tk , ci ) denotes the probability of documents not from category ci where term tk occurs at least once; P ( tk , ci ) denotes the probability of documents from category ci where term tk does not occur; P ( tk , ci ) denotes the probability of documents not from category ci where term tk does not occur Feature Selection Method Information Gain

Mathematical Form P(tk , ci )log

P (tk , ci ) P( tk , ci ) + P( tk , ci )log P(tk ) ⋅ P(ci ) P ( tk ) ⋅ P(ci ) log

Mutual Information

P(tk , ci ) P(tk ) P (ci )

N ⋅ [ P(tk , ci ) ⋅ P( tk , ci ) − P (tk , ci ) ⋅ P ( tk , ci ) ]

2

Chi-square

Odds Ratio

P(tk ) ⋅ P ( tk ) ⋅ P(ci ) ⋅ P(ci ) log

P(tk ci ) ⋅ (1 − P (tk ci )) (1 − P(tk ci )) ⋅ P(tk ci )

Basically, there are two distinct ways to rank and assess the features, i.e. globally and locally. Global feature selection aims to select features which are good across all categories. Local feature selection aims to differentiate those terms that are more distinguishable for certain categories only. The sense of either ’global’ or ’local’ does not have much impact on the selection of method itself, but it does affect the performance of classifiers built upon different categories. In TC, the main purpose is to address whether this document belongs to a specific category. Obviously, we prefer the salient features which are unique from one category to another, i.e. a ’local’ approach. Ideally, the salient feature set from one category does not have any items overlapping with those from other categories. If this cannot be avoided, then how to better present them comes into the picture.

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While many previous works have shown the relative strengths and merits of these methods [4, 9, 11, 14, 20], our experience with feature selection over a number of standard or ad-hoc data sets shows the performance of such methods can be highly dependant on the data. This is partly due to the lack of understanding of different data sets in a quantitative way, and it needs further research. From our previous study of all feature selection methods and what has been reported in the literature [20], we noted when these methods are applied to text classification for term selection purpose, they are basically utilizing four fundamental information elements shown in Table 2, i.e. A denotes the number of documents belonging to category ci where the term tk occurs at least once; B denotes the number of documents not belonging to category ci where the term tk occurs at least once; C denotes the number of documents belonging to category ci where the term tk does not occur; D denotes the number of documents not belonging to category ci where the term tk does not occur. Table 2. Fundamental information elements used for feature selection in text classification ci

ci

tk

A

B

tk

C

D

These four information elements have been used to estimate the probability listed in Table 1. Table 3 shows the functions in Table 1 as presented by these four information elements A, B, C and D. Table 3. Feature selection methods and their formations as represented by information elements in Table 2 Method Information Gain Mutual Information

Mathematical Form Represented by Information Elements A+C A+C A A C C − log + log( ) + log( ) N N N A+ B N C+D log( AN ( A + B)( A + C ))

Chi-square

N ( AD − BC ) 2 ( A + C )( B + D)( A + B )(C + D)

Odds Ratio

log( AD BC )

4 A Probability Based Term Weighting Scheme 4.1 Revisit of tfidf

As stated before, while many researchers believe that the term weighting schemes in the form as tfidf representing those three aforementioned assumptions, we understand tfidf in a much simpler manner, i.e.

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1. Local weight - the tf term, either normalized or not, specifies the weight of tk within a specific document, which is basically estimated based on the frequency or relative frequency of tk within this document. 2. Global weight - the idf term, either normalized or not, defines the contribution of tk to a specific document in a global sense. If we temporarily ignore how tfidf is defined, and focus on the core problem, i.e. whether this document is from this category, we realize that a set of terms is needed to represent the documents effectively and a reference framework is required to make the comparison possible. As previous research shows that tf is very important [7, 12, 14] and using tf alone can already achieve good performance, we retain the tf term. Now, let us consider idf, i.e. the global weighting of tk. The conjecture is that if the term selection can effectively differentiate a set of terms Tk out from all terms T to represent category ci, then it is desirable to transform that difference into some sort of numeric values for further processing. Our approach is to replace the idf term with the value that reflects the term’s strength of representing a specific category. Since this procedure is performed jointly with the category membership, this basically implies that the weights of Tk are category specific. Therefore, the only problem left is how to compute such values. 4.2 Probability Based Term Weighting

We decide to compute those term values using the most direct information, e.g. A, B and C, and combine them in a sensible way which is different from existing feature selection measures. From Table 2, two important ratios which directly indicate terms’ relevance with respect to a specific category are noted, i.e. A/B and A/C, • A/B: it is easy to understand that if term tk is highly relevant to category ci only, which basically says that tk is a good feature to represent category ci, then the value of A/B tends to be higher. • A/C: given two terms tk, tl and a category ci, the term with a higher value of A/C, will be the better feature to represent ci, since a larger portion of it occurs with category ci.

In the following of this paper, we name A/B and A/C relevance indicator since these two ratios immediately indicate the term’s strength in representing a category. In fact, these two indicators are nicely supported by probability estimates. For instance, A/B can be extended as (A/N)/(B/N), where N is the total number of documents, A/N is the probability estimate of documents from category ci where term tk occurs at least once and B/N is the probability estimate of documents not from category ci where term tk occurs at least once. In this manner, A/B can be interpreted as a relevance indicator of term tk with respect to category ci. Surely, the higher the ratio, the more important the term tk is related to category ci. A similar analysis can be made with respect to A/C. The ratio reflects the expectation that a term is deemed as more relevant if it occurs in the larger portion of documents from category ci than other terms. Since the computing of both A/B and A/C has its intrinsic connection with the probability estimates of category membership, we propose a new term weighting factor which utilizes the aforementioned two relevance indicators to replace idf in the classic

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tfidf weighting scheme. Considering the probability foundation of A/B and A/C, the most immediate choice is to take the product of these two ratios. Finally, the proposed weighting scheme is formulated as tf ⋅ log(1 +

AA ). BC

5 Experimental Study Two data sets were tested in our experiment, i.e. MCV1 and Reuters-21578. MCV1 is an archive of 1434 English language manufacturing related engineering papers which we gathered by the courtesy of the Society of Manufacturing Engineers (SME). It combines all engineering technical papers published by SME from year 1998 to year 2000. All documents were manually classified [8]. There are a total of 18 major categories in MCV1. Figure 1 gives the class distribution in MCV1. Reuters-21578 is a widely used benchmarking collection [14]. We followed Sun’s approach [15] in generating the category information. Figure 2 gives the class distribution of the Reuters dataset used in our experiment. Unlike Sun [15], we did not randomly sample negative examples from categories not belonging to any of the categories in our data set, instead we treated examples not from the target category in our dataset as negatives.

Fig. 1. Class distribution in MCV1

We compared our probability based term weighting scheme with the classic tfidf on MCV1 and Reuters-21578 using Bayesian classifier, i.e. Complement Naïve Bayes (CompNB) [10], and Support Vector Machine (SVM) [17] as the classification algorithms. The CompNB has been recently reported that it can significantly improve the performance of Naïve Bayes over a number of well known datasets, including Reuters-21578 and 20 Newsgroups. Various correction steps are adopted in CompNB, e.g. data transformation, better handling of word occurrence dependencies and so on. In our experiments, we borrowed the package implemented in Weka 3.5.3 Developer version [18]. For SVM, we chose the well known implementation SVMLight [5, 6].

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Linear function has been adopted as its kernel function, since previous work has shown that the linear function can deliver even better performance without tedious parameter tuning in TC [3, 5]. As for the performance measurement, precision, recall and their harmonic combination, i.e. the F1 value, were calculated [1, 16]. Performance was assessed based on five-fold cross validation. Since we are very concerned about the performance of every category, we report the overall performance in macroaveraged manner, i.e. macro-average F1, to avoid the bias for minor categories in imbalanced data associated with micro-averaged scores [14, 19].

Fig. 2. Class distribution in Reuters-21578

Major standard text preprocessing steps were applied in our experiments, including tokenization, stop word and punctuation removal, and stemming. However, feature selection was skipped for SVM experiments and all terms left after stop word and punctuation removal and stemming were kept as features. Figure 3 and 4 show the overall performance of tfdif and the probability based term weights on different categories in MCV1 and Reuters-21578 respectively. They are reported in terms of macro-averaged F1 values. Our first observation is that the proposed scheme of probability based term weights is able to outperform tfidf over both data sets using SVM and Bayesian classifier. The results of tfidf based on Reuters21578 is in line with the literature [15]. Table 4 presents the macro-averaged F1 values of both term weighting schemes tested over two data sets. We note that using our proposed weighting scheme can improve the overall performance from 6% to more than 12%. Surprisingly, we also observe that when the probability based term weights are adopted, CompNB has delivered the result which is very close to the best one that SVM can achieve using classic tfidf scheme in Reuters-21578. This has demonstrated the great potential of using CompNB as a state-of-the-art classifier. As shown in Figure 1 and Figure 2, both MCV1 and Reuters-21578 are actually skewed data sets. In MCV1, there are six categories that own only around 1% of text population each and 11 categories falling below the average. The same case also happens to the Reuters-21578 data set. While it has 13 categories, grain and crude, the

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Fig. 3. F1 scores of both tfidf and the probability based term weights using CompNB and SVM tested over MCV1

Fig. 4. F1 scores of both tfidf and the probability based term weights using CompNB and SVM tested over Reuters-21578

two major categories, share around half of the text population. There are eight categories in total falling below the average. Previous literature did not report successful stories over these minor categories [15, 19]. Since our study shows that the probability based term weighting scheme works better than tfidf approach, we examine why this is the case. A close analysis shows that

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the probability based scheme displays much better results over minor categories in both MCV1 and Reuters-21578, regardless of classifiers used. For all minor categories shown in both figures, we observed a sharp increase of performance occurs when the system’s weighting method switches from tfidf to the probability one. Table 5 reveals more insights with respect to the system performance. In general, we observe that using the probability based term weighting scheme can greatly enhance the systems’ recalls. Although it falls slightly below tfidf in terms of precision using SVM, it still improves the systems’ precisions in CompNB, far superior to those tfidf can deliver. For SVM, while the averaged precision of tfidf in MCV1 is 0.8355 which is about 5% higher than the probability’s, the averaged recall of tfidf is 0.6006 only, far less than the probability based’s 0.7443. The case with Reuters-21578 is even more impressive. While the averaged precision of tfidf is 0.8982 which is only 1.8% higher than another, the averaged recall of probability based scheme reaches 0.9080, compared to tfidf’s 0.7935. Overall, the probability based weighting scheme surpasses tfidf in terms of F1 values over both data sets. Table 4. Macro-averaged F1 values of tfidf and probability based term weights on MCV1 and Reuters-21578

Classifier SVM CompNB

tfidf 0.6729 0.4517

MCV1 Prob.Based 0.7553 0.5653

tfidf 0.8381 0.6940

21578 Prob.Based 0.8918 0.8120

Table 5. Macro-averaged precision and recall of tfidf and probability based term weights on MCV1 and Reuters-21578

Data MCV1 21578

Classifier SVM CompNB SVM CompNB

Precision tfidf Prob.Based 0.8355 0.7857 0.4342 0.6765 0.8982 0.8803 0.5671 0.7418

tfidf 0.6006 0.4788 0.7935 0.9678

Recall Prob.Based 0.7443 0.5739 0.9080 0.9128

6 Conclusion In this paper, we have introduced a novel term weighting scheme which is generally formulated as tf times terms’ relevance with respect to different categories. This scheme directly makes use of two critical information ratios as a new way to compute the terms’ strengths in representing a category. These two ratios are nicely supported by the probability estimates which are deemed to embody the most salient information regarding terms’ category membership. The experimental study using CompNB and SVM over two data sets, i.e. MCV1 and Reuters-21578,

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demonstrates the merits of this new weighting scheme. Its combination with CompNB in delivering the state-of-the-art performance and the remarkable performance improvement over minor categories are noted. Its joint application with other algorithms in TC needs further exploration.

References 1. Baeza-Yates, R., Ribeiro-Neto, B.: Modern information retrieval. Addison-Wesley Longman Publishing Co, Boston (1999) 2. Debole, F., Sebastiani, F.: Supervised term weighting for automated text categorization. In: Proceedings of the 2003 ACM symposium on Applied computing (2003) 3. Dumais, S., Chen, H.: Hierarchical classification of Web content. In: Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR2000) (2000) 4. Forman, G.: An extensive empirical study of feature selection metrics for text classification. The Journal of Machine Learning Research, Special Issue on Variable and Feature Selection 3, 1289–1305 (2003) 5. Joachims, T.: Text categorization with Support Vector Machines: Learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) Machine Learning: ECML-98. LNCS, vol. 1398, Springer, Heidelberg (1998) 6. Joachims, T.: A Statistical Learning Model of Text Classification with Support Vector Machines. In: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval (2001) 7. Leopold, E., Kindermann, J.: Text Categorization with Support Vector Machines - How to Represent Texts in Input Space. Machine Learning 46, 423–444 (2002) 8. Liu, Y., Loh, H.T., Tor, S.B.: Building a Document Corpus for Manufacturing Knowledge Retrieval. In: Proceedings of the Singapore MIT Alliance Symposium (2004) 9. Ng, H.T., Goh, W.B., Low, K.L.: Feature selection, perception learning, and a usability case study for text categorization. ACM SIGIR Forum. In: Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval (1997) 10. Rennie, J.D.M., Shih, L., Teevan, J., Karger, D.R.: Tackling the Poor Assumptions of Naive Bayes Text Classifiers. In: Proceedings of the Twentieth International Conference on Machine Learning (2003) 11. Ruiz, M.E., Srinivasan, P.: Hierarchical Text Categorization Using Neural Networks. Information Retrieval 5, 87–118 (2002) 12. Salton, G., Buckley, C.: Term Weighting Approaches in Automatic Text Retrieval. Information Processing and Management 24, 513–523 (1988) 13. Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983) 14. Sebastiani, F.: Machine Learning in Automated Text Categorization. ACM Computing Surveys (CSUR) 34, 1–47 (2002) 15. Sun, A., Lim, E.-P., Ng, W.-K., Srivastava, J.: Blocking Reduction Strategies in Hierarchical Text Classification. IEEE Transactions on Knowledge and Data Engineering (TKDE) 16, 1305–1308 (2004) 16. van_Rijsbergen, C.J.: Information Retrieval. 2nd edn. Butterworths, London, UK (1979) 17. Vapnik, V.N.: The Nature of Statistical Learning Theory, 2nd edn. Springer, New York (1999)

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18. Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005) 19. Yang, Y., Liu, X.: A re-examination of text categorization methods. In: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval (1999) 20. Yang, Y., Pedersen, J.O.: A Comparative Study on Feature Selection in Text Categorization. In: Proceedings of ICML-97, 14th International Conference on Machine Learning (1997)

Text Classification for Healthcare Information Support Rey-Long Liu Department of Medical Informatics Tzu Chi University Hualien, Taiwan, R.O.C. [email protected]

Abstract. Healthcare information support (HIS) is essential in managing, gathering, and disseminating information for healthcare decision support through the Internet. To support HIS, text classification (TC) is a key kernel. Upon receiving a text of healthcare need (e.g. symptom description from patients) or healthcare information (e.g. information from medical literature and news), a text classifier may determine its corresponding categories (e.g. diseases), and hence subsequent HIS tasks (e.g. online healthcare consultancy and information recommendation) may be conducted. The key challenge lies on high-quality TC, which aims to classify most texts into suitable categories (i.e. recall is very high), while at the same time, avoid misclassifications of most texts (precision is very high). High-quality TC is particularly essential, since healthcare is a domain where an error may incur higher cost and/or serious problems. Unfortunately, high-quality TC was seldom achieved in previous studies. In the paper, we present a case study in which a high-quality classifier is built to support HIS in Chinese disease-related information, including the cause, symptom, curing, side-effect, and prevention of cancer. The results show that, without relying on domain knowledge and complicated processing, cancer information may be classified into suitable categories, with a controlled amount of confirmations.

1 Introduction The Word-Wide Web (WWW) has been a main source of healthcare information for both health consumers and professionals. Healthcare information support (HIS) is thus essential. It aims to manage, gather, and disseminate information for healthcare decision support through the Internet. The challenging requirements of HIS lie on (1) medical information on the Internet may not be scientifically-based [10], while patients require validated healthcare information [5], (2) healthcare information should be rich in several essential aspects (e.g. cause, symptom, curing, side-effect, and prevention of a disease, e.g. [2]), (3) healthcare professionals only have a limited amount of time and effort to gather information and/or make validation, and (4) even those users that receive higher education are often unable to construct proper queries (i.e. keywords linked with conjunction and disjunction operators [3]). 1.1 Problem Definition and Motivation In this paper, we present a case study in which HIS is approached by high-quality text classification (TC), which aims to classify most texts into suitable categories (i.e. H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 44–53, 2007. © Springer-Verlag Berlin Heidelberg 2007

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recall is very high), and avoid misclassifications of most texts (precision is very high). More specifically, we explore the extent to which a high-quality classifier may help to manage, gather and disseminate Chinese disease-related information, including the cause, symptom, curing, side-effect, and prevention of cancers. Consultancy

General Users (e.g. patients)

Classified Inquiry

Inquiry Query Relevant Information

High-Quality TC

Healthcare Professionals

Classification Confirmation

Classified Query

Classified Information Base

Classified Information

Information Gathered

Information Gathering Systems

Fig. 1. High-quality TC for healthcare information support

High-quality TC is particularly essential since healthcare is a domain in which any processing error may incur higher cost and/or serious problems. Figure 1 illustrates the contributions of a high-quality classifier to HIS: (1) patients may employ natural language to express their interests (e.g. symptoms of diseases), which are classified to retrieve relevant information or send consultancy inquiries more precisely and completely, and (2) healthcare professionals may be consulted only when necessary to validate the classifier’s decision (e.g. classifying the information gathered using various techniques, e.g. [10]) or respond to a patient’s inquiries (e.g. online health consultancy services provided by many web sites). High-quality TC is thus essential for both patients and healthcare professionals in health promotion. 1.2 Main Challenges Main challenges of the study lie on high-quality TC. Previous TC techniques often delegate a classifier to each category. The classifier is associated with a threshold, and upon receiving a document, it autonomously makes a yes-no decision for the corresponding category. Conceptually, a document is “accepted” by the classifier if its degree of acceptance (DOA) with respect to the category (e.g. similarity with the category or probability of belonging to the category) is higher than or equal to the corresponding threshold; otherwise it is “rejected.” With the help of the thresholds, each document may be classified into zero, one, or several categories. Unfortunately, in practice, most classifiers may not be perfectly built and tuned [1] [6] [14], due to several common problems: (1) imperfect selection of training documents (e.g. noises, over-fitting and content ambiguities) and (2) imperfect system setting (e.g. parameter setting and feature selection). These problems are often inevitable and may incur improper DOA estimations. A document that belongs to (does not belong to) a category

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could not always get a higher (lower) DOA value with respect to the category. Improper DOA estimations may heavily deteriorate the performance of TC. Therefore, it is difficult to have a classifier that may achieve very high performances in both precision and recall. Low precision incurs the problem of false information recommendation, while low recall incurs the problem of incomplete information recommendation. Both problems are essential for HIS, especially when the information is critical for health promotion. 1.3 Contributions and Organization of the Paper Section 2 explores the feasibility of employing interactive confirmations to approach high-quality TC. The main idea is to consult healthcare professionals to make confirmations to some of the TC decisions made by the classifier. Its challenge lies on the tradeoff between TC performance and cognitive load of the professionals in making confirmations: higher TC performance often incurs heavier cognitive load. Accordingly, in section 3 we present a confirmation strategy to achieve high-quality TC. To empirically evaluate the strategy, section 4 reports a case study in which real-world Chinese healthcare information about several categories of cancer are tested. The result shows that the strategy significantly performs better than baseline techniques. Moreover, without relying on any domain knowledge and complicated text processing, those diseases corresponding to Chinese descriptions of symptoms may be identified, making subsequent HIS tasks more targeted.

2 Interactive Confirmations for High-Quality TC When classifiers have been built and tuned to their best extent, system-user interaction should be a final approach to confirm the classifier’s decision to achieve highquality TC. Therefore, the goal of employing interactive confirmation for high-quality TC differs from many previous attempts, which often aimed at improving the classifier building process (e.g. iterative classifier refinement [11]), the threshold tuning process (e.g. [6]), and the document selection process (e.g. boosting [8], adaptive resampling [4], and query zoning [9]). The challenge of the confirmation lies on the tradeoff between the performance of TC and the cognitive load incurred to health professionals. To achieve higher-quality TC, the system often needs to consult the professionals more often, and hence increase the cognitive load in reading and validation. Conversely, to reduce the cognitive load of the professionals, the system should make decisions on its own, and hence deteriorate the performance of TC. To simultaneously achieve high-quality TC and control the cognitive load, the system should be intelligent enough to consult the professionals only when necessary. The intelligent behavior is a key challenge that did not get much attention in previous TC studies. 2.1 Evaluation Criteria Since the ideal case is to achieve high-quality TC with a limited amount of interactive confirmations, there should be two criteria: Confirmation Precision (CP) and Confirmation Recall (CR). CP is measured by [number of necessary confirmations conducted) / [number of confirmations conducted]. Since a confirmation for a decision is

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47

necessary if and only if the decision is wrong, CP may also be defined by [number of wrong decisions identified] / [number of decisions identified as potentially wrong]. On the other hand, CR is measured by [number of necessary confirmations conducted] / [number of confirmations that should be conducted]. Similarly, it may also be defined by [number of wrong decisions identified] / [number of wrong decisions that should be identified]. It is interesting to note that, CR is related to the quality of TC, while CP is related to the cognitive load incurred to the professional. To help the classifier to achieve perfect performance (both precision and recall of TC are 100%), CR should be 100%, indicating that all wrong decisions are identified for the professional to confirm (and hence be corrected by the professional). On the other hand, an extremely low CP may incur a very heavy cognitive load to the professional (e.g. when all decisions call for confirmations, CP will approach 0 and hence the classifier becomes nearly useless). Therefore, interactive high-quality TC aims to achieve nearly 100% in CR, under the requirement that CP should be as high as possible. 2.2 Straightforward Confirmation Strategies Based on the criteria, there are two straightforward confirmation strategies to pursue interactive high-quality TC: Uniform Confirmation (UC) and Probabilistic Confirmation (PC). For each category c, both strategies are based on threshold tuning documents (i.e. validation documents [12]), which are either positive (belonging to c) or negative (not belong to c). UC sets a confirmation range. Once a document’s DOA value falls in the range, a confirmation is conducted. Since the goal is to achieve high CR, UC sets a range that is large enough to cover the DOA values of those documents for which the classifier might make mistakes. The lower limit of the range may be set to the maximum DOA value below which no DOA values of positive documents lie. On the other hand, the upper limit may be set to the minimum DOA value beyond which no DOA values of negative documents lie. Obviously, UC may incur lower CP, and hence incur heavier cognitive load. On the other hand, PC works for those classifiers that tune a threshold to optimize performance in some criterion, such as the popular F1 measure, which integrates precision and recall by [2×precision×recall / (precision+recall)]. PC is based on the observation that those documents whose DOA values are closer to the threshold tend to have a higher probability of leading the classifier to make erroneous decisions. Suppose DOA values fall in the range of [Min, Max]. The probability of conducting a confirmation for a document with respect to a category is: Prob(confirmation) = (D – Max) / (T – Max), if D ≥ T; = (D – Min) / (T – Min), otherwise, where D is the DOA value of the document with respect to the category, and T is the threshold tuned for the category. Obviously, PC hopes to promote CP, but may incur lower CR, and hence has difficulties in guaranteeing the classifier’s performance. Therefore, both UC and PC are not good enough to achieve interactive high-quality TC, which aims to achieve very high CR, under the requirement that CP should be as high as possible. To develop a more effective confirmation strategy, the main technical

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issue lies on, among the decisions made by the classifier, intelligently identifying those decisions that deserve confirmations.

3 Intelligent Confirmation by Content Overlap Measurement We present an intelligent confirmation technique for interactive high-quality TC. The technique is named ICCOM (Intelligent Confirmation by Content Overlap Measurement). The basic idea is to associate each category’s classifier with ICCOM. Once a document is entered, the classifier is invoked to make its decision (either acceptance or rejection). Based on the decision, ICCOM is invoked to determine whether a confirmation is required. Therefore, the integrated system makes three kinds of decisions: acceptance, rejection, and confirmation. The expert is consulted only when the decision is confirmation. Content overlap measurement (COM) is a key basis on which ICCOM makes decisions. It aims to measure the degree of content overlap (DCO) between a document and a category [7]. The basic idea is that if DCO between c and d is not high enough, d should not be classified into c, even though d mentions some content of c. COM is particularly helpful for previous TC techniques in which whether a feature may be selected mainly depends on content relatedness among the categories, without paying much attention to how the contents of a category c and a document d overlap with each other (i.e. DCO). Table 1 presents the algorithm for COM. Given a category c and a document d, the algorithm considers two kinds of terms: those terms that are positively correlated with c but do not appear in d (ref. Step 2), and those terms that are negatively correlated with c but appear in d (ref. Step 3). Both kinds of terms lead to the reduction of DCO (ref. Step 2.1 and 3.1). Therefore, a smaller DCO indicates that d talks more information not in c, and vice versa. In that case, it is less proper to classify d into c. Table 1. Content Overlap Measurement Procedure COM(c, d), where (1) c is a category, (2) d is a document for thresholding or testing Return: Degree of content overlap (DCO) between d and c Begin (1) DCO = 0; (2) For each term t that is positively correlated with c but does not appear in d, do (2.1) DCO = DCO - χ2(t,c); (3) For each term t that is negatively correlated with c but appears in d, do (3.1) DCO = DCO - (number of occurrences of t in d) × χ2(t,c); (4) Return DCO; End.

The correlation strengths is estimated by χ2 (chi-square). For a term t and a category c, χ2(t,c) = [N × (A×D - B×C)2] / [(A+B) × (A+C) × (B+D) × (C+D)], where N is the total number of documents, A is the number of documents that are in c and contain t, B

Text Classification for Healthcare Information Support

Rejection Threshold (RT)

The classifier’s threshold (T)

Min DOA x

49

o

xx

x

o

x

x

Max DOA o

x

oo

o

o

o

o

Rejection Invoking COM to compute DCO

Negative Confirmation Threshold (NCT) x

x

Rejection

o

Confirmation

ox

Positive Confirmation Threshold (PCT) o

Confirmation

x

o

oo

o

oo

Acceptance

Fig. 2. Threshold tuning to identify confirmation zones (o: positive validation document; x: negative validation document)

is the number of documents that are not in c but contain t, C is the number of documents that are in c but do not contain t, and D is the number of documents that are not in c and do not contain t. Therefore, χ2(t,c) indicates the strength of correlation between t and c. We say that c and t are positively correlated if A×D > B×C; otherwise they are negatively correlated. Note that, a term t may even appear in d but not in any training document. It needs to be considered when measuring DCO between d and c (ref. Step 3). However, its χ2 value is incomputable (since both A and B are zero). ICCOM tackles the problem by treating d as a training document, and hence incrementing N and B by 1. For each category, ICCOM collaborates with the classifier by tuning three thresholds: rejection threshold (RT), positive confirmation threshold (PCT) and negative confirmation threshold (NCT). As illustrated in Figure 2, the three thresholds work together to help the system to make decisions: rejection, acceptance, or confirmation. RT is used to identify those documents whose DOA values are too low, and hence may be rejected without any confirmation (i.e. rejection). PCT is used to check whether a document is accepted by both the classifier (i.e. DOA value ≥ the classifier’s threshold T) and COM (i.e. DCO value ≥ PCT). If so, the document is accepted without confirmation (i.e. acceptance); otherwise a confirmation is required (i.e. confirmation). Similarly, NCT is used to check whether a document is rejected by both the classifier (i.e. DOA value < the classifier’s threshold T) and COM (i.e. DCO value ≤ NCT). If so, the document is rejected without confirmation (i.e. rejection); otherwise a confirmation is required (i.e. confirmation).

4 A Case Study on Chinese HIS for Cancer We present a case study to illustrate the contributions of ICCOM. In the case study, the targeted users include patients and healthcare professionals that use Chinese as

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their native language. The disease domain is cancer. As noted in Section 1.1, with the support of the classifier, (1) patients may employ natural language to express their interests (e.g. symptoms), which are classified to retrieve relevant information or send consultancy inquiries, and (2) healthcare professionals are consulted only when necessary to validate the classifier’s decision or respond to patients’ inquiries. 4.1 Experimental Data Experimental data is collected from Yahoo! at Taiwan (http://tw.yahoo.com/). We focus on 16 types of cancers (e.g. liver cancer, lung cancer, …, etc.), which are topranked by the department of health in Taiwan. For each type of cancer, documents are +” (knowledge+) area. collected by sending its Chinese name as a query to the “ The documents are then selected and separated into 5 categories: cause, symptom, curing, side-effect, and prevention of the type of cancer. Therefore, there are totally 80 (=16×5) categories. They contain 2850 documents. We randomly select 1/10 of the documents as test documents, and the others as training documents. As suggested by previous studies (e.g. [12]), the set of training documents is split into two subsets: the classifier building subset and the threshold tuning (or validation) subset. The former was used to build the classifier, while the latter was used to tune a threshold for each category. Both sets have the same number of documents. A 2-fold cross validation is conducted so that each training document is used for both classifier building and threshold tuning only once.

知知

4.2 Evaluation Criteria To measure the systems’ performances, we employ two groups of criteria: (1) criteria to evaluate the effectiveness of confirmation, and (2) criteria to evaluate the quality of TC. For the former, we employ the criteria defined in Section 2: CP and CR. For the latter, we employ the popular criteria: precision (P), recall (R), and F1. P is equal to [total number of correct classifications / total number of classifications made], R is equal to [total number of correct classifications / total number of correct classifications that should be made], and F1 is equal to 2PR/(P+R). Note that when measuring F1, we assume that each confirmation request issued by the systems may get a correct answer (rejection or acceptance). With the assumption, CR is directly related to the system’s performances in F1 (and hence both P and R), since a higher CR means that more errors are identified and hence corrected (if necessary) by confirmations. 4.3 The Underlying Classifier and Confirmation Strategies We employ the Rocchio’s classifier (RO) as the underlying classifier. RO was commonly employed in text classification (e.g. [11]) and filtering (e.g. [8] [9]). Some studies even showed that its performances were more promising in several ways (e.g. [5] [6]). RO constructs a vector for each category, and the similarity between a document d and a category c is estimated using the cosine similarity between the vector of d and the vector of c. More specifically, the vector for a category c is constructed by considering both

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relevant documents and non-relevant documents of c: η1*∑Doc∈PDoc/|P| − η2*∑Doc∈NDoc/|N|, where P is the set of vectors for relevant documents (i.e. the documents in c), while N is the set of vectors for non-relevant documents (i.e. the documents not in c). We set η1=16 and η2=4, since the setting was shown to be promising in previous studies (e.g. [11]). RO requires a fixed (predefined) feature set, which is built using the documents for classifier building. After removing stop words, all Chinese characters may be candidate features. No domain knowledge and lexical processing are employed. The features are selected according to their weights estimated by the χ2 (chi-square) weighting technique. The technique was shown to be more promising than others [13]. To conduct more thorough investigation, we try various feature set sizes. To make TC decisions, RO also requires a thresholding strategy to set a threshold for each category. As in many previous studies (e.g. [8] [12] [14]), RO tunes a relative threshold for each category by analyzing document-category similarities. The threshold tuning documents are used to tune each relative threshold. As suggested by many studies (e.g. [12]), the thresholds are tuned in the hope to optimize the system’s performance with respect to F1. To measure the contribution of ICCOM with respect to other confirmation strategies, we implement the two baseline confirmation strategies presented in Section 2: UC and PC. Therefore, we have three versions: RO+ICCOM, RO+UC, and RO+PC, which enhance RO with ICCOM, UC, and PC, respectively. 4.4 Results Table 2 summarizes the experimental results in classifying all test documents into the 80 categories. We focus on the best version of RO under different sizes of feature set (FS). The results show that only UC and ICCOM may help RO to achieve very high F1 (> 0.94). However, CP of ICCOM is significantly better than UC (31.8% improvement in the 1st fold and 60.8% improvement in the 2nd fold), indicating that ICCOM enhances RO to achieve high-quality TC with fewer confirmations. Table 2. Classification of cancer information

1st fold 2nd fold

Best F1 by RO

F1 by RO+PC

CP of RO+PC

F1 by RO+UC

CP of RO+UC

F1 by RO+ICCOM

CP of RO+ICCOM

0.3485

0.8413

0.0969

0.9610

0.0848

0.9607

0.1117

0.7823

0.1037

0.9656

0.0725

0.9433

0.1166

(FS=1500)

0.3270 (FS=1500)

We are also interested in symptom categories for the 16 cancer types. Experiments on the categories may measure the performance of the system in classifying patients’ natural language symptom descriptions for cancer type identification. In testing, we remove cancer names in the test documents, since in practice patients often do not know cancer types for their symptoms. Table 3 summarizes the results. Although all the confirmation strategies may help RO to achieve very high F1, ICCOM achieves the best CP again. The improvement ranges from 40.5% to 170.5%. For the 40 test

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symptom documents, RO+ICCOM conducts 35 and 51 confirmations in the 1st and 2nd folds, respectively. Therefore, to achieve high-performance cancer type identification, only about 1 confirmation is required for a symptom description. The confirmation (i.e. possible cancer types) may provide the patients with additional reference to reduce possible identification errors. Table 3. Classification of symptom description without cancer names

1st fold 2nd fold

Best F1 by RO

F1 by RO+PC

CP of RO+PC

F1 by RO+UC

CP of RO+UC

F1 by RO+ICCOM

CP of RO+ICCOM

0.8919

0.9610

0.0676

0.9744

0.1017

0.9610

0.1429

0.9620

0.1000

0.9750

0.0580

0.9744

0.1569

(FS=300)

0.8718 (FS=300)

5 Conclusion and Future Work Text classification is a key kernel to provide healthcare information support through the Internet. It helps to manage, gather, and disseminate healthcare information for decision support. Since healthcare is a domain where a classification error may incur high cost and/or serious problems, the classifier should be “conservative” in the sense that, when it is possible to make an error, a confirmation request should be issued to healthcare professionals or patients. For healthcare professionals, the confirmation request suggests the professionals to make validation only when necessary. For patients, the confirmation request provides an additional reference for patients to consider. The key challenge lies on the identification of possible errors for confirmation. In this paper, we present an intelligent conformation strategy, and explore its contributions in a case study on Chinese cancer information. The results are of practical significance to healthcare information support through the Internet. Future research directions lie on the development of an intelligent interface, which employs systemuser interaction to elicit more detailed descriptions (e.g. symptoms) from users when users’ original descriptions are not precise enough for classification. Acknowledgments. This research was supported by the National Science Council of the Republic of China under the grants NSC 95-2221-E-320-002.

References 1. Arampatzis, A., Beney, J., Koster, C.H.A., van der Weide, T.P.: Incrementality, Half-life, and Threshold Optimization for Adaptive Document Filtering. In: Proceedings of the 9th Text Retrieval Conference (2000), pp. 589–600. Gaithersburg, Maryland (2000) 2. Fahey, D.K., Weinberg, J.: LASIK Complications and the Internet: Is the Public being Mislead? Journal of Medical Internet Research 5(1) (2003) 3. Ivanitskaya, L., O’Boyle, I., Casey, A.M.: Health Information Literacy and Competencies of Information Age Students: Results From the Interactive Online Research Readiness Self-Assessment (RRSA). Journal of Medical Internet Research 8(2), e6 (2006)

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4. Iyengar, V.S., Apte, C., Zhang, T.: Active Learning using Adaptive Resampling. In: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 91–98. Boston, Massachusetts (2000) 5. Kittler, A.F., Hobbs, J., Volk, L.A., Kreps, G.L., Bates, D.W.: The Internet as a Vehicle to Communicate Health Information During a Public Health Emergency: A Survey Analysis Involving the Anthrax Scare of 2001, Journal of Medical Internet Research 6(1) (2004) 6. Liu, R.-L., Lin, W.-J.: Adaptive Sampling for Thresholding in Document Filtering and Classification. Information Processing and Management 41(4), 745–758 (2005) 7. Liu, R.-L.: Dynamic Category Profiling for Text Filtering and Classification. Information Processing and Management 43(1), 154–168 (2007) 8. Schapire, R.E., Singer, Y., Singhal, A.: Boosting and Rocchio Applied to Text Filtering. In: Proceedings of the 21st annual international ACM SIGIR conference on research and development in information retrieval, pp. 215–223. Melbourne, Australia (1998) 9. Singhal, A., Mitra, M., Buckley, C.: Learning Routing Queries in a Query Zone. In: Proceedings of the 20th annual international ACM SIGIR conference on research and development in information retrieval, pp. 25–32. Philadelphia, Pennsylvania (1997) 10. Tang, T.T., Hawking, D., Craswell, N., Griffiths, K.: Focused Crawling for both Topical Relevance and Quality of Medical Information. In: Proceedings of the ACM 14th Conference on Information and Knowledge Management, pp. 147–154. Bremen, Germany (2005) 11. Wu, H., Phang, T.H., Liu, B., Li, X.: A Refinement Approach to Handling Model Misfit in Text Categorization. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 207–216. Edmonton, Alberta, Canada (2002) 12. Yang, Y.: A Study of Thresholding Strategies for Text Categorization. In: Proceedings of the 24th annual international ACM SIGIR conference on research and development in information retrieval, pp. 137–145. New Orleans, Louisiana (2001) 13. Yang, Y., Pedersen, J.O.: A Comparative Study on Feature Selection in Text Categorization. In: Proceedings of the 14th International Conference on Machine Learning, pp. 412– 420. Nashville, Tennessee (1997) 14. Zhang, Y., Callan, J.: Maximum Likelihood Estimation for Filtering Thresholds. In: Proceedings of the 24th annual international ACM SIGIR conference on research and development in information retrieval, pp. 294–302. New Orleans, Louisiana (2001)

Nurse Scheduling Using Fuzzy Multiple Objective Programming Seyda Topaloglu and Hasan Selim Dokuz Eylul University, Department of Industrial Engineering, 35100, Izmir, Turkey [email protected], [email protected]

Abstract. Nurse scheduling is a complex scheduling problem and involves generating a schedule for each nurse that consists of shift duties and days off within a short-term planning period. The problem involves multiple conflicting objectives such as satisfying demand coverage requirements and maximizing nurses’ preferences subject to a variety of constraints imposed by legal regulations, personnel policies and many other hospital-specific requirements. The inherent nature of the nurse scheduling problem (NSP) bears vagueness of information on target values of hospital objectives and on personal preferences. Also, the ambiguity of the constraints is some source of uncertainty that needs to be treated in providing a high quality schedule. Taking these facts into account, this paper presents the application of Fuzzy Set Theory (FST) within the context of NSP and proposes a fuzzy goal programming model. To explore the viability of the proposed model, computational experiments are presented on a real world case problem. Keywords: Fuzzy system applications, Planning and scheduling.

1 Introduction Nurse scheduling problem (NSP) consists of generating a configuration of individual schedules which are patterns of days off and on for each nurse, over a given planning horizon. The scheduling of nurses is particularly challenging because hospitals work around the clock and face fluctuating service demand on different days and shifts. Besides, there are conflicting viewpoints of the hospital and the nursing staff. Hospitals are required to fulfill minimum demand coverage during each shift, while nurses want individualized schedules that take into account their preferences. Nurses make preference concerning total work hours to be assigned for the planning period, requested days off, shift types and work patterns. A critical measure for the quality of a nurse schedule is the perceived fairness or balance for nurses. If some nurses feel that their preferences are continually being ignored, bickering, absenteeism, low morale, poor job performance and high turnover rates will start to be seen. For these reasons, it is important to satisfy nurses’ preferences evenly. The NSP is subject to a variety of constraints which can be divided into two classes generally: hard constraints and soft constraints. Hard constraints usually include demand coverage requirements, while soft constraints include all the restrictions on personal schedules and are generally referred to as time related constraints such as H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 54–63, 2007. © Springer-Verlag Berlin Heidelberg 2007

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consecutive shift type restrictions, minimum and maximum consecutive working days, complete weekend off, nurses’ preferences or requirements. Traditional methods such as linear programming, integer programming and networks have been employed to solve the NSP [1,2]. When these methods are unable to cope with the complex nature of the NSP, artificial intelligence methods, heuristics, and metaheuristics have been developed for its solution [3,4]. As pointed in Burke et al. [5], nurse scheduling in hospital environments presents a range of objectives and requirements. For example, maximizing staff preferences may conflict with an objective that requires a certain number of staff to work a certain shift. Actually, the characteristic of this problem is that it is often over-constrained by personnel preferences and priorities. In case where there are multiple goals with priorities, goal programming (GP) and other tools have been used [6,7]. Much of the decision making in the real world takes place in an environment in which the goals, the constraints, and the consequences of the possible actions are not known precisely. In such cases, Fuzzy Set Theory (FST) [8] provides the appropriate framework. In the real world applications of NSP, vagueness of information on target values of management objectives and on personal preferences, and ambiguity of the constraints are some source of uncertainty that need to be treated in providing a higher quality schedule. As emphasized by Burke et al. [5], there is a certain amount of promise in investigating fuzzy methodologies as an attempt to address the dynamic nature of the NSP problem in practice and to deal with the inherent uncertainty. To the best of our knowledge, no work has been carried out by now on addressing and dealing with the uncertainty that is inherent in the NSP using fuzzy modeling approach. Considering the aforementioned needs, and the gap in the existing literature, this paper presents the application of FST within the context of NSP. More specifically, a fuzzy multi-objective goal programming model is developed to consider uncertainty in the target values of the hospital management and nurses’ preferences. To confirm the viability of the proposed model, a real world application is provided. To provide the decision maker for a more confident solution set for policy decision making, a sensitivity analysis is presented.

2 The Proposed Model The following indices, parameters and decision variables are considered for the formulation of the model. Indices n m i j k p w Dw

number of nurses number of days within the scheduling horizon (m = 14) index for nurses; i = 1,…,n index for the day of the week; j = 1,…,m shift type; k = 0,1,2, and 3 (off day, day, evening and night shifts, respectively) scheduling period; p = 1, 2, and 3 (day: 7 am-15 pm, evening: 15 pm-23 pm and night: 23 pm-7 am periods of a work day, respectively) index for weeks, w=1, 2 set of weekend days in week w

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Parameters apk durk lwh, uwh whni ldjp, udjp stik doij

max_st min_st max_do min_do max_off min_off max_on min_on

1 if shift type k contains period p, 0 otherwise duration of shift type k lower and upper bounds on total working hours of nurses for the scheduling period the most preferred total working hour of nurse i lower and upper bounds on demand for nurses on day j in period p preference score of nurse i for shift type k to be assigned preference score of nurse i for day j to be off; j = 1,.., m-2 (weekend is considered as a single day, e.g., doi6 stands for the preference score of nurse i for the first weekend off over the two-week planning horizon) maximum preference score a nurse can achieve if assigned to all preferred shift types minimum preference score a nurse receives if assigned to the most undesirable shift types maximum preference score a nurse can achieve if all her/his requested days off are scheduled minimum preference score a nurse receives if all her/his requested days off are ignored maximum number of off-on-off patterns that a schedule can acquire minimum number of off-on-off patterns that a schedule can acquire maximum number of on-off-on patterns that a schedule can acquire minimum number of on-off-on patterns that a schedule can acquire

Decision variables Xijk 1 if nurse i is assigned to shift type k on day j, 0 otherwise Yiw 1 if nurse i takes the weekend off in week w, 0 otherwise off_pij the amount of positive deviation from the undesirable isolated day/evening/night shifts on pattern for nurse i on day j on_pij the amount of positive deviation from the undesirable isolated days off pattern for nurse i on day j µ Hjp membership function for demand coverage hospital objective in period p on day j µ N1i membership function for total working hour preference of nurse i µ N2i membership function for shift type preference of nurse i µ N3i membership function for requested days off preference of nurse i µ N4i membership function for the number of off-on-off patterns in the schedule of nurse i µ N5i membership function for the number of on-off-on patterns in the schedule of nurse i

Constraints A nurse should either take a day off or be assigned to an available shift on each work day. 3

∑X

ijk

=1

for all i = 1,..,n and j = 1,…,m

(1)

k =0

Total work hours of a nurse should be greater that the specified lower bound. m

3

∑∑ dur X k

j =1 k =1

ijk

≥ lwh

for all i = 1,..,n

(2)

Nurse Scheduling Using Fuzzy Multiple Objective Programming

57

Total working hours of a nurse should not exceed the specified upper bound. m

3

∑∑ dur X k

ijk

≤ uwh

for all i = 1,..,n

(3)

j =1 k =1

The minimum staff level for each period of a work day should be met. n

3

∑∑ a

pk

X ijk ≥ ld jp

for all j = 1,…,m and p = 1,2,3

(4)

i =1 k =1

A nurse should not work more than five consecutive days. 3

∑(X

ijk

k =1

+ X i ( j +1) k + X i ( j + 2 ) k + X i ( j +3) k + X i ( j + 4 ) k + X i ( j +5) k ) ≤ 5

(5)

for all i = 1,..,n and j = 1,…,m-5 A nurse should take at least 12 hours off between consecutive shifts. For this reason, the following constraint sets (6), (7), (8) and (9) are required. For example, constraint set (6) ensures that no nurse is assigned to a day shift immediately after a night shift. X ij 3 + X i ( j +1)1 ≤ 1

for all i = 1,..,n and j = 1,…,m-1

(6)

X ij 3 + X i ( j +1) 2 ≤ 1

for all i = 1,..,n and j = 1,…,m-1

(7)

X ij 2 + X i ( j +1)1 ≤ 1

for all i = 1,..,n and j = 1,…,m-1

(8)

X ij1 + X i ( j +1) 3 ≤ 1

for all i = 1,..,n and j = 1,…,m-1

(9)

Constraint sets (10) and (11) ensure that a nurse is assigned at least two consecutive days in a particular shift. X i 2 k ≥ X i 1k X i ( j −1) k + X i ( j +1) k ≥ X ijk

for all i = 1,..,n and k = 1,2,3 for all i = 1,..,n j = 2,…,m-1 and k = 1,2,3

(10) (11)

Constraint sets (12) and (13) are required to indicate whether a nurse takes the weekend off in the assumed work week.

∑X

ij 0

− 2Yiw ≥ 0

for all i = 1,..,n and w ΠW

(12)

∑X

ij 0

− Yiw ≤ 1

for all i = 1,..,n and w ΠW

(13)

j ∈D w

j ∈D w

It is attempted to avoid off-on-off and on-off-on patterns in each nurse’s schedule. The occurrence of this pattern is indicated by the off_pij and on_pij variables in constraints (14) and (15), respectively. 3

X ij 0 + ∑ X i ( j +1) k + X i ( j + 2 ) 0 − off _ p ij ≤ 2 k =1

for all i = 1,..,n and j = 1,…,m-2

(14)

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3

∑X k =1

3

i ( j +1) k

+ X i ( j +1) 0 + ∑ X i ( j +1) k − on _ p ij ≤ 2 k =1

(15)

for all i = 1,..,n and j = 1,…,m-2 As mentioned previously, we consider uncertainty in hospital’s objective of fulfilling demand coverage and in nurses’ objectives which are the satisfaction of their preferences concerning total work load, desired shift types, requested days off and work patterns. The trapezoidal type membership function is used for the hospital objective, while trapezoidal and triangular types are used for nurses’ objectives. Graphical illustrations of some of these membership functions and their corresponding formulations are presented below: μ Hjp

ld jp

ld jp ld jp + 1

ud jp

Fig. 1. Membership function for hospital’s objective of fulfilling demand coverage

μ Hjp

⎧ ⎪1 n 3 ⎪ ⎪ ud jp − ∑∑ a pk X ijk ⎪ i=1 k =1 =⎨ − ud (ld jp + 1) jp ⎪ ⎪0 ⎪ ⎪⎩

n

3

i =1

k =1

if ld jp ≤ ∑∑ a pk X ijk ≤ (ld jp + 1) n

3

i =1

k =1

if (ld jp + 1) ≤ ∑∑ a pk X ijk ≤ ud jp if

n

3

i =1

k =1

∑∑ a

pk

X ijk ≥ ud jp

μ N 2i 1

0 min_st

max_st

Preference score of nurse i for shift type

Fig. 2. Membership function for shift type preference

(16)

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59

μ N 1i

μ N1i 1

Workload of nurse i

0 lwh

uwh

whn i

whn i

μ N1i 1

0 lwh

whni

uwh

Workload of nurse i

Fig. 3. Some examples for the membership functions defined for nurses’ total working hour preference μ N 4i 1

0 max_off

Number of off -on-off patterns

Fig. 4. Membership functions for the number of off-on-off patterns

Membership functions for the number of on-off-on patterns ( μ N 5i ) are defined as in the same manner for the number of off-on-off patterns.

3 The Fuzzy Solution Approach Using Belman and Zadeh’s [9] min operator, the feasible fuzzy solution set is obtained by the intersection of all membership functions representing the fuzzy goals and constraints. The min operator focuses only on the maximization of the minimum membership grade. It is not a compensatory operator. That is, goals with a high

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degree of membership are not traded off against goals with a low degree of membership. Therefore, some computationally efficient compensatory operators [see, 10] can be used to investigate better results. Among the compensatory operators which are well suited in solving multi-objective programming problems, Werners’ [11] fuzzy and operator is easy to handle, and has generated reasonable consistent results in applications. For this reason, we use this approach in this paper. Werners [11] formulates the fuzzy and operator as follows.

maximize subject to

γλ + (1 − γ )(1 / K )∑k λk

⎫ ⎪ ⎪ μ k ( x ) ≥ λ + λk , ∀k ∈ K , ∀x ∈ X ⎬ ⎪ λ , λ k , γ ∈ [0, 1] ⎪⎭

(17)

and other system constraints. Where λ is the overall satisfaction level, K is the total number of fuzzy objectives and parameters, μ k (x) is the membership function of fuzzy goal k, and γ is the coefficient of compensation defined within the interval [0, 1].

4 Application of the Model to the Case Problem In the case problem, 28 nurses working in a ward are scheduled for a two-week planning period. First 11 nurses prefer the day shift, next 11 nurses prefer the evening shift and the last 6 nurses prefer the night shift the most. Due to the hospital working hour regulations, minimum and maximum working hours (lwh, uwh) for a nurse within the planning period are specified as 64 and 96 hours, respectively. The work day is split into three shifts which are: day shift (D: 7 am-15 pm), evening shift (E: 15 pm23 pm) and night shift (N: 23 pm-7 am). In order to specify the individual preferences of nurses as to the requested days off and shift types to work, Table 1 lists the degree or importance of a preference in qualitative terms, the corresponding value of v and the preference score pij (v). This pointing system has been adapted from Bard and Purnomo [12] and the only thing required is that each preference be assigned to one of the enumerated categories. The demand data for the day, evening and night shifts is taken from Ozkarahan [13]. Accordingly on weekdays, the minimum requested number of nurses (ldjp,) for the day and evening shifts is 6, whereas it is 4 for the night shift. On weekend days, the minimum requested number changes to 6, 5 and 3 for the day, evening and night shifts, respectively. The maximum requested number of nurses (udjp) has been specified as ldjp+3. The minimum and maximum preference scores a nurse can achieve if assigned to all preferred shift types (min_st and max_st) are taken as 8 and 40 in order. The minimum and maximum preference score for the requested days off (min_do and max_do) are 0 and 16. The minimum and maximum number of off-on-off patterns (min_off and max_off) and on-off-on patterns (min_on and max_on) that a schedule can acquire are 0 and 6.

The model includes 5499 constraints and 2956 variables. It has been solved for different coefficients of compensation using CPLEX 9.0 software. The comparative results are presented in Table 2.

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Table 1. Quantification of preferences Degree of preference Simple Serious Severe Extreme

Preference score, pij(v)=2v-1

Equivalent no. of preference points, v 1 2 3 4

1 2 4 8

Table 2. The solutions of the model 0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

Min

1 (min operator) 0.50

0.50

0.50

0.50

0.50

0.50

0.50

0.50

0.50

0.50

0 (ad. appr. 1

Max

1

1

1

1

1

1

1

1

1

1

1

aver. Min

0.83 0.50

0.94 0.50

0.98 0.50

0.96 0.67

0.98 0.50

0.96 0.50

0.99 0.50

0.99 0.50

0.95 0.50

0.98 0.50

1 0

J

P Hjp P N 1i P N 2i P N 3i P N 4i P N 5i

Max

1

1

1

1

1

1

1

1

1

1

1

aver. Min

0.63 0.50

0.89 0.50

0.89 0.50

0.90 0.56

0.88 0.50

0.89 0.56

0.85 0.56

0.87 0.50

0.89 0.63

0.87 0.53

0.83 0.50

Max

1

1

1

1

1

1

1

1

1

1

1

aver. Min

0.69 0.50

0.9 0.50

0.87 0.50

0.88 0.50

0.88 0.50

0.87 0.50

0.85 0.50

0.86 0.50

0.90 0.50

0.86 0.50

0.87 0.38

Max

1

1

1

1

1

1

1

1

1

1

1

aver. Min

0.6 1

0.75 1

0.77 1

0.77 1

0.78 1

0.79 1

0.80 1

0.78 1

0.78 1

0.79 1

0.79 1

Max

1

1

1

1

1

1

1

1

1

1

1

aver. Min

1 0.50

1 0.67

1 0.67

1 0.67

1 0.83

1 0.67

1 0.67

1 0.67

1 0.67

1 0.67

1 0.83

Max

0.83

1

1

1

1

1

1

1

1

1

1

aver.

0.74

0.93

0.95

0.96

0.95

0.94

0.95

0.96

0.95

0.94

0.96

As seen from Table 2, none of the solution alternatives are definitely superior over one another, and one of these alternatives can be selected as the best one concerning the priorities of the decision maker. It should be emphasized here that, the decision maker considers the tradeoffs among all of the solution alternatives. For example, if the satisfaction of nurses’ working hour preference is more important than the other objectives, the fourth alternative ( γ =0.7) can be treated as the best one. More specifically, we have the greatest value for the minimum μ N 1i and average μ N 1i , which means that among the 28 nurses the minimum achievement level for the total working hour preference is 0.67, whereas it is at most 0.50 in other solutions. Since, there is no significant difference between the membership functions of the other objectives, the alternative seems reasonable. In a similar way, if the shift type preference is more important, the ninth alternative ( γ =0.2) is selected. The solutions for γ values 0 and 1 correspond to the min operator and simple additive approach, respectively. If the average membership function values are taken into consideration, it can be seen that min operator approach performs worse relative

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to the other alternatives. On the other hand, the simple additive approach is also unsuitable for the NSP due to the uneven solution values. As seen in Table 2, the differences between the maximum and minimum membership function values for the first and third objectives of nurses are too big for a fair schedule. The schedule for γ =0.7 is presented in Table 3 as an example. Table 3. The nurse schedule Nurses 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

M D D

T D D

W D D

D D D

D D D

D D D D

D

D

E

E

E E

E E E E E E

N N N N E N

D D

D D

D

D

E E E

Week 1 Th F D D D D D D D

N N N N E N

D D E E E E E

D D E E E E E

D

N N N

E N E N N N

E E E

N

D D

D D

D

D

D D D D

D D D D

E

M D D

T D D

D D D

D E D

E E

E E

E E

E E

E N E N N

N E

N E

E

D D D E

D D D D D E E

E E E

E E N

N E E

N E E

N N

N N N

D

E E E E E E N E

N N N N

N N N

W D D

E

E

N N N

Su

D

E E E N

Sa

N N N

Week 2 Th D D D

D D D D E E E

N E E E E N N N

N E

F D D D D

D D D D E E E

E E E E N N N N E

Sa

Su

D D D D D D D

D D D D D D D

E

E

E E E

E E E E N

E

E N

E

N N E

N N E

N

For instance, nurse 1 prefers working a day shift severely. Her working hour preference is stated using trapezoidal membership function that increases between 64 and 80 hours. She requests both the weekends off. As seen from Table 3, nurse 1 has been assigned to the day shift on her working days. She works a total of 80 hours during the planning period and takes both of the weekends off as requested. Additionally, the off-on-off and on-off-on patterns in her schedule have been prevented completely. As a result, the values of all membership functions which reflect the degree of satisfaction of nurse 1’s objectives are 1.

5 Conclusion In this paper, a fuzzy multi-objective model has been developed for the NSP. Fuzzy goal programming approach is employed to consider uncertainty in the target values

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of the hospital management, nurses’ preferences and constraints. To confirm the viability of the proposed model, a real world application is provided. The individual preferences of nurses which differ from one another are treated effectively using fuzzy modeling approach, which otherwise is not possible by conventional mathematical programming methods. This work contributes to scheduling nurses more personalized and more equitably.

References 1. Miller, H.E., Pierskalla, W., Rath, G.: Nurse Scheduling Using Mathematical Programming. Operations Research 24, 857–870 (1976) 2. Millar, H.H., Kiragu, M.: Cyclic and Non-Cyclic Scheduling of 12 h Shift Nurses by Network Programming. European Journal of Operational Research 104, 582–592 (1998) 3. Meyer auf’m Hofe, H.: ConPlan/SIEDAplan: Personnel Assignment as a Problem of Hierarchical Constraint Satisfaction. In: Proceedings of the Third International Conference on the Practical Application of Constraint Technology. London, pp. 257–271 (1997) 4. Aickelin, U., White, P.: Building Better Nurse Scheduling Algorithms. Annals of Operations Research 128, 159–177 (2004) 5. Burke, E.K., De Causmaecker, P., Vanden Berghe, G., Van Landeghem, H.: The State of the Art of Nurse Rostering. Journal of Scheduling. 7, 441–499 (2004) 6. Ozkarahan, I., Bailey, J.E.: Goal Programming Model Subsystem of a Flexible Nurse Scheduling Support System. IIE Transactions 16, 306–316 (1998) 7. Azaiez, M.N., Al Sharif, S.S.: A 0-1 Goal Programming Model for Nurse Scheduling. Computers & Operations Research 32, 491–507 (2005) 8. Zadeh, LA.: Fuzzy Sets. Information and Control. 8, 338–353 (1965) 9. Bellman, R.E., Zadeh, L.A.: Decision Making in a Fuzzy Environment. Management Science 17, 141–164 (1970) 10. Lai, Y-J., Hwang, C-L.: Fuzzy Multiple Objective Decision Making Methods and Applications. Springer, New York (1994) 11. Werners, B.: Aggregation Models in Mathematical Programming. In: Mitra, G., Greenberg, H.J., Lootsma, F.A., Rijckaert, M.J., Zimmerman, H.-J. (eds.) Mathematical Models for Decision Support, pp. 295–305. Springer, Heidelberg (1988) 12. Bard, J.F., Purnomo, H.W.: Preference Scheduling Using Column Generation. European Journal of Operational Research 164, 510–534 (2005) 13. Ozkarahan, I.: A Flexible Nurse Scheduling Support System. Ph.D. Dissertation. Arizona State University (1987)

Fuzzy Adaptive Threshold Determining in the Key Inheritance Based Sensor Networks Hae Young Lee and Tae Ho Cho School of Information and Communication Engineering, Sungkyunkwan University 300 Cheoncheon-dong, Jangan-gu, Suwon 440-746, Korea {sofware, taecho}@ece.skku.ac.kr Abstract. Sensor networks are often deployed in unattended environments, thus leaving these networks vulnerable to false data injection attacks. False data injection attacks will not only cause false alarms that waste real world response efforts, but also drain the finite amount of energy in a battery powered network. The key inheritance based filtering scheme can detect a false report at the very next node of the compromised node that injected the false report before it consumes a significant amount of energy. The choice of a security threshold value in this scheme represents a trade off between security and overhead. In this paper, we propose a fuzzy adaptive threshold determining method for the key inheritance based filtering scheme. The fuzzy rule based system is exploited to determine the security threshold value by considering the average energy level of all the nodes along the path from the base station to a cluster, the number of nodes in that cluster, and the number of compromised nodes. We also introduce a modified version of this scheme to reduce the overhead for changing the threshold value. The proposed method can conserve energy, while it provides sufficient resilience.

1

Introduction

Sensor networks have attracted a lot of attention recently due to their broad applications in both military and civilian operations [1]. In many applications sensor nodes are deployed in open environments, and hence are vulnerable to physical attacks, potentially compromising the nodes cryptographic keys [2]. An adversary may use compromised nodes to inject false report into the network [3]. False data injection attacks will not only cause false alarms that waste real world response efforts, but also drain the finite amount of energy in a battery powered network [4]. To minimize the grave damage, false reports should be dropped en-route as early as possible, and the few eluded ones should be further rejected at the base station [5]. The early dropping of false reports leads to significant savings of energy [4]. Several security solutions [1,3,4,5,6,7,8,9] have recently been proposed to detect false reports. 

This research was supported by the MIC (Ministry of Information and Communication), Korea, under the ITRC (Information Technology Research Center) support program supervised by the IITA (Institute of Information Technology Advancement). (IITA-2006-C1090-0603-0028).

H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 64–73, 2007. c Springer-Verlag Berlin Heidelberg 2007 

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Fig. 1. False sensing report can be injected through compromised node (filled circle), which can lead to not only false alarms but also the depletion of limited energy resource

Especially, the key inheritance based filtering scheme [9] can detect a false report at the very next node of the compromised node that injected the false report before it consumes a significant amount of energy. Similar to [3,6], the choice of a security threshold value in this scheme is important since it represents a trade off between security and overhead. A large threshold value makes forging reports more difficult, but it consumes more energy in forwarding [6]. A small threshold value may make this scheme inefficient or even useless if the number of compromised node exceeds it [7]. Therefore, we should choose a threshold value such that it provides sufficient resilience, while still small enough to conserve energy [6]. In this paper, we propose a fuzzy adaptive threshold determining method for the key inheritance based filtering scheme. The fuzzy rule based system is exploited to determine a threshold value by considering the average energy level of all the nodes along the path from the base station to a cluster, the number of nodes in that cluster, and the number of compromised nodes. We also introduce a modified version of the key inheritance based filtering scheme to reduce the overhead for changing the threshold value. The proposed method can conserve energy, while it provides sufficient resilience. The effectiveness of the proposed method is shown with the simulation result at the end of the paper. The remainder of the paper is organized as follows: Section 2 briefly describes the key inheritance based filtering scheme and motivation of this work. Section 3 introduces the proposed scheme in detail. Section 4 reviews the simulation result. Finally, conclusion is discussed in Section 5.

2

Background

In this section, we briefly describe the key inheritance based filtering scheme (KIF) [9] and motivation of this work.

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2.1

The Key Inheritance Based Filtering Scheme (KIF) Overview

In [9], the key inheritance based filtering scheme (KIF) that can prevent forwarding of false reports is proposed. The keys of each node used in the message authentication consist of its own key and the keys inherited from (shared with) its upstream (toward the base station) nodes. Every authenticated report contains the combination of MACs generated by using the keys possessed by the consecutive nodes along the path from the base station to a terminal node. If no more than a certain number T nodes are compromised, KIF can detect a false report at the very next hop node of the compromised node that injected the false report before it consumes a significant amount of energy. Here T is a security threshold value determined based on the security requirements of the application.

Fig. 2. KIF involves the three phases: key inheritance, report endorsement, and enroute filtering. In the key inheritance phase (a), every node along a path inherits the keys from its upstream nodes. In the report endorsement phase (b), multiple nodes generate a report collaboratively. In the en-route phase (c), every forwarding node authenticates the report and replaces the un-matching MAC in the report with the MAC generated by itself.

KIF involves the following three phases: 1. In the key inheritance phase, the base station prepares the (T 2 + 3T + 4)/2 keys for the path to a terminal node. For example, the base station in Fig. 2 prepares the seven keys, k1 , · · · , k7 , for the path to node u1 when T = 2. Every node along the path inherits the T + 1 keys from its T + 1 upstream nodes and a new key is assigned to the node as its own key. It thus has T + 2 keys. For example, node u2 in Fig. 2 has the four keys, K2 = {k1 , k2 , k5 , k7 }. k2 , k5 , and k7 are inherited from u5 , u3 , and u4 , respectively. k1 is its own key.

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2. In the report endorsement phase, T + 1 nodes generate a report collaboratively when they sensed an event. More specifically, every participating node generates T + 2 MACs over the event then sends these MACs to its cluster head. The cluster head collects the MACs from all the participating nodes then put them into a report. Finally, it forwards the report toward the base station. Every report contains (T 2 + 3T + 4)/2 MACs. For example, the cluster head u4 in Fig. 2 collects the four MACs for the same event from each cluster node and generates a report R3 that contains the seven MACs, {mk1 , mk2 , mk3 , mk4 , mk5 , mk6 , mk7 }, where mk is the MAC generated by using key k. 3. In the en-route filtering phase, every forwarding node verifies the MACs in a report based on its keys. Upon success, it replaces the one different MAC in the report to the one generated by itself. Finally, it forwards the report toward the base station. For example, when node u4 receives the report R3 , it verifies the four MACs, {mk3 , mk4 , mk6 , mk7 }, in R3 by using its four keys, {k3 , k4 , k6 , k7 }. Since mk6 = mk6 and the other MACs match exactly, R3 is authenticated. The node replaces mk6 with mk6 then forwards the report that contains {mk1 , mk2 , mk3 , mk4 , mk5 , mk6 , mk7 }. 2.2

Motivation

Similar to [3,6], the choice of T in KIF is important since it represents a trade off between security and overhead. A large T makes forging reports more difficult, but it consumes more energy in forwarding [6]. A small T may make the filtering capability useless if the number of compromised node exceeds T [7]. Therefore, we should choose T adaptively such that it achieves sufficient resilience, while still small enough to conserve energy [6].

3

Fuzzy Adaptive Threshold Determining

In this section, we describe the proposed method in detail. To change T efficiently, a modified KIF for the proposed method is also introduced. 3.1

Assumptions

We assume that the base station can know or estimate the average energy level of all the nodes along the path from the base station to a cluster, the number of nodes in that cluster, and the number of compromised nodes. We also assumes that the base station has a mechanism to authenticate broadcast messages (e.g., based on μTESLA [10]), and every node can verify the broadcast messages. 3.2

Overview

In the proposed method, the base station periodically determines T for the path to each cluster with a fuzzy rule based system (Fig. 3 (a)). The average energy

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level of the nodes along the path from the base station to a cluster, the number of nodes in that cluster, and the number of compromised nodes are used to determine T . If the new T differs from the current, the base station broadcasts the new T to all the nodes along the path (Fig. 3 (b)).

Fig. 3. The base station periodically determines T with a fuzzy rule based system (a). If the new T differs from the current, the base station broadcasts the new T (b).

3.3

Factors That Determine the Security Threshold Value

The energy is the most important resource that should be considered in sensor networks. Generally, sensor nodes are limited in power and irreplaceable since these nodes have limited capacity and are unattended [11]. Therefore, we have to determine T based on the energy level of nodes. In KIF, T should be smaller than the number of cluster nodes because a report is collaboratively generated by T + 1 cluster nodes. For example, if a cluster consists of four nodes, T can be 0 (disable filtering), 1, 2, or 3. KIF can be resilient to up to T colluding compromised nodes. Thus, if a certain number C nodes are compromised, we should set T to C or larger (but smaller than the number of cluster nodes). If C exceeds the number of cluster nodes, KIF may be useless [7]. For example, KIF cannot detect false reports injected by five colluding compromised nodes when T is smaller than 5. Under this situation, we may as well disable filtering, i.e., set T to 0. So, we have to determine T based on the number of cluster nodes and the number of compromised nodes. 3.4

Fuzzy Membership Functions

The membership functions of three input parameters - (a) Energy Level, (b) Cluster Nodes, and (c) Compromised Nodes - of the fuzzy logic are illustrated in Fig. 4. The labels in the fuzzy variables are presented as follows. – Energy Level = {Very Low, Low, Above Half} – Cluster Nodes = {Very Small, Small, Large, Very Large} – Compromised Nodes = {Very Small, Small, Large, Very Large} The output parameter of the fuzzy logic is Threshold = Very Small, Small, Large, Very Large, which is represented by the membership functions as shown in Fig. 4(d). Note that we can use additional fuzzy variables (e.g., Medium) if more detailed determining is required.

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Fig. 4. The membership functions of three input parameters and of one output parameter are shown

3.5

Fuzzy Rules

If it is reported or estimated that no node has been compromised, T can be very small (e.g., 0). R04: if Energy Level is Above Half and Cluster Nodes is Very Large and Compromised Nodes is Very Small then Threshold is Very Small If a few nodes are compromised and the number of compromised nodes does not exceed the number of cluster nodes, T should be equal to or greater than the number of compromised nodes. R07: if Energy Level is Above Half and Cluster Nodes is Large and Compromised Nodes is Small then Threshold is Small If the number of compromised nodes exceeds the number of cluster nodes, KIF may be useless. Thus, we may as well disable filtering, i.e., set T to 0. R11: if Energy Level is Above Half and Cluster Nodes is Small and Compromised Nodes is Large then Threshold is Very Small If non-compromised nodes have not enough energy, although the number of compromised nodes is smaller than the number of cluster nodes, T can be either the number of compromised or 0 (if the overhead for filtering consumes too much energy).

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R34: if Energy Level is Very Low and Cluster Nodes is Very Large and Compromised Nodes is Large then Threshold is Very Small R35: if Energy Level is Very Low and Cluster Nodes is Very Large and Compromised Nodes is Small Then Threshold is Small

Fig. 5. This figure shows an example of the three phases of MKIF. Every node in MKIF inherits the keys at the deterministic indices from its upstream nodes. In MKIF, we can easily change T without key re-inheritance within the initial security threshold value.

3.6

A Modified KIF (MKIF)

In KIF, changing T may consume a significant amount of energy since all nodes have to re-inherit keys from (reestablish pairwise keys with) their upstream nodes. The proposed method may be inefficient if T is frequently changed. Thus, we introduce a modified KIF (MKIF) that does not need to re-inherit keys. In MKIF, every node inherits keys from its upstream nodes as shown in Fig. 5(a) (T = 2). The node has not its own key and the indices of its inherited keys are deterministic (similar to the concept of association in the interleaved hop-by-hop authentication scheme [3]). For example, node u3 inherits k1 , k2 , and k6 from its upstream nodes of one, two, and three hops away (i.e., u4 , u5 , and the base station), respectively. On the other hand, in KIF, these indices are randomly selected. As shown in Fig. 5(b) and (c), we can easily change T without key re-inheritance within the initial Tinit . If T has been changed to 1, the cluster head, u3 , collects M3 = {mk1 , mk2 } and M2 = {mk1 , mk3 } from itself and u2 . It then generates a report R3 that contains {mk1 , mk2 , mk3 } and forwards the report.

Fuzzy Adaptive Threshold Determining in the Key Inheritance

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71

Simulation Result

To show the effectiveness of the proposed method, we have compared the proposed method with the fixed threshold based MKIF through the simulation. Note that KIF consumes more energy than MKIF. Each node takes 16.25, 12.5μJ to transmit/receive a byte and each MAC generation consumes 15μJ [6]. The size of an original report is 24 bytes. The size of a MAC is 1 byte.

Fig. 6. This figure shows the average energy consumption caused by a report when the number of cluster nodes = 21 and the number of compromised nodes is between 1 and 20

Fig. 6 shows the average energy consumption caused by a report (authenticated or false) when the number of cluster nodes is 21 and the number of compromised nodes is between 1 and 20. As shown in the figure, the proposed method (solid line) consumes less energy than the fixed threshold based MKIF (T = 10 and 20) up to fifteen compromised nodes since the proposed method determines T adaptively according to the number of compromised nodes. MKIF with T = 10 consumes less energy than the proposed method if more than fifteen node have been compromised (it consumes about 150μJ/report). However, it cannot detect false reports when an adversary has compromised more than then nodes. That is, activating MKIF just drains the finite energy resource of the network. MKIF with T = 20 can detect forged reports up to twenty compromised nodes but consumes too much energy in energy forwarding. If no node has been compromised, it just decreases the lifetime of the network. On the other hand, the proposed method provides sufficient resilience, while still small enough to conserve energy. Fig. 7 shows the average energy consumption caused by a report when the number of cluster nodes is 11 and the number of compromised nodes is between 1 and 20. Up to ten compromised nodes, energy consumption in the proposed method is increased as the number of compromised nodes is increased. But the energy consumption is reduced when more than ten nodes have been compromised since the method disables filtering if the number of capture nodes exceeds the number of cluster nodes. Thus, the proposed method can save energy.

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Fig. 7. This figure shows the average energy consumption caused by a report when the number of cluster nodes = 11 and the number of compromised nodes is between 1 and 20

Fig. 8. This figure shows the average energy consumption per report when the number of cluster nodes = 11, the number of compromised nodes is 10, and the average energy level is between 100 and 10%

Fig. 8 shows the average energy consumption caused by a report when the number of cluster nodes is 11, the number of compromised nodes is 10, and the average energy level is between 100 and 10%. The proposed method disables filtering if the energy level is less than 40% (not enough energy to activate filtering). Thus, the proposed method can prolong network lifetime.

5

Conclusion

In this paper, we proposed a fuzzy based adaptive threshold determining method for the key inheritance based filtering scheme. The fuzzy rule based system is exploited to determine T by considering the average energy level of all the nodes along the path from the base station to a cluster, the number of nodes in that cluster, and the number of compromised nodes. We also introduced a modified

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KIF to reduce the overhead for changing T . The proposed method can conserve energy, while it provides sufficient resilience. The effectiveness of the proposed method was shown with the simulation result.

References 1. Zhang, Y., Liu, W., Lou, W., Fang, Y.: Location-Based Compromise-Tolerant Security Mechanisms for Wireless Sensor Networks. IEEE J. Sel. Area Comm. 24(2), 247–260 (2006) 2. Przydatek, B., Song, D., Perrig, A.: SIA: Secure Information Aggregation in Sensor Networks. In: Proc. of SenSys, pp. 255–265 (2003) 3. Zhu, S., Setia, S., Jajodia, S., Ning, P.: An Interleaved Hop-by-Hop Authentication Scheme for Filtering of Injected False Data in Sensor Networks. In: Proc. of S&P, pp. 259–271 (2004) 4. Li, F., Wu, J.: A Probabilistic Voting-based Filtering Scheme in Wireless Sensor Networks. In: Proc. of IWCMC, pp. 27–32 (2006) 5. Yang, H., Lu, S.: Commutative Cipher Based En-Route Filtering in Wireless Sensor Networks. In: Proc. of VTC 2003, pp. 1223–1227 (2003) 6. Ye, F., Luo, H., Lu, S.: Statistical En-Route Filtering of Injected False Data in Sensor Networks. IEEE J. Sel. Area Comm 23(4), 839–850 (2005) 7. Zhang, W., Cao, G.: Group Rekeying for Filtering False Data in Sensor Networks: A Predistribution and Local Collaboration-based Approach. In: Proc. of INFOCOM 2005, pp. 503–514 (2005) 8. Zhang, Y., Yang, J., Vu, H.T.: The Interleaved Authentication for Filtering False Reports in Multipath Routing based Sensor Networks. In: Proc. of IPDPS (2006) 9. Lee, H., Cho, T.: Key Inheritance-Based False Data Filtering Scheme in Wireless Sensor Networks. Lect. Notes Comput. Sc. 4371, 116–127 (2006) 10. Perrig, A., Szewczyk, R., Tygar, J.D., Wen, V., Culler, D.E.: SPINS: Security Protocols for Sensor Networks. Wirel. Netw. 8(5), 521–534 (2002) 11. Chi, S., Cho, T.: Fuzzy Logic based Propagation Limiting Method for Message Routing in Wireless Sensor Networks. Lect. Notes Comput. Sc. 3983, 58–67 (2006)

A New Approach for Evaluating Students’ Answerscripts Based on Interval-Valued Fuzzy Sets Hui-Yu Wang1 and Shyi-Ming Chen2 1

Department of Education, National Chengchi University, Taipei, Taiwan, R.O.C. [email protected] 2 Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, R.O.C. [email protected]

Abstract. In this paper, we present a new approach for evaluating students’ answerscripts based on the similarity measure between interval-valued fuzzy sets. The marks awarded to the answers in the students’ answerscripts are represented by interval-valued fuzzy sets, where each element in the universe of discourse belonging to an interval-valued fuzzy set is represented by an interval between zero and one. An index of optimism λ determined by the evaluator is used to indicate the degree of optimism of the evaluator, where λ ∈ [0, 1]. The proposed approach using interval-valued fuzzy sets for evaluating students’ answerscripts can evaluate students’ answerscripts in a more flexible and more intelligent manner. Keywords: Similarity functions, students’ answerscripts, interval-valued fuzzy grade sheets, interval-valued fuzzy sets, index of optimism.

1 Introduction To provide students with evaluation reports regarding their examination as sufficient as possible and with the unavoidable error as small as possible is a chief goal of educational institutions [1]. In recent years, some methods [1], [2], [4]-[17] have been presented for students’ evaluation based on the fuzzy set theory [18]. Biswas presented a fuzzy evaluation method (fem) for applying fuzzy sets in students’ answerscripts evaluation. He also generalized the fuzzy evaluation method to propose a generalized fuzzy evaluation method (gfem) for students’ answerscripts evaluation. In [1], the fuzzy marks awarded to answers in the students’ answerscripts are represented by fuzzy sets. In a fuzzy set, the grade of membership of an element ui in the universe of discourse U belonging to a fuzzy set is represented by a real value between zero and one, However, if we can allow the marks awarded to the questions of the students’ answerscripts to be represented by interval-valued fuzzy sets [8], [9], then there is room for more flexibility, where the grade of membership of an element in the universe of discourse belonging to an interval-valued fuzzy set is represented by an interval in [0, 1]. In this paper, we present a new approach for students’ answerscripts evaluation based on interval-valued fuzzy sets. The marks awarded to the answers in the H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 74–83, 2007. © Springer-Verlag Berlin Heidelberg 2007

A New Approach for Evaluating Students’ Answerscripts

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students’ answerscripts are represented by interval-valued fuzzy sets. An index of optimism λ determined by the evaluator is used to indicate the degree of optimism of the evaluator, where λ ∈ [0, 1]. If 0 ≤ λ < 0.5, then the evaluator is a pessimistic evaluator. If λ = 0.5, then the evaluator is a normal evaluator. If 0.5 < λ ≤ 1.0, then the evaluator is an optimistic evaluator. The proposed methods can evaluate students’ answerscripts in a more flexible and more intelligent manner. In Section 2, we briefly review similarity measures between interval-valued fuzzy sets from [3]. In Section 3, we present a new method for students’ answerscripts evaluation using interval-valued fuzzy sets. The conclusions are discussed in Section 4.

2 Similarity Measures Between Interval-Valued Fuzzy Sets In [19], Zwick et al. presented a method for measuring the distance between two real intervals. Let X and Y be two intervals in [ β 1 , β 2 ], where X = [x1, x2] and Y = [y1, y2]. Then, the distance D(X, Y) between the intervals X and Y can be calculated as follows: D(X, Y) =

| x1 − y1 | + | x 2 − y 2 | . 2( β 2 − β1 )

(1)

Therefore, the degree of similarity S(X, Y) between the intervals X and Y can be calculated as follows [3]: S(X, Y) = 1 – D(X, Y).

(2)

Let X and Y be two intervals in [0, 1], where X = [x1, x2] and Y = [y1, y2]. Based on Eqs. (1) and (2), the degree of similarity S(X, Y) between the intervals X and Y can be calculated as follows [3]: ⎧1, ⎪

S(X, Y) = ⎨

| x1 − y1 | + | x 2 − y 2 | , ⎪1 − 2 ⎩

if y1 ≤ x1 ≤ x 2 ≤ y

2

(3) otherwise

where S(X, Y) ∈ [0, 1]. It is obvious that if X and Y are identical intervals, then D(A, B) = 0 and S(X, Y)= 1. The larger the value of S(X, Y), the higher the similarity between the intervals X and Y. Assume that x and y are two real values between zero and one, where x = [x, x] and y = [y, y], then based on Eq. (3), we can see that S(x, y) = S([x, x], [y, y]) =1–

|

x− y|+| x− y| 2

= 1 – |x – y|. (4) ~ Let à and B be two interval-valued fuzzy sets in the universe of discourse X, where X = {x1, x2, …, xn}, à = [a11, a12]/x1 + [a21, a22]/x2 + … + [an1, an2]/xn, ~ B =[b11, b12]/x1 + [b21, b22]/x2 + … + [bn1, bn2]/xn,

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[ai1, ai2] denotes the grade of membership of xi belonging to the interval-valued fuzzy set Ã, [bi1, bi2] denotes the grade of membership of xi belonging to the interval-valued ~ fuzzy set B , 0 ≤ ai1 ≤ ai2 ≤ 1, 0 ≤ bi1 ≤ bi2 ≤ 1, and 1 ≤ i ≤ n. Based on the matrix ~ representation method, the interval-valued fuzzy sets à and B can be represented by the matrices A and B , respectively, where

A = , B = . ~ ~ If à and B are identical interval-valued fuzzy sets (i.e., à = B ), then aij = bij, 1 ≤ i ≤ n and 1 ≤ j ≤ 2. In this situation, we can see that A = B . By applying Eq. (3), the degree of similarity T( A , B ) between the interval-valued ~ fuzzy sets à and B can be measured by the similarity function T [3], n

∑ S ([ai1 , ai 2 ],[bi1 , bi 2 ])

T( A , B ) =

i =1

n

∑1−

=

, n | a i1 − bi1 | + | a i 2 − bi 2 |

i =1

2 n

,

(5)

where T( A , B ) ∈ [0, 1]. The larger the value of T( A , B ), the higher the similarity ~ ~ between the interval-valued fuzzy sets à and B . It is obvious that if à and B are ~ identical interval-valued fuzzy sets (i.e., à = B ), then T( A , B ) = 1.

3 A New Method for Evaluating Students’ Answerscripts Based on Interval-Valued Fuzzy Sets In this section, we present a new method for evaluating students’ answerscripts based on interval-valued fuzzy sets. Let X be the universe of discourse, where X = {0%, 20%, 40%, 60%, 80%, 100%}. In [1], Biswas used the five fuzzy linguistic hedges E (excellent), V (very good), G (good), S (satisfactory) and U (unsatisfactory) of the universe of discourse X, called the Standard Fuzzy Sets, for students’ answerscripts evaluation, defined as follows: E = 0/0% + 0/20% + 0.8/40% + 0.9/60% + 1/80% + 1/100%, V = 0/0% + 0/20% + 0.8/40% + 0.9/60% + 0.9/80% + 0.8/100%, G = 0/0% + 0.1/20% + 0.8/40% + 0.9/60% + 0.4/80% + 0.2/100%, S = 0.4/0% + 0.4/20% + 0.9/40% + 0.6/60% + 0.2/80% + 0/100%, U = 1/0% + 1/20% + 0.4/40% + 0.2/60% + 0/80% + 0/100%.

It is obvious that these five standard fuzzy sets E (excellent), V (very good), G (good), S (satisfactory) and U (unsatisfactory) also can equivalently be represented by ~ ~ ~ ~ ~ interval-valued fuzzy sets E , V , G , S and U , respectively, where

A New Approach for Evaluating Students’ Answerscripts

77

~ E = [0, 0]/0% + [0, 0]/20% + [0.8, 0.8]/40% + [0.9, 0.9]/60% + [1, 1]/80% + [1, 1]/100%, ~ V = [0, 0]/0% + [0, 0]/20% + [0.8, 0.8]/40% + [0.9, 0.9]/60% + [0.9, 0.9]/80% + [0.8, 0.8]/100%, ~ G = [0, 0]/0% + [0.1, 0.1]/20% + [0.8, 0.8]/40% + [0.9, 0.9]/60% + [0.4, 0.4]/80% + [0.2, 0.2]/100%, ~ S = [0.4, 0.4]/0% + [0.4, 0.4]/20% + [0.9, 0.9]/40% + [0.6, 0.6]/60% + [0.2, 0.2]/80% + [0, 0]/100%, ~ U = [1, 1]/0% + [1, 1]/20% + [0.4, 0.4]/40% + [0.2, 0.2]/60% + [0, 0]/80% + [0, 0]/100%. ~ ~ ~ ~ ~ The standard interval-valued fuzzy sets E , V , G , S and U can be represented by

matrices E , V , G , S and U , respectively, where E = , V = , G = , S = , U = . Assume that “A”, “B”, “C”, “D” and “E” are letter grades, where 90 ≤ A ≤ 100, 70 ≤ B < 90, 50 ≤ C < 70, 30 ≤ D < 50 and 0 ≤ E < 30. Assume that an evaluator evaluates the ith question (i.e., Q.i) of a student’s answerscript using an intervalvalued fuzzy grade sheet as shown in Table 1. Table 1. An interval-valued fuzzy grade sheet

Question No.

Interval-valued fuzzy mark

Derived fuzzy letter grade

Q.1 Q.2 Q.3

[0, 0] [0.2, 0.3] [0.4, 0.5] [0.6, 0.7] [0.8, 0.9] [1, 1]

…

…

…

…

…

…

…

…

Q.n Total mark = In the second row of the interval-valued fuzzy grade sheet shown in Table 1, the interval-valued fuzzy marks [0, 0], [0.2, 0.3], [0.4, 0.5], [0.6, 0.7], [0.8, 0.9] and [1, 1], awarded to the answer of question Q.1, indicate that the degrees of the evaluator’s satisfaction for that answer are 0%, 20%, 40%, 60%, 80% and 100%, respectively. Let this interval-valued fuzzy mark of the answer of question Q.1 be denoted by F~ . 1 Then, we can see that F~ is an interval-valued fuzzy set of the universe of discourse 1 X, where

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X = {0%, 20%, 40%, 60%, 80%, 100%}, ~ F1 = [0, 0]/0% + [0.2, 0.3]/20% + [0.3, 0.4]/40% + [0.6, 0.7]/60% + [0.8, 0.9]/80% + [1, 1]/100%. ~ In this case, F1 can be represented by a matrix F1 , shown as follows:

F1 = . The proposed interval-valued fuzzy evaluation method (IVFEM) for students’ answerscripts evaluation is presented as follows: Step 1: For each question in the answerscript repeatedly perform the following tasks: ~ The evaluator awards an interval-valued fuzzy mark Fi represented by an interval-

valued fuzzy set to each question Q.i by his/her judgment and fills up each cell of the ith row for the first seven columns, where 1 ≤ i ≤ n. Let Fi be the matrix ~ representation of the interval-valued fuzzy mark Fi of question Q.i, where 1 ≤ i ≤ n. Based on Eq. (5), calculate the degrees of similarity H( E , Fi ), H( V , Fi ), H( G , Fi ),

H( S , Fi ) and H( U , Fi ), respectively, where E , V , G , S and U are matrix ~ ~ ~ representations of the standard fuzzy sets E (excellent), V (very good), G (good), ~ ~ S (satisfactory) and U (unsatisfactory), respectively. Assume that H( E , Fi ) = βi1, H( V , Fi ) =βi2, H( G , Fi ) =βi3, H( S , Fi ) =βi4 and H( U , Fi ) = βi5, where βij ∈ [0, 1], ~ ~ ~ ~ ~ 1 ≤ i ≤ n, and 1 ≤ j ≤ 5. Because the standard fuzzy sets E , V , G , S and U correspond to the letter grades “A”, “B”, “C”, “D” and “E”, respectively, the derived fuzzy letter grade g~i of question Q.i is represented by a fuzzy set, shown as follows: g~i = βi1/ A + βi2/B + βi3/ C + βi4/ D + βi5/ E,

(6)

where A, B, C, D and E are letter grades, H( E , Fi ) = βi1, H( V , Fi ) =βi2, H( G , Fi ) =βi3, H( S , Fi ) =βi4 and H( U , Fi ) = βi5, βij ∈ [0, 1], 1 ≤ i ≤ n, and 1 ≤ j ≤ 5. Step 2: Calculate the total mark of the student as follows:

Total Mark =

n 1 × ∑ [T (Q.i ) × K ( g~i )], 100 i =1

(7)

where T(Q.i) denotes the mark allotted to the question Q.i in the question paper, g~i denotes the fuzzy letter grade awarded to Q.i by Step 1, and K( g~i ) denotes the derived grade-point of the derived fuzzy letter grade g~ based on the index of

λ

λ

i

λ

optimism determined by the evaluator, where ∈ [0, 1]. If 0 ≤ < 0.5, then the evaluator is a pessimistic evaluator. If = 0.5, then the evaluator is a normal evaluator. If 0.5 < ≤ 1.0, then the evaluator is an optimistic evaluator. Because 90 ≤ A ≤ 100, 70 ≤ B < 90, 50 ≤ C < 70, 30 ≤ D < 50 and 0 ≤ E < 30, the derived gradepoint K(gi) shown in Eq. (7) is calculated as follows:

λ

λ

A New Approach for Evaluating Students’ Answerscripts

79

( )

K g~i = {β i1 × [(1 − λ ) × 90 + λ × 100] + β i 2 × [(1 − λ ) × 70 + λ × 90] + β i3 × [(1 − λ ) × 50 + λ × 70] + β i 4 × [(1 − λ ) × 30 + λ × 50] + β i5 × [(1 − λ ) × 0 + λ × 30]} /( β i1 + β i 2 + β i3 + β i 4 + β i 5 ),

(8)

λis the index of optimism determined by the evaluator,λ∈ [0, 1], H( E , F )

where

i

= βi1, H( V , Fi ) =βi2, H( G , Fi ) =βi3, H( S , Fi ) =βi4 and H( U , Fi ) = βi5, βij ∈ [0, 1], 1 ≤ i ≤ n, and 1 ≤ j ≤ 5. Put the derived total mark in the appropriate box at the bottom of the interval-valued fuzzy grade sheet. Example 3.1: Consider a student’s answerscript to an examination of 100 marks. Assume that in total there are four questions to be answered:

TOTAL MARKS = 100, Q.1 carries 30 marks, Q.2 carries 20 marks, Q.3 carries 30 marks, Q.4 carries 20 marks. Assume that an evaluator awards the student’s answerscript using the interval-valued fuzzy grade sheet shown in Table 2, where the index of optimism determined by the evaluator is 0.60 (i.e., 0.60). Assume that “A”, “B”, “C”, “D” and “E” are letter grades, where 90 ≤ A ≤ 100, 70 ≤ B < 90, 50 ≤ C < 70, 30 ≤ D < 50 and 0 ≤ E < 30. ~ ~ ~ Assume that the five standard fuzzy sets are E (excellent), V (very good), G (good), ~ ~ S (satisfactory) and U (unsatisfactory) represented by interval-valued membership values, shown as follows: ~ E = [0, 0]/0% + [0, 0]/20% + [0.8, 0.8]/40% + [0.9, 0.9]/60% + [1, 1]/80% + [1, 1]/100%, ~ V = [0, 0]/0% + [0, 0]/20% + [0.8, 0.8]/40% + [0.9, 0.9]/60% + [0.9, 0.9]/80% + [0.8, 0.8]/100%, ~ G = [0, 0]/0% + [0.1, 0.1]/20% + [0.8, 0.8]/40% + [0.9, 0.9]/60% + [0.4, 0.4]/80% + [0.2, 0.2]/100%, ~ S = [0.4, 0.4]/0% + [0.4, 0.4]/20% + [0.9, 0.9]/40% + [0.6, 0.6]/60% + [0.2, 0.2]/80% + [0, 0]/100%, ~ U = [1, 1]/0% + [1, 1]/20% + [0.4, 0.4]/40% + [0.2, 0.2]/60% + [0, 0]/80% + [0, 0]/100%.

λ

λ=

Table 2. Interval-valued fuzzy grade sheet of Example 3.1 Question No.

Q.1 Q.2 Q.3 Q.4

Interval-valued fuzzy mark

[0, 0] [0, 0] [0, 0] [1, 1]

[0, 0] [0, 0] [0.5, 0.6] [0.7, 0.8]

[0, 0] [0, 0] [1, 1] [0.5, 0.6]

[0.5, 0.6] [0.7, 0.8] [0.7, 0.8] [0, 0]

[1, 1] [0.8, 0.9] [0.4, 0.5] [0, 0]

Derived fuzzy letter grade

[0.7, 0.8] [1, 1] [0, 0] [0, 0] Total mark =

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H.-Y. Wang and S.-M. Chen

From Table 2, we can see that the interval-valued fuzzy marks of the questions Q.1, Q.2, Q.3 and Q.4 represented by interval-valued fuzzy sets are F~1 , F~2 , F~3 and ~ F4 , respectively, where

~ F1 = [0, 0]/0% + [0, 0]/20% + [0, 0]/40% + [0.5, 0.6]/60% + [1, 1]/80% + [0.7, 0.8]/100%, ~ = [0, 0]/0% + [0, 0]/20% + [0, 0]/40% + [0.7, 0.8]/60% + [0.8, 0.9]/80% + F2 [1, 1]/100%, ~ = [0, 0]/0% + [0.5, 0.6]/20% + [1, 1]/40% + [0.7, 0.8]/60% + [0.4, 0.5]/80% + F3 [0, 0]/100%, ~ F4 = [1, 1]/0% + [0.7, 0.8]/20% + [0.5, 0.6]/40% + [0, 0]/60% + [0, 0]/80% + [0, 0]/100%. ~ ~ ~ ~ ~ [Step 1] The standard interval-valued fuzzy sets E , V , G , S and U can be represented by the matrices E , V , G , S and U , respectively, where E = , V = , G = , S = , U = .

~ The interval-valued fuzzy marks F~1 , F~2 , F~3 , F4 also can be represented by matrices F1 , F2 , F3 and F4 , respectively, where F1 = , F2 = , F3 = , F4 = .

By applying Eq. (5), we can get 0−0 + 0−0 1 ⎡⎛ H( E , F1 ) = ⎢⎜ (1 − 6 ⎢⎜⎝ 2 ⎣

⎞ ⎛ 0−0 + 0−0 ⎟ + ⎜ (1 − ⎟ ⎜ 2 ⎠ ⎝

⎛ 0.9 − 0.5 + 0.9 − 0.6 ⎜ (1 − ⎜ 2 ⎝ =

⎞ ⎛ 1−1 + 1−1 ⎟ + ⎜ (1 − ⎟ ⎜ 2 ⎠ ⎝

1 (1 + 1 + 0.2 + 0.65 + 1 + 0.75) 6

= 0.767,

⎞ ⎛ 0.8 − 0 + 0.8 − 0 ⎟ + ⎜ (1 − ⎟ ⎜ 2 ⎠ ⎝

⎞ ⎟+ ⎟ ⎠

⎞ ⎛ 1 − 0.7 + 1 − 0.8 ⎟ + ⎜ (1 − ⎟ ⎜ 2 ⎠ ⎝

⎞⎤ ⎟⎥ ⎟⎥ ⎠⎦

A New Approach for Evaluating Students’ Answerscripts 0−0 + 0−0 1 ⎡⎛ H( V , F1 ) = ⎢⎜ (1 − 6 ⎢⎜⎝ 2 ⎣

⎞ ⎛ 0−0 + 0−0 ⎟ + ⎜ (1 − ⎟ ⎜ 2 ⎠ ⎝

⎛ 0.9 − 0.5 + 0.9 − 0.6 ⎜ (1 − ⎜ 2 ⎝

=

⎞ ⎛ 0.9 − 1 + 0.9 − 1 ⎟ + ⎜ (1 − ⎟ ⎜ 2 ⎠ ⎝

⎞ ⎟+ ⎟ ⎠

⎞ ⎛ 0.8 − 0.7 + 0.8 − 0.8 ⎟ + ⎜ (1 − ⎟ ⎜ 2 ⎠ ⎝

⎞⎤ ⎟⎥ ⎟⎥ ⎠⎦

1 (1 + 1 + 0.2 + 0.65 + 0.9 + 0.95) 6

= 0.783, 0−0 + 0−0 1 ⎡⎛ H( G , F1 ) = ⎢⎜ (1 − 6 ⎢⎜⎝ 2 ⎣

⎞ ⎛ 0.1 − 0 + 0.1 − 0 ⎟ + ⎜ (1 − ⎟ ⎜ 2 ⎠ ⎝

⎛ 0.9 − 0.5 + 0.9 − 0.6 ⎜ (1 − ⎜ 2 ⎝

=

⎞ ⎛ 0.8 − 0 + 0.8 − 0 ⎟ + ⎜ (1 − ⎟ ⎜ 2 ⎠ ⎝

81

⎞ ⎛ 0.8 − 0 + 0.8 − 0 ⎟ + ⎜ (1 − ⎟ ⎜ 2 ⎠ ⎝

⎞ ⎛ 0.4 − 1 + 0.4 − 1 ⎟ + ⎜ (1 − ⎟ ⎜ 2 ⎠ ⎝

⎞ ⎟+ ⎟ ⎠

⎞ ⎛ 0.2 − 0.7 + 0.2 − 0.8 ⎟ + ⎜ (1 − ⎟ ⎜ 2 ⎠ ⎝

⎞⎤ ⎟⎥ ⎟⎥ ⎠⎦

1 (1 + 0.9 + 0.2 + 0.65 + 0.4 + 0.45) 6

= 0.600, 0.4 − 0 + 0.4 − 0 1 ⎡⎛ H( S , F1 )= ⎢⎜ (1 − 6 ⎢⎜⎝ 2 ⎣

⎞ ⎛ 0.4 − 0 + 0.4 − 0 ⎟ + ⎜ (1 − ⎟ ⎜ 2 ⎠ ⎝

⎛ 0.6 − 0.5 + 0.6 − 0.6 ⎜ (1 − ⎜ 2 ⎝

=

⎞ ⎟+ ⎟ ⎠

⎞ ⎛ 0.2 − 1 + 0.2 − 1 ⎞ ⎛ 0 − 0.7 + 0 − 0.8 ⎟ + ⎜ (1 − ⎟ + ⎜ (1 − ⎟ ⎜ ⎟ ⎜ 2 2 ⎠ ⎝ ⎠ ⎝

⎞⎤ ⎟⎥ ⎟⎥ ⎠⎦

1 (0.6 + 0.6 + 0.1 + 0.95 + 0.2 + 0.25) 6

= 0.450, 1− 0 + 1− 0 1 ⎡⎛ H( U , F1 ) = ⎢⎜ (1 − 6 ⎢⎜⎝ 2 ⎣

⎞ ⎛ 1− 0 + 1− 0 ⎟ + ⎜ (1 − ⎟ ⎜ 2 ⎠ ⎝

⎛ 0.2 − 0.5 + 0.2 − 0.6 ⎜ (1 − ⎜ 2 ⎝

=

⎞ ⎛ 0.9 − 0 + 0.9 − 0 ⎟ + ⎜ (1 − ⎟ ⎜ 2 ⎠ ⎝

⎞ ⎛ 0.4 − 0 + 0.4 − 0 ⎟ + ⎜ (1 − ⎟ ⎜ 2 ⎠ ⎝

⎞ ⎛ 0 −1 + 0 −1 ⎟ + ⎜ (1 − ⎟ ⎜ 2 ⎠ ⎝

⎞ ⎟+ ⎟ ⎠

⎞ ⎛ 0 − 0.7 + 0 − 0.8 ⎟ + ⎜ (1 − ⎟ ⎜ 2 ⎠ ⎝

⎞⎤ ⎟⎥ ⎟⎥ ⎠⎦

1 (0 + 0 + 0.6 + 0.65 + 0 + 0.25) 6

= 0.250.

~ ~ ~ ~ ~ Because the standard fuzzy sets are E , V , G , S and U corresponding to the letter grades “A”, “B”, “C”, “D” and “E”, respectively, the derived fuzzy letter grade g~1 of question Q.1 is represented by a fuzzy set, shown as follows: g~1 = 0.767/A + 0.783/B + 0.600/C + 0.450/D + 0.250/E.

λ

Because the value of the index of optimism given by the evaluator is 0.60 (i.e., 0.60), by applying Eq. (8), we can get

K( g~1 ) = {0.767 × [(1 - 0.60) × 90 + 0.60 ×100] + 0.783 × [(1 - 0.60) × 70 + 0.60 × 90] + 0.600 × [(1 - 0.60) × 50 + 0.60 × 70] + 0.450 × [(1 - 0.60) × 30 + 0.60 × 50] + 0.250 × [(1 - 0.60) × 0 + 0.60 × 30]} / (0.767 + 0.783 + 0.600 + 0.450 + 0.250) = (0.767 × 96 + 0.783 × 82 + 0.600 × 62 + 0.450 × 42 + 0.250 × 18) / 2.85

= (73.632 + 64.206 + 37.2 + 18.9 + 4.5)/2.85 = 69.627. In the same way, we can get K( g~2 ) = 71.343, K( g~3 ) = 60.466 and K( g~ 4 ) = 44.575.

λ=

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H.-Y. Wang and S.-M. Chen

[Step 2] Because the questions Q.1, Q.2, Q.3 and Q.4 carry 30 marks, 20 marks, 30 marks and 20 marks, respectively (i.e., T(Q.1) = 30, T(Q.2) = 20, T(Q.3) = 30 and T(Q.4) = 20), based on Eq. (7), we can get Total Mark =

T (Q.1) × K ( g~1 ) + T (Q.2) × K ( g~2 ) + T (Q.3) × K ( g~3 ) + T (Q.4) × K ( g~4 )

100 30 × 69.627 + 20 × 71.343 + 30 × 60.466 + 20 × 44.575 = 100 2088.81 + 1426.86 + 1813.98 + 891.50 = 100

= 62.211 = 62 (assuming that no half mark is given in the total mark).

4 Conclusions In this paper, we have presented a new method for evaluating students’ answerscripts based on interval-valued fuzzy sets, where the marks awarded to the answers in the students’ answerscripts are represented by interval-valued fuzzy sets. An index of optimism λ determined by the evaluator is used to indicate the degree of optimism of the evaluator, where λ ∈ [0, 1]. The proposed method can evaluate students’ answerscripts in a more flexible and more intelligent manner.

Acknowledgements The authors would like to thank Professor Jason Chiyu Chan, Department of Education, National Chengchi University, Taipei, Taiwan, Republic of China, for providing very helpful comments and suggestions. This work was supported in part by the National Science Council, Republic of China, under Grant NSC 95-2221-E011-116-MY2.

References 1. Biswas, R.: An Application of Fuzzy Sets in Students’ Evaluation. Fuzzy Sets and Systems 74, 187–194 (1995) 2. Chang, D.F., Sum, C.M.: Fuzzy Assessment of Learning Performance of Junior High School Students. In: Proceedings of the, First National Symposium on Fuzzy Theory and Applications, Hsinchu, Taiwan, Republic of China, pp. 10–15 (1993) 3. Chen, S.M.: A New Method for Handling Multicriteria Fuzzy Decision-Making Problems. Cybernetics and Systems 25, 409–420 (1994) 4. Chen, S.M.: Evaluating the Rate of Aggregative Risk in Software Development Using Fuzzy Set Theory. Cybernetics and Systems 30, 57–75 (1999) 5. Chen, S.M., Lee, C.H.: New Methods for Students Evaluating Using Fuzzy Sets. Fuzzy Sets and Systems 104, 209–218 (1999) 6. Cheng, C.H., Yang, K.L.: Using Fuzzy Sets in Education Grading System. Journal of Chinese Fuzzy Systems Association 4, 81–89 (1998)

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7. Chiang, T.T., Lin, C.M.: Application of Fuzzy Theory to Teaching Assessment. In: Proceedings of the, Second National Conference on Fuzzy Theory and Applications, Taipei, Taiwan, Republic of China, pp. 92–97 (1994) 8. Gorzalczany, M.B.: A Method of Inference in Approximate Reasoning Based on IntervalValued Fuzzy Sets. Fuzzy Sets and Systems s 21, 1–17 (1987) 9. Gorzalczany, M.B.: An Interval-Valued Fuzzy Inference Method – Some Basic Properties. Fuzzy Sets and Systems 31, 243–251 (1989) 10. Echauz, J.R., Vachtsevanos, G.J.: Fuzzy Grading System. IEEE Transactions on Education 38, 158–165 (1995) 11. Law, C.K.: Using Fuzzy Numbers in Education Grading System. Fuzzy Sets and Systems 83, 311–323 (1996) 12. Ma, J., Ma Zhou, D.: Fuzzy Set Approach to the Assessment of Student-Centered Learning. IEEE Transactions on Education 43, 237–241 (2000) 13. Wang, H.Y., Chen, S.M.: New Methods for Evaluating the Answerscripts of Students Using Fuzzy Sets. In: Proceedings of the 19th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems, Annecy, France, pp. 442–451 (2006) 14. Wang, H.Y., Chen, S.M.: New Methods for Evaluating Students’ Answerscripts Using Fuzzy Numbers Associated with Degrees of Confidence. In: Proceedings of the, IEEE International Conference on Fuzzy Systems, Vancouver, BC, Canada, pp. 5492–5497 (2006) 15. Wang, H.Y., Chen, S.M.: New Methods for Evaluating Students Answerscripts Using Vague Values. In: Proceedings of the 9th Joint Conference on Information Sciences, Kaohsiung, Taiwan, Republic of China, pp. 1184–1187 (2006) 16. Wang, H.Y., Chen, S.M.: Evaluating Students Answerscripts Based on the Similarity Measure between Vague Sets. In: Proceedings of the 11th Conference on Artificial Intelligence and Applications, Kaohsiung, Taiwan, Republic of China, pp. 1539–1545 (2006) 17. Weon, S., Kim, J.: Learning Achievement Evaluation Strategy Using Fuzzy Membership Function. In: Proceedings of the 31st ASEE/IEEE Frontier in Education Conference, Reno, NV, T3A-19–T3A-24 (2001) 18. Zadeh, L.A.: Fuzzy Sets. Information and Control 8, 338–353 (1965) 19. Zwick, R., Carlstein, E., Budescu, D.V.: Measures of Similarity Among Fuzzy Concepts: A Comparative Analysis. International Journal of Approximate Reasoning 1, 221–242 (1987)

An Intelligent Multimedia E-Learning System for Pronunciations Wen-Chen Huang1 , Tsai-Lu Chang-Chien1 , and Hsiu-Pi Lin2,3 1

National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan 2 Chia-Nan University of Pharmacy and Science, Tainan, Taiwan 3 National Yunlin University of Science and Technology, Yunlin, Taiwan

Abstract. The proposed system relates to an interactive scoring system for learning a language, in which a means such as a web camera is used to capture the learners lip movements and then a score is given by making a comparison with images stored in the database. The images stored in the database are those previously recorded by a teacher. By means of the scoring system, the learner can identify and rectify pronunciation problems concerning the lips and tongue. The system also records sounds as well as images from the student. The proposed system processes this data with multimedia processing techniques. With regard to the interactive perspective, a user-friendly visual interface was constructed to help learners use the system. The learners can choose the words they want to practice by capturing their lip image sequences and speech. The lip region image sequences are extracted automatically as visual feature parameters. Combining the visual and voice parameters, the proposed system calculates the similarity between a learners and a teachers pronunciation. An evaluation score is suggested by the proposed system through the previous similarity computation. By this learning process, learners can see the corresponding lip movement of both themselves and a teacher, and correct their pronunciation accordingly. The learners can use the proposed system to practice their pronunciation as many times as they like, without troubling the human teacher, and thus they are able to take more control of improving their pronunciation. Keywords: Multimedia E-Learning System, Speech evaluation, Automatic pronunciation scoring mechanism.

1

Introduction

An object of the proposed system is to provide an interactive scoring system for learning a language, which can evaluate a learners speech according to lip and tongue movements. To achieve this, the system basically comprises of a means for capturing images of a teacher and learners mouth movements; a database for storing the teacher movements and corresponding words; and a scoring mechanism for determining the learners score by comparing the learners mouth movements with those of the same words stored in the database. The image capturing means used in the system can be a web camera or other suitable equipment. To H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 84–93, 2007. c Springer-Verlag Berlin Heidelberg 2007 

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facilitate comparison, the images can be previously unified in size and/or number before comparison, for example, by deleting one of the images having the least difference. The learners score can be a sum of differences between the learners images and the images of the same words stored in the database; alternatively, it can be determined according to a dynamic time warping (DTW) process. The interactive scoring system can also further include a mechanism for judging whether the words to be inputted exist in the database. Accordingly, the scoring system can provide the language learner a tool to understand differences between their lip movements and the teachers when speaking the same words, and thus improve the learners pronunciation. The proposed system exhibits the following advantages: 1. The interactive system allows learners to practice alone, which can aid both learners who wish to study more outside of class as well as those who are to shy to talk in group work. 2. The lip images can help the deaf to speak more accurately, as it is obviously impractical for them to learn a language by purely audio means . 3. A novel scoring mechanism for learning languages is developed by comparing images of mouth movements between learners and teachers, since these are key to correct pronunciation. 4. The use of voice recognition also works to enhance the judging performance of the proposed system. Figure 1 shows main components of a scoring system and their relationships in operation. As shown in Figure 1 , a learner, teacher, database and scoring mechanism are involved in the proposed system. Prior to being used by the learner, the teacher can input words (hereinafter this will also include a letter or sentence) into the database as both text and mouth images, as captured by the web camera. These data will be stored in the database for comparison, and this procedure can be updated at any time. The words and lip images stored in the database are accessible, the learner can first select a word for practicing, and a web camera provided with the system captures consecutive images of the learners mouth while saying the word. The appropriate area covering the learners mouth will be determined by moving a frame shown on the computer screen. The scoring mechanism is to determine a score or grade for the learners pronunciation by comparing the learners lip movements with those stored in the database. The accuracy of the learners pronunciation can be evaluated according to a difference process or a dynamic time warping process. The onscreen process is shown in Figure 2 . Notice that the blue ellipse lines are the detected lip regions operated by the proposed system. The images will be further processed for comparison. The consecutive images in color mode are then presented on the screen for the user to determine an area covering the mouth by moving a selection frame. The frame can be scaled by inputting a width and a height or by directly dragging the frame. Figure 3 shows the corresponding images for both teachers and learners lip images after dynamic time warping (DTW) operation. The top line of images is for the teacher and the bottom line of corresponding images is for the learner.

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Fig. 1. System Framework

Fig. 2. The consecutive lip images captured by web camera

Through these corresponding images, the learner can compare the noticeable differences between these two images and correct their movements accordingly. Moreover, the recorded sound of the teacher and student can be listened to by

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Fig. 3. The corresponding lip images between teacher and student

clicking the voice button. In the preferred embodiment of the proposed system, two processes, difference and dynamic time warping (DTW), are provided for comparison. This paper is organized as follows. In the next section, the current interactive scoring system and its place in operations are described. Section 3 illustrates the characteristics and methods used in the proposed system as well as its scoring mechanism. Section 4 demonstrates the experimental results by different algorithms. At the end of this paper, a conclusion is made in section 5.

2

Background

Currently, digital tools for learning languages are very popular with learners. Some of the tools even provide figured interfaces for users to conveniently operate and practice listening, speaking, reading and writing, for example, a multimedia storytelling website for foreign language learning [11]. However, most of the tools for practicing speaking are not efficient, as only real voice or demonstration films are provided, without any feedback of the learners practicing. Moreover, learning languages by listening is unfeasible for the deaf. Similar problems occur in asynchronous on-line courses, in which audio-visual information is sent to learners for practicing. It is difficult for the learners to find errors in pronunciation and syllables by distinguishing differences between their mouth movements and the teachers, and such tools are useless for the deaf. Therefore, it is difficult to evaluate the learners competency in speaking a language. The following techniques have been developed by some researchers to address this problem. In Deng et al.s speech evaluation [1], three types of speech characteristics, i.e., magnitude, pitch contour and Mel-Frequency Cepstral coefficients, are evaluated by dynamic time warping (DTW) and Hidden Markov Model (HMM). As a result, Mel-Frequency Cepstral coefficients show the highest relationship, pitch contour shows less, and magnitude shows the least. In Liangs research about speech practicing [5] , the main activities should include syllables associated with Pin-Yin and accent, rhythm, students speech and recognition in pronunciation types. In the investigations about recognition of lip shapes [6] [3], [4] [9], [2], lip contours for different vowels pronounced by different people are statistically analyzed. According to the results of statistics, several recognizable parameters are selected to establish a classification tree for a single vowel. The modified one-dimension

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fast Hartley transform provides a structural analysis of lip contours, and the test results indicate that recognition ratios of single vowel with this classification tree are 95% for trained users and 85% for those that are untrained. Taking another approach, a bimodal audio-visual system has been developed. Mattews, etc [7] provide a recognition method for reading lips according to visual features. Three parameters representing consecutive lip contours are adapted and analyzed with Hidden Markov Model (HMM). Silsbee [10] provide other solutions in researching lip-reading with lip features, which, for example, are contour-based and imagebased. In the contour-based process, edge information, deformable templates or active contours are used to find the features which can be preserved after translation, rotation, scaling and different illumination. However, much useful information may be omitted in this method, for example, features of teeth and tongue. The image-based process includes principal component analysis, wavelet and fast Fourier transform (FFT), which describe the consecutive lip images with less information. The automatic speech recognizer (ASR) provides is able to distinguish end users speech. However, the ASR system is usually disrupted by ambient noises, and thus its accuracy is lower. Leung [4] provide another solution, in which an area covering the mouth is selected according to Elliptic Shape Function and FUZZY C-MEANS. They also apply this technique to determine the lip contours on consecutive RGB images by dividing the lips into several parts, then finding the lip features and recognizing with Hidden Markov Model.

3

Methods

The technological basis of the proposed system is based on the hybrid of image and signal processing. For a sequence of lip movement images, automatic lip detection is executed. At the same time, the users sound is recorded and the voice parameters are extracted. The Pronunciation Evaluation Mechanisms will be introduced in the last subsection. 3.1

Automatic Lip Detection

Figure 4 shows the framework of automatic lip detection. First, we need to detect the location of the human face by using YCbCr color segmentation technique. Then we use the RGB and HSV color segmentation to extract the rough part of the lip image. The noise removal method is implemented by morphology operation and median filter. Finally, the refined lip detection is obtained by the connected component. 3.2

Voice Parameter Extraction

Voice is a time-varying signal. In signal processing, under the short time stationary assumption, a frame is extracted by fixed sampling windows size. There are three common characteristic parameters used in voice processing: Linear

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Fig. 4. Automatic lip detection framework

Fig. 5. Voice Parameter Extraction

Predictive Coding, Cepstrum Coefficient, Mel-scale Frequency Cepstral Coefficients [1,5] We adopt the Mel-scale Frequency Cepstral Coefficients as our voice parameter. Figure 5 shows the procedure of voice parameter extraction. 3.3

Pronunciation Evaluation Mechanisms

In the pronunciation evaluation mechanisms, we propose a hybrid method which includes information of sequences of lip images and their corresponding voice parameters. Currently, the voice recognition rate becomes acceptable in a relatively quiet environment. However, the voice recognition rate decreases dramatically with noisier surroundings. Therefore, through the use of sequences of lip images and voice recognition techniques, the evaluation of pronunciation should be significantly enhanced. Figure 6 shows the process of the proposed pronunciation evaluation mechanisms. There are three different evaluation techniques used in the proposed system. They are spatial-temporal difference, visual lip parameters evaluation, and voice parameter evaluation.

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Fig. 6. The process of pronunciation evaluation

4

Experimental Results

After extracting lip image and voice parameters, real data experiments are implemented. The quantitative measurements of automatic lip detection and the pronunciation evaluation are performed. 4.1

Automatic Lip Detection

There were 15 people involved in testing the recognition rate of automatic lip detection and each person spoke 3 ˜ 4 sentences. We used a web camera to capture lip images, and each image size was 320x240. The sampling rate was about 20 frames per second. The experimental platform was Pentium 4 CPU 2.4GHz, 256M RAM. Since every sentence had a different length, each person has about 90 120 frames. For each lip image, there are four parts of the lip to be evaluated, the top, bottom, left and right. Table 1 shows the correct detection rate for automatic lip detection. The overall correct detection rate is about 92%. [!t] 4.2

The Pronunciation Evaluation

In our experimental environment, there were nine people using the proposed system. Every one speak eleven different sentences. Each sentence was recorded three times. Therefore, there were a total of 297 sample sentences. From all these sample sentences, the maximal error between learner and teacher is obtained and is set to be the lowest score. The evaluation result from the proposed system was divided into five groups, very good, good, normal, not good, and

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Table 1. The correct detection rate for automatic lip detection Range Lip Lip Lip Lip

Correct Error Total Percentage

(top) (bottom) (left) (right)

Total

1570 1637 1625 1628

104 37 49 46

1674 1674 1674 1674

93.78% 97.78% 97.07% 97.25%

1543

131 1674

92.17%

Table 2. The evaluation level and its corresponding score range Level Very good Good Normal Not good Bad

Range < 100 90< score = = 80< score 0, l2 (n2 )>0 b. n1 and n2 both are in the side of destination of separating line. Then normal distances are l1 (n1 )0 c.Opposite case of b. Then normal distances are l1 (n1 )>0, l2 (n2 ) and b =< a , s > conflict if s ∩ s = ∅. The greedy algorithm can be described as follows: (1)The bids are sorted by some criterion. The paper[6] proposed sorting the list L by descending average amount per good. More generally, they proposed sorting L by a criterion of the form a/|s|c for some number c, c ≥ 0, possibly depends on the number of goods, k. (2) A greedy algorithm generates an allocation. L is the sorted list in the first phase. Walk down the list L, accepting bids if the goods demanded are still unallocated and not conflicted. In the paper[6],they argue that c = 1/2 seems a best heuristic parameter for the approximation. In this paper, we use c = 1/2 as the initial parameter for using Lehmann’s algorithm. For our local searches, it is a good strategy to use c = 1/2 as the initial state. Example. Assume there are three goods a, b, and c, and three bidders Alice, Bob, and Charles. Alice bids 10 for a. Bob bids 20 for {b, c}. Charles bids 18 for {a, b}. √ We sort the bids by the criterion of the form a/ |s|. Alice’s bid is calculated as 10/ 1= √ 2 = 14(approximately). Charles’s bid is calculated 10. Bob’s bid is calculated as 20/ √ as 18/ 2 = 13(approximately). The sorted list is now Bob’s bid < 20, {b, c} >, Charles’s bid < 18, {a, b} >, and Alice’s bid < 10, {a} >. The algorithm walks down the list. At first, Bob wins {b, c} for 20. Then, Charles cannot get the good because his bid conflicts with Bob’s bid. Finally, Alice gets {a} for 10.

3 Enhanced Algorithms with Greedy Winner Determination 3.1 Hill-Climbing Search Lehmann’s greedy winner determination could succeed specifying the lower bound of the efficiency. The straightforward extension of the greedy algorithm is to construct a local search algorithm that continuously updates the allocation so that the efficiency is increased. Intuitively, one allocation corresponds to one state of a local search. An initial idea of this approach is shown in [8]. In this section, we summarize the algorithm in which we update the Lehmann’s initial allocation with the remaining bundles that could produce higher revenue. Our local search is the hill-climbing search, in which we explore new neighbor allocations until the point where there are no more allocations that have a higher revenue. In general, the definition of neighbors of a state for a local search is a key point. Here, we define single bid distance for neighbors of a state. Intuitively, the single bid distance is the distance when a state (bid allocation) can be transformed to another state (bid allocation) with inserting single bids, excluding bids that are conflicted with the inserted bids, and inserting other bids by greedy allocation while it is possible. The following algorithm describes the detailed behavior

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of our Hill-climbing search with single bid distance. We consider the main aim of this algorithm is ”allocating the goods as much as possible to each buyers”. This means that in this algorithm we do not aim to sell all goods. The inputs are L and Allocation. L is the bid list of an auction. Allocation is the initial greedy allocation of items for the bid list. 1: function LocalSearch(Alloc, L) 2: currentP rice:= price(Alloc); remainBids:= L - Alloc; 3: for each b ∈ remainBids as sorted order 4: if b conflicts B ∈ Alloc ( B is a set of bids ) then 5: newAlloc:= Alloc-B+b; consBids:= consistentBids(newAlloc, remainBids); 6: newAlloc:=newAlloc+consBids; 7: newP rice := price(newAlloc); 8: if currentP rice < newP rice then return LocalSearch(newAlloc,L); 9: end for each 10: return Alloc

The function ”consistentBids” finds consistent bids for the set ”newAllocation” by walking down the list ”remainBids”. Here, a new inserted bid will wipe out some bids that are conflicted with the inserted bid. So there will be free items to allocate after the insertion. The function ”consistentBids” tries to insert the other bids for selling the higher value goods as much as possible. Example. Assume there are three goods a, b, and c, and there are six bids, {a, b, c} = 30, {a} = 15, {c} = 13, {d, e}  = 15, {a, c} = 14, {b} = 8. Further we assume the values of Lehmann’s criterion a/ |s| are 17.6, 15, 13, 10.7, 10, and 8, respectively. In this case, the initial allocation is Lehmann’s greedy allocation {a, b, c}, {d, e} and the total revenue is 45. Here, the remaining list is {a}, {c}, {a, c}, {b}. In this algorithm, we pick {a} since it is the top of the remaining list. Then we insert {a} to the allocation and remove {a, b, c}. The allocation is now {a}, {d, e}. We then try to insert the other bids that do not conflict with the allocation. Then, the allocation becomes {a}, {b}, {c}, {d, e}. The total revenue is 51. The total revenue is increased. Thus, the allocation is updated to {a}, {b}, {c}, {d, e}. Our local algorithm continues to update the allocation until there is no allocation that has a greater revenue. This could improve and reinforce the revenue that Lehmann’s greedy allocation can achieve. 3.2 Local Search for Multiple Values of the Sorting Factor ’c’ Lehmann’s greedy winner determination could succeed specifying the lower bound of the efficiency. The straightforward extension of the greedy algorithm is to construct a local search algorithm that continuously updates the allocation so that the efficiency is increased. Intuitively, one allocation corresponds to one state of a local search. The efficiency of revenues got by Lehmann’s algorithm (and its enhancements) deeply depend on which value was set to the bid sorting factor c. Lehmann et al. reported c = 1/2 is the approximately best value. But the optimal values for each auction are varied from 0 to 1 even if the number of items is constant. Here, we propose an

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enhancement for proposed local search algorithms with multiple values parallel search for the sorting factor c. In the algorithm, the value of factor c for Lehmann’s algorithm is selected from a pre-defined list. It is reasonable to select c from neighbors of 1/2, namely, C = {0.0, 0.1, . . . , 1.0}. The results are aggregated and the best one (that has highest revenue) is selected as the final result. The outline of the algorithm is described as follows. Essentially, we selected some values for c, and for each c we conducted a local search. This part can be done sequentially and further can be done in parallel manner. The outline is very simple. The inputs are L and C. L is the bid list, namely, a set of bundles. C is a candidate c’s value list. 1: Function LSForMultiCValues(L,C) 2: maxAlloc := ∅ 3: while |C| > 0 4: c is selected from C; C := C − c; Allocation :=LocalSearch(L,c); 5: if price(maxAlloc) < price(Alloc) then maxAlloc := Alloc; 6: return maxAlloc

Function LocalSearch(L, c) finds a value when the value of sorting factor c is c for initial allocation with Lehmann’s algorithm and the bid set is L. For this function, we can use both the hill-climbing search and Lehmann’s greedy search for the demanding revenue/speed balances. 3.3 Simulated Annealing Search We also prepared a small extension of the proposed algorithm to the simulated annealing local search. The algorithm is a combination of the presented hill-climbing approach and a random search based on the standard simulated annealing algorithm. We use a parameter that represents the temperature. The temperature is set high value at beginning and continuously decreased during the temperature is larger than 0. For each cycle, a neighbor is randomly selected and its value may be less than the current value in some cases. Even such a case, if a probability value based on the temperature is larger than 0, the state is moved to the new allocation that has less value. This could make us to get off from local minimum. Also, we prepared a soft restart mechanism for SA. In the restart mechanism, c is just randomly chosen each time and continue loops until the revenue will not be updated in last k times of search. Here, we used k = 5 for our experiments. The inputs are L and Alloc. L is Bid List. Alloc is the initial allocation. 1: temp = constant; 2: function SASearch(Alloc, L) 3: currentP rice:= price(Alloc); remainBids:= L - Allocation; 4: whiletemp > 0 5: b:= randomlySelectedFrom(remainBids). 6: if b conflicts B ∈ Alloc ( B is a set of bids )then 7: newAlloc:= Alloc-B+b;

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consBids:= consistentBids(newAlloc, remainBids); newAlloc:=newAlloc+consBids; if newAlloc == ∅ then break; newP rice := price(newAlloc); if currentP rice < newP rice then return SASearch(newAlloc,L); else if currentP rice >= newP rice and SomeP robability(temp) > 0 then temp := temp − 1; return SASearch(newAlloc,L); return currentP rice

Since SA is a random search algorithm that will produce different results at each time with same parameter sets, it is reasonable to choose c just randomly and continue loops until the revenue will not be updated in last k times of search. Here, we used k = 5 for our experiments.

4 Experimental Results 4.1 Experimental Settings The following are the common settings of the experiments conducted in this paper. We implemented our algorithms in a C program for the following experiments. The experiments have been done with C-based implementations for examining the performance differences among algorithms. A prolog-based distributed and parallel execution environment is used for examining the effects of parallelism in our algorithms. The program was employed on Mac OS X 10.4.6, CPU: CoreDuo 2.0GHz, and 2GBytes memory. Thus, the actual computation time will be much smaller if we employ a massively parallel and distributed execution environment. We leave this for the future work. On each experiment, we compared the following four search algorithms. ”greedy(C=0.5)” uses Lehmann’s greedy allocation algorithm with parameter (c = 0.5). ”greedy-all” uses the best results of Lehmann’s greedy allocation algorithm with parameter (0 ≤ c ≤ 1). This is a simple algorithm but Lehmann’s et,al. did not mention about this algorithm. ”HC(c=0.5)” uses a local search in which the initial allocation is Lehmann’s allocation with c = 0.5 and conduct the hill-climbing search shown in the previous section. ”HC-all” is the aggregated best result of the hill-climbing search with parameter (0 ≤ c ≤ 1). ”SA” uses the simulated annealing algorithm shown in the last section. 4.2 Robustness of Bid Size Changes In [8], we have shown the basic performance of each algorithm in the context of revenue maximization for middle size numbers of bids(from 100 to 1000 bids) in an auction. We showed that ”HC-all” and ”SA” perform best, and their performance is increasing when the number of bids in an auction increases. On the situation of 1000 bids in an auction, both algorithms got approximately 0.997 of optimal revenues in average. We also showed that it takes approximately 600 seconds to handle 100,000 bids in an auction when we use HC-all, and it takes less than 60 seconds when we use HC(C=0.5).

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Fig. 1. The optimality of our hill-climbing algorithm in small-size bids and larger-size bids

In this section, we analyze the effects of bid size in an auction. Here, the items and the price for a bid (bundle) were set by the following. The number of items per one bundle is sampled from distribution Uniform(1, 10). The basic value (Y ) of an item is sampled from distribution Uniform(100, 10100). The actual value (X) of a bundle is defined by X = Z + (Z × j) × R. Where Z is the sum of each basic item value Y in the bundle, j is a constant parameter, and R is a random number sampled from a Gaussian distribution with mean 0 and standard deviation 1.0. We use j = 0.1 in the following experiments. Here, complementarity of items is not assumed. To cover whole phenomena in the experiments, we set the number of items to 30. We conducted 100 trials for each experimental settings and the average results of them are shown. The optimality of each determined winners for a auction is normalized to the range of the optimal solutions for value 1, and the worst solution (no items are allocated) for value 0. Here, we used CASS[5] to get the optimum solutions. Figure 1(A) shows the result of the experiment for smaller number of bids in a auction (less than 100 bids for each). Here, we can see the performance of ”SA” is best if the bids are less than 30 in a auction. The performance of ”HC-all” is stable and almost best, its the optimality value is constantly closer to 99.05 %. The performance of ”SA” is nearly equal to ”HC-all” if the bids are more than 30 in a auction. Figure 1(B) shows the result of the experiment for larger number of bids in a auction (up to 10000 for each). Since it is difficult to get optimal allocations when bid size exceeds 1000, we estimated the optimal value as 101 % of best allocations obtained by all algorithms we proposed.1 We can see the performance of ”HC-all” and ”SA” is best. The relationship among algorithms is stable. 4.3 Comparison to Zurel’s Algorithm with Standard Datasets Zurel and Nisan[7] proposed a very competitive approximate winner determination algorithm for combinatorial auctions. In [7], they evaluated the performance of their presented algorithm with the data set presented in [9]. Since the data set in [9] is relatively 1

The purpose of showing this figure is to compare performance of each algorithm. Therefore, it is not serious problem when we use estimated optimal values here.

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zurel HC-all SA HC(C=0.5) greedy-all greedy(C=0.5)

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1 0.8 0.6 0.4 0.2 0 Ar

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L6 L7 data set

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Fig. 2. The comparison between Zurel’s and ours

small size, we conducted detailed comparisons to the Zurel’s approach with common datasets from CATS benchmark[10]. Here, we used varsize dataset that supports most type of bid distributions, and it has over 800 trials for each distribution. According to Figure 2, our approach (especially, SA) is slightly better in optimality for each test set.

5 Concerns on Truthfulness For a truthful greedy protocol, Lehmann[6] pointed out the following 4 requirements: Exactness, Monotonicity, Critical, and Participation. Exactness requires that the allocation must be exact, that is, a single-minded bidder either gets exactly the set of goods he desires or he gets nothing. Monotonicity requires that if j’s bid is granted if he declares < s, v >, it is also granted if he declares < s , v  > for any s ⊆ s, v  ≥ v. Critical says that the payment for a bid that is granted does not depend on the amount of the bid, it depends only on the other bids. Then, it says that it is exactly equal to the critical value below which the bid would have lost. Participation ensures that bidders do not lose by participating in the auction. At least the above 4 requirements need to be satisfied. However, unfortunately, we found that the protocol that uses our proposed winner determination algorithm does not satisfy Monotonicity. Here, we show that our protocol does not satisfy Monotonicity. The following is one of our counter examples that show the cases that do not satisfy Monotonicity.

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Suppose player i’s bid as follows: bid s = {a, b, c, x1}, v = 10000, v/|s|c = 2500 bid s = {a, b, c}, v = 10001, v/|s|c = 3334 v is the value for a bid. v/|s|c is the criterion for sorting. We assume c = 1 in this counter example. From the definition of Monotonicity, if s is granted, it is also granted if i declares s . Suppose the other bids are declared as follows: bid A = {x1, x2}, v = 10000, v/|s|c = 5000 bid B = {x2, x3, x4, x5}, v = 12000, v/|s|c = 3000 bid C = {x3, x10, x11, x12, x13}, v = 11000, v/|s|c = 2200 bid D = {x10, x11, x12, x13, x14, x15, x16}, v = 13000, v/|s|c = 1857 bid E = {a, x2}, v = 12002, v/|s|c = 6001 bid F = {b, x11}, v = 11020, v/|s|c = 5510 bid G = {c, x10}, v = 11010, v/|s|c = 5505 Here, if the bid set is {A, B, C, D, E, F, G, s}, then the winner set is {B, D, s}. However, if the bid set is {A, B, C, D, E, F, G, s }, then the winner set is {E, F, G}. Because of the interest on the space, we omit the trace of both case. Essentially, our algorithm often enters into local optimal points in the sense of search space. Thus, Monotonicity is not satisfied.

6 Related Work There have been a lot of works on the optimal algorithm for winner determination in combinatorial auctions[9]. For example, CABOB[1] and CASS[5] have been proposed by aiming to get the optimal allocations. On the other hand, approximated algorithms have been proposed by using the techniques in the combinatorial optimization field. Lehmann[6] proposed the greedy allocation algorithm which inspired us to propose the reinforcement method proposed in this paper. Recently, Dobzinski et,al. proposed improved approximation algorithms for auctions with submodular bidders[11]. Lavi et,al, reported an LP based algorithm that can be extended to support the classic VCG[12]. Those researches are mainly focused on theoretical aspects. In the contrast to those papers, we rather focus on experimental and implementation aspects. Those papers did not present experimental analysis about the settings with large number of bids we presented in this paper. Also, Hoos[13] and Guo[14] proposed local-search based winner determination algorithms for combinatorial auction problems. We provided an analysis about effects of bid size in auction they did not provide. Furthermore, we showed the performance on standard dataset with comparison of the sophisticated algorithm proposed by Zurel.

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7 Conclusions This paper showed the robustness of our winner determination algorithms for combinatorial auctions with large number of bids. The proposed algorithms are based on the Lehmann’s greedy allocation with local improvement for multiple c values. Experimental results demonstrated that our proposed algorithms are robust on changing the distribution and the number of bids in an auction. Also, we showed that some of our algorithms perform well compared with an existing sophisticated algorithm proposed by Zurel et,al. Finally, we shortly described a theoretical limitation about our algorithms that concerns with giving truthfulness of the auction mechanism. Currently, our implementation can handle (at least) more than 1,000,000 bids. To apply our algorithms for real e-markets is one of our future work.

References 1. Sandholm, T., Suri, S., Gilpin, A., Levine, D.: Cabob: A fast optimal algorithm for winner determination in combinatorial auctions. Management Science 51(3), 374–390 (2005) 2. McMillan, J.: Selling spectrum rights. The Journal of Economic Perspectives (1994) 3. Epstein, R., Henriquez, L., Catalan, J., Weintraub, G.Y., Martinez, C., Espejo, F.: A combinatorial auction improves school meals in chile: A case of or in developing counteries. International Transactions in Operational Research 11, 593–612 (2004) 4. Cramton, P., Shoham, Y., Steinberg, R.: Combinatorial Auctions. MIT Press, Cambridge (2005) 5. Fujishima, Y., Leyton-Brown, K., Shoham, Y.: Taming the computational complexity of combinatorial auctions: Optimal and approximate approarches. In: Proc. of the 16th International Joint Conference on Artificial Intelligence (IJCAI99), pp. 548–553 (1999) 6. Lehmann, D., O’Callaghan, L.I., Shoham, Y.: Truth revelation in rapid, approximately efficient combinatorial auctions. Journal of the ACM 49, 577–602 (2002) 7. Zurel, E., Nisan, N.: An efficient approximate allocation algorithm for combinatorial auctions. In: Proc. of the Third ACM Conference on Electronic Commerce (EC 2001), pp. 125– 136 (2001) 8. Fukuta, N., Ito, T.: Towards better approximation of winner determination for combinatorial auctions with large number of bids. In: Proc. of The 2006, WIC/IEEE/ACM International Conference on Intelligent Agent Technology(IAT2006), pp. 618–621 (2006) 9. de Vries, S., Vohra, R.V.: Combinatorial auctions: A survey. International Transactions in Operational Research 15(3), 284–309 (2003) 10. Leyton-Brown, K., Pearson, M., Shoham, Y.: Towards a universal test suite for combinatorial auction algorithms. In: Proc. of EC 2000 (2000) 11. Dobzinski, S., Schapira, M.: An improved approximation algorithm for combinatorial auctions with submodular bidders. In: SODA ’06: Proceedings of the seventeenth annual ACMSIAM symposium on Discrete algorithm, pp. 1064–1073. ACM Press, New York (2006) 12. Lavi, R., Swamy, C.: Truthful and near-optimal mechanism design via linear programming. In: 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS’05), pp. 595–604 (2005) 13. Hoos, H.H., Boutilier, C.: Solving combinatorial auctions using stochastic local search. In: Proc. of the AAAI2000 (2000) 14. Guo, Y., Lim, A., Rodrigues, B., Zhu, Y.: A non-exact approach and experiment studies on the combinatorial auction problem. In: Proc. of HICSS2005 (2005)

Agent Based Dynamic Job Shop Simulation System Şerafettin Alpay Eskişehir Osmangazi University, Bademlik, 26030, Eskişehir, Turkey [email protected]

Abstract. Although most real manufacturing systems have dynamic job shop structures, there is no general analytic method that has been found for analyzing them yet and computer simulation is still an outstanding tool. One of the most difficult problems in a dynamic job shop environments is to assign the optimal due dates. Due date assignment is an important task in shop-floor control, affecting both timely delivery and customer satisfaction. The ability to meet the due dates, however, is dependent not only on reasonableness of the due dates but also on the scheduling or dispatching procedures. In this paper, an agent based dynamic job shop simulation system is designed and developed to help the decision makers who have to mainly solve the problems of selecting correct due date assignment models and dispatching rules depending on selected performance criteria in their multi machine dynamic stochastic job shop environment. Keywords: Multi-Agent Systems, Dynamic Job Shop Scheduling, Simulation, Due Date Assignment, Dispatching.

1 Introduction One of the most popular models in scheduling theory is that the job shop, as it is considered to be a good representation of general domain and has earned for being notoriously difficult to solve [1]. The job shop scheduling problem may be characterized as one in which a number of jobs, each comprising one or more operations to be performed in a specified sequence on specified machines and requiring certain amounts of time, are to be processed. The objective usually is to find a processing order or a scheduling rule on each machine for which a chosen measure of performance is optimal. In the common industrial setting, the scheduling problem is dynamic one in that jobs arrive at random over time and the processing times are not, in general deterministic. The analytical approach to dynamic problem has proved to extremely difficult, even with several limiting assumptions. In the face of the difficulties associated with analytic techniques, researchers in this area have relied on computer simulation of real representative of job shops to make a decision in the dynamic scheduling environments [2]. Because there is an important relationship between due dates and dispatching procedures that was found in the first studies on scheduling [3], to determine the optimal due date assignment models and dispatching rules to be used for selected H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 364–373, 2007. © Springer-Verlag Berlin Heidelberg 2007

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performance criteria in a dynamic stochastic job shop environment is an important task that has to be performed by the decision makers. In this paper, an agent based dynamic job shop simulation system (AB-DJSS) is designed and developed to help the decision makers who have to mainly solve the problems of selecting optimal due date assignment models and dispatching rules depending on selected performance criteria in their multi machine dynamic stochastic job shop environment.

2 Dynamic Stochastic Job Shops The job shop scheduling literature can be categorized into static problems with a fixed set of jobs and dynamic problems that allow jobs to arrive to shop in as ongoing, and usually random, fashion. If all problem parameters are known with certainty, the dynamic scheduling problem called deterministic. However, it is sometimes assumed that scheduler can not observe the parameters in advance, but only has knowledge of a probability distribution for the various problem parameters, in which case the dynamic scheduling problem referred to as stochastic [4]. In this paper, we are concerned with dynamic stochastic job shop scheduling. In a dynamic stochastic job shop, the number of jobs available for processing varies over time. Jobs continually enter and leave the production system in a random manner governed by some probabilistic laws [5]. The release times, routings and processing times of the jobs are stochastic parameters and not known in advance [6]. Incorporation this dynamic and stochastic behavior of job arrival in the theoretical model renders the results that obtained more applicable in realistic situations [5]. Analysis of dynamic job shops is usually so complicated and difficult that a feasible analytical solution procedure can hardly be found and computer simulation becomes the only feasible solution [2, 5, 7, 10]. 2.1 Due Date Assignment The consideration of due date assignments is an important element in production control, affecting both timely delivery and reduced finished goods inventory [8]. Because product and service delivery systems are not capable of successfully achieving an arbitrary set of due dates, the reasonableness of assigned due dates directly affects due date performance. Due date performance can be quantitatively measured by the conformity of a schedule to assigned due dates. Particular measures are functions of job flow time and assigned due date, such as job lateness, job tardiness and job earliness [9]. The due date management problem is of great practical significance to an organization for many vital planning functions, such as planned order release and resource requirements planning. Completion of jobs ahead of due dates would result in storage costs; on the other hand, if the jobs are completed after the due dates there will be tangible costs (e.g. clerical work, plant overtime) as well as intangible costs (e.g. loss of goodwill, dwindled customer satisfaction etc.). Management therefore desires both predictability (i.e. to set due dates correctly) and controllability (i.e. to meet the set due dates) [5]

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Due date based scheduling of manufacturing systems has been widely examined in the related literature [8,9,10,17,18,19]. Generally, the models of assigning due dates are divided into two categories: static models based on long-term shop experience, and dynamic models based on the continuously changing shop conditions such as resource availability. The availability of resources may vary over time as customer’s demands change. Under static models, arriving jobs are classified into a few groups and each job within a group is assigned the same fixed due date. Since static models do not consider continuously changing shop conditions, they do not produce realistic results. Nonetheless, they are simple and easy to implement. Under dynamic models, the job’s manufacturing interval dynamically changes according to processing and resource requirements, and to current shop conditions. Dynamic models are more complex to implement, but produce more realistic results [11]. 2.2 Dispatching Rules Dispatching is a procedure that uses logical decision rules to select a job for processing on a machine that has just become available. These decision rules, also called dispatching or priority rules, determine the value of priority attribute that is assigned to each job. Once the priorities for all candidate jobs are determined, the jobs are sorted and the job with the highest priority is chosen [12]. Researchers and practitioners have used dispatching rules which essentially suboptimal, though nevertheless effective and simple in their application even to job shop problems of fairly large size [13]. One classification of the rules is based on the detail of information they use. A local rule ignores all information except that which is locally available. In contrast, a global rule is not confined to the local details and may use information gathered from other machines, such as that pertaining to the queue at the next machine in the job route. Another classification of dispatching rules is based on the influence of the timing on their decisions. A static rule makes decisions that only depend on the status quo of the jobs and machines, i.e., the decisions made by the rule are not affected by when they are made, and assigned job priorities do not change over time. A dynamic rule, on the other hand, incorporates time-dependent information based on the current state of the scheduling environment or even the anticipated status of the environment at some time in the future. Consequently, job priorities assigned by dynamic rules do change over time [12]. For a detailed list of such rules and classifications, please see Panwalker and Iskander study [14]. 2.3 Performance Criteria Several performance criteria have been used to evaluate shop scheduling performance. The classification of the more commonly used criteria may be given as: time based measures (e.g. mean waiting time, machine idle time), work in process measures (e.g. average number of jobs in queue), due date related measures (e.g. mean tardiness, mean earliness, mean absolute lateness) and cost based measures (e.g. cost of idle machines, cost of carrying work in process inventory). Among those criteria, due date related measures are well known and they suggest several objectives that one can use to formulate scheduling problems. Two of them that have become

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classics are minimizing the summarized tardiness and minimizing the maximum lateness [10].

3 Agent Based Dynamic Job Shop Simulation System (AB-DJSS) Job shop type manufacturing systems consist of different machines with unique capabilities. Depending on job routing there are two types of job shops: classic job shop and job shop with recirculation. In the latter case, a job may visit a machine more than once, whereas in the former it does not. Depending on how many machines the job shop consists of, the number of machines in a job route is limited, but combinatorially increasing by the number of machines. In case of dynamic scheduling problem, the arrival of jobs is continuous, the time interval between the arrivals is mainly set by statistical analysis of the modeled real manufacturing system. Decreasing or increasing the time interval causes higher or lower demand and shop load ratio. AB-DJSS has been developed to meet the requirements of high flexibility and compatibility. Multi-agent architecture has been found to be the most suitable for the purpose of simulating the different job shop environments and collecting required output information of the system. AB-DJSS provides the decision makers an opportunity to construct the different multi machine dynamic stochastic job shop models including classic job shops or job shop with recirculation. AB-DJSS presents many due date assignment models and dispatching rules that decision makers can select to analyze. AB-DJSS is also capable of gathering the information related to many performance criteria. That information may be very explanatory in analyzing the due date performance of a real system and be very useful to make a decision if necessary. Three types of agents have been built: shop agent, machine agent and job agent. Shop agent creates machines. The number of machines (m) is defined by the decision maker according to the real manufacturing environments. Each machine is then assigned to one machine agent. Due to multi machine architecture, the number of machines can be extended or reduced easily. After creating the machines, shop agent creates jobs by given time interval of job arrivals which has been set by using negative exponential distribution with λ parameter. Shop agent sets λ parameter value according to the expected shop utilization rate (shop load ratio). The expected shop utilization rate can be determined by: ρ = λμoμg / m

(1)

where ρ is the expected shop utilization rate, μo is the mean operation time and μg is the mean number of operations per job. Whenever a job is created, one job agent is assigned to the job. The shop agent also creates an operation list for newly generated job. The operation list defines the job route as well and it is randomly generated by using uniform distribution between minimum and maximum number of operations. For classic job shops, a machine can appear only once in the operation list whereas for the job shops with recirculation, a machine can appear more than once. For each operation in the list, an operation time is assigned by using exponential distribution considering the defined mean operation time (μo). Total processing time of the job is the sum of all its operation times. After the operation list and operation times are

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generated, the due date of the job can be determined. Shop agent assigns the due date of the job by using the selected due date assignment model. Available due date assignment models classified as dynamic and static models provided by AB-DJSS are given below: • − − − − −

Static models Slack time (SLK), Total work content (TWK), Number of operations (NOP), TWK + NOP, Processing time + Wait (PPW),

• − − −

Dynamic models Job in Queue (JIQ), Job in System (JIS), Operation Flow time Sampling (OPF) − Dynamic TWK, Dynamic PPW

Some of the models like JIQ and JIS require an allowance value and they have to be determined before using. When decision maker can not supply a value and/or no historical data is available for estimation, this determination process may need special pre-simulation runs to generate the historical data and statistical analysis of them for estimation. AB-DJSS is also capable of doing pre-simulation studies and generating all required information for further analysis. All information about the operation list, operation times, and the due date are then sent to the related job agent through the communication and then the processing of the job starts. Each job agent carries all necessary information about its job. That information may vary depending on the selected due date assignment models and dispatching rules. The job agent manages the job through the machines in its operation list. When a job enters the queue of a machine, job agent provides all required information to the machine agent for dispatching operation when the machine is in idle. This information consists of expected operation time on the machine, due date and remaining processing time of the job. The job agent continuously monitors the job in the system and refreshes remaining processing time information when required by the machine agent. When all operations on the job are completed, the job agent finalizes the process related to the job. This finalization process includes all calculation operations related to the selected performance criteria and collecting the statistical data. This information is then sent to the shop agent so that it gathers and produces the final performance results for the simulation run and the life cycle of a job agent ends. The number of job agents at a time depends only on the number of jobs in the shop and there is no limitation for it. Some of the selectable performance criteria provided by AB-DJSS are given below: • − − −

Performance criteria Flow time related measures, Tardiness related measures, Earliness related measures,

− Lateness related measures, and also − Mean absolute lateness (MAL), − Mean squared lateness (MSL).

When the job agent sends the job to the next machine’s queue in its operation list, it informs the machine agent related with the machine that a new job enters its queue.

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When the machine is in idle, the machine agent queries the required information from the shop agent and the job agents related to the jobs in its queue and determines the priorities of the jobs according to the selected dispatching rule. Available rules provided by AB-DJSS are classified as static and dynamic rules and given below: • − − − − − −

Static rules First in first out (FIFO), Last in first out (LIFO), Shortest processing time (SPT), Longest processing time (LPT), Earliest due date (EDD), First in system first served (FISFS),

• Dynamic rules − Slack time (SLK) − Slack time per operation (SLK/OPN) − Modified operation due date (MOD), − Critical ratio (CR), − Critical ratio + shortest processing time (CR+SPT), − Allowance + Critical ratio + Shortest processing time (ALL+CR+SPT).

After determination of the job priorities, the machine agent sequences the jobs according to their priorities and selects the job with the highest priority as the next job to be processed in the machine. When the job leaves the machine, the job agent reads the next operation from the operation list of the job and sends the job to the next machine’s queue. The shop agent is also responsible for managing the simulation runs and requires some simulation parameters be determined by decision makers to start the simulation. These parameters are given below: • − − −

Simulation parameters Simulation period or total number of jobs to be simulated, Replication number, and Number of jobs to be ignored to reach the steady state: AB-DJSS helps decision makers to determine the number of jobs to be ignored by providing “Welch’s graphical procedure” [15].

While a simulation run is in progress, the shop agent collects much information about the process like flow time, waiting time, due dates, jobs in route, time in route and jobs in system. After the simulation is completed, the shop agent summarizes and presents the results related to the selected performance criteria. All the results and collected information can also be saved into a data file for further analysis.

4 Analysis of Performance of AB-DJSS One of the important advantages of AB-DJSS is that AB-DJSS is very flexible and provides the decision makers an opportunity to construct the simulation models for the different dynamic job shop environments quickly. Extendable structure of

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AB-DJSS and no more needing any programming knowledge for the simulation process are other important advantages. Although there is no general analytic method for analyzing dynamic job shops, Cheng [16] made an analysis of job flow time in a dynamic job shop. Cheng, in his study, generated the mean and standard deviation of job flow time analytically in a dynamic job shop in which First In First Out (FIFO) dispatching rule was used. Cheng also made a simulation study for the same shop conditions and compared the simulation results with analytically obtained results to test. As it will be true to test the results of AB-DJSS with the results of Cheng’s analytic model instead of comparing with the results of different simulation studies, a simulation study in a dynamic stochastic job shop which has the same specifications and assumptions, was designed and performed by AB-DJSS. For 10 replications by ABDJSS, the obtained mean and standard deviation values of flow-time (F) are given in Table 1. In the Table, m denotes the number of machines and ρ denotes shop load ratio. Table 1. Mean and standart deviation values of Flow-time for 10 replications by AB-DJSS

No 1 2 3 4 5 6 7 8 9 10 Av

Mean 13,184 12,954 13,575 13,178 13,483 13,119 13,196 13,208 12,725 13,367

Std. 9,31 10,06 9,14 9,50 10,58 9,27 9,45 9,83 9,55 9,41

13,198

9,61

m=5, ρ=0.90 Mean Std. 53,891 40,91 56,816 40,26 59,602 42,03 57,580 41,53 56,829 36,80 52,323 36,04 51,909 36,31 44,361 30,64 63,383 42,96 57,475 39,15

m=9, ρ=0.50 Mean Std 24,407 16,79 25,108 16,38 22,832 14,48 24,796 16,67 23,740 15,79 24,282 15,94 22,920 15,33 24,085 15,39 25,764 17,97 23,637 15,96

m=9, ρ=0.90 Mean Std. 116,150 75,01 105,940 58,08 94,508 68,01 125,802 80,01 106,247 67,40 119,514 77,06 83,807 54,73 107,879 68,33 129,276 97,24 127,303 83,58

55,417

24,157

111,642

38,66

16,07

72,95

Final results of AB-DJSS and Cheng’s analytic and simulation results are summarized in Table 2. Table 2. Final results of AB-DJSS and Cheng (1985)

F mean

m=5, ρ=0.50 m=5, ρ=0.90 m=9 ρ=0.50 m=9 ρ=0.90

Cheng’s simulation results

Cheng’s analytic results

13,17 55,29 24,38 109,44

13,32 56,59 23,97 112,69

ABDJSS results 13,20 55,42 24,16 111,64

Cheng’s simulation results

8,88 41,84 15,50 100,32

F Std. Dev. Cheng’s ABanalytic DJSS results results 8,28 9,61 37,82 38,66 14,45 16,07 71,03 72,95

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The final results of AB-DJSS are closer to the analytically obtained results than Cheng’s simulation results and Table 2 confirms that AB-DJSS is more capable of producing the valid simulation results. AB-DJSS is superior to a non-agent based simulation architecture from many different viewpoints: flexibility, extendibility, construction speed of new and different dynamic job shop simulation models and ease of use.

5 Case Study Dispatching Rules: Four commonly used dispatching rules were selected [17]: 1. FIFO, 2. SPT, 3. CR+SPT, 4. All+CR+SPT Due Date Assignment Models: The following due date assignment models were used [17]: 1. TWK, 2. PPW, 3. DTWK, 4. DPPW Performance Measures : Two shop performance measures, Mean Absolute Lateness (MAL) and Mean Squared Lateness (MSL) [17] were considered. Shop Utilization: Two shop utilization rates were considered in the study: 90% and 80% [17]. The shop model: General job shop scheduling assumptions were applied for the purpose of model standardization [17]. The parameters of the model are as follows: 1. The shop is composed of 5 non-identical machines. 2. Job arrivals at the shop form a Poisson process. The shop arrival rate is determined by the shop utilization setting. 3. The number of operations per job varies uniformly between 1 and 9. 4. To determine a job’s routing, each machine is equally likely to perform the job’s next operation. A machine can perform more than one operation of a job under the constraint that no two successive operations require the same machines. 5. Operation processing times are exponentially distributed with a mean of 1 unit of time. 6. Number of jobs to be ignored is first 1.000 jobs (determined by using Welch’s graphical procedure). 7. To minimize the variation of the results, 6 replications were made. Results and discussions For each of 32 experiments (i.e. 4×4×2), 6 independent runs were made and 10.000 jobs were completed after the completion of the first 1.000 jobs in each run. The data were then gathered and averaged over the 6 runs. All MAL and MSL results are summarized in Table 3. The results in Table 3 indicate a great variation among the combinations of the three decision factors. Performance deteriorates as shop utilization increases. In particular, there is a drastic deterioration in the mean squared lateness (MSL). This is because the more congested the shop is, the less stable the scheduling system becomes, thus, causing increases in job flow time as well as in its variability. For the performance of dispatching rules, the results in Table 3 demonstrate that due date dependent rules, CR+SPT and ALL+CR+SPT, outperforms due date independent rules, FIFO and SPT. They also shows that due date information is very helpful in

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controlling and coordinating the scheduling process that renders the performance more robust to the variations on the shop load. Dynamic due date assignment models exhibit a performance superior to that of their static counterparts. Under almost all combinations of dispatching rules, both MAL and MSL performances are improved when dynamic due date assignment models are employed. This case is more evident in higher shop loads with CR+SPT and ALL+CR+SPT rules. Thus, dynamic due date assignment models, especially DTWK model, in conjunction with due date related dispatching rules provide the greatest improvement in heavily loaded shop in respect of MAL and MSL performances. Table 3. Experimental results for MAL and MSL (MSL results are given in parenthesis) UTIL.

D.D.A. Mod.

FIFO

SPT

CR+SPT

ALL+CR+SPT

TWK

23.37 (1004.3) 19.73 (766.7) 20.68 (891.9) 14.77 (413.9) 11.12 (232.7) 9.65 (186.6) 10.23 (231.3) 9.20 (160.1)

13.30 (1216.6) 14.44 (1401.3) 13.03 (1191.6) 18.09 (1442.0) 5.99 (165.6) 13.15 (310.2) 6.65 (166.2) 10.22 (247.7)

14.56 (457.1) 14.64 (458.2) 7.62 (131.3) 9.81 (184.1) 7.32 (121.5) 7.26 (121.2) 4.59 (50.2) 7.22 (99.9)

14.46 (454.9) 14.48 (446.9) 7.54 (126.6) 9.76 (182.1) 7.14 (113.2) 7.14 (114.5) 4.58 (49.7) 7.22 (99.8)

PPW %90 DTWK DPPW TWK PPW %80 DTWK DPPW

6 Conclusions In this paper, an agent based dynamic job shop simulation system is designed and developed to help the decision makers who have to mainly solve the problems of selecting correct due date assignment models and dispatching rules depending on selected performance criteria in their multi machine dynamic stochastic job shop environment. To reduce the complexity of analyzing the dynamic job shops and increase the flexibility in the decision making process, the system is designed and developed as multi agent system. Using the developed system, it is possible to simulate many dynamic manufacturing processes with different job structures under various operating conditions and monitor the system behaviors. The system provides 10 due date assignment models (5 of them are static and other 5 are dynamic), 12 dispatching rules (6 of them are static and other 6 are dynamic) and many performance criteria for analyzing the dynamic job shops. By determining many other shop parameters, it is also possible to generate and collect the large number of simulation data for further analysis. All generated and collected data can also be saved into disk files in mostly known statistical formats.

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As the system is in multi agent architecture, new due date assignment models and new dispatching rules as well as new performance criteria can be easily added to the system and the system can be easily extended.

References 1. Jain, A.S., Meeron, S.: A state-of-the-art-review of job shop scheduling tecniques (1998) http://citeseer.nj.nec.com/jain98stateart.htm 2. Ramasesh, R.: Dynamic job shop scheduling: a survey of simulation research. OMEGA 18, 43–57 (1990) 3. Roman, D.B., del Valle, A.G.: Dynamic assignation of due dates in an assembly shop based simulation. International Journal of Production Research 34(6), 1539–1554 (1996) 4. Wein, L.W., Ou, J.: The impact of processing time knowledge on dynamic job shop scheduling. Management Science 37(8), 1003–1014 (1991) 5. Cheng, T.C.E, Gupta, M.C.: Survey of scheduling research involving due date determination decisions. European. Journal of Operations Research 38, 156–166 (1989) 6. Jones, A., Rabelo, L.C.: Survey of job shop scheduling (1999) http://www.nist.gov/ msidlibrary/doc/jobshop1.pdf 7. Cheng, T.C.E: Optimal common due date with limited completion time deviation. Comput. Opns. Res. 15(2), 185–188 (1988) 8. Tsai, C.H, Chang, G.T., LI, R.K.: Integrating order release control with due date assignment rules. International Journal of Production Research 35(12), 3379–3392 (1997) 9. Vig, M.M., Dooley, K.J.: Dynamic rules for due date assignment. International Journal of Production Research 29(7), 1361–1377 (1991) 10. Lengyel, A., Hatono, I., Ueda, K.: Scheduling for on-time completion in job shops using feasibility function. Computers & Industrial Engineering 45, 215–229 (2003) 11. Rajasekera, J.R, Murr, M.R., So, K.C.: A due date assignment model for a flow shop with application in a lightguide cable shop. Journal of Manufacturing Systems 10(1), 1–7 (1991) 12. Bhaskaran, K., Pinedo, M.: Dispatching. In: Salvendy, G. (ed.) Handbook of Industrial Engineering. ch. 83, John Wiley and Sons, New York (1992) 13. Blackstone, J.H., Phillips, D.T., Hogg, G.L.: A state-of-the-art survey of dispatching rules for manufacturing job shop operations. International Journal of Production Research 20, 27–45 (1982) 14. Panwalker, S.S., Iskander, W.: A survey of dispatching rules. Operations Research 25, 45–61 (1977) 15. Law, A.M., Kelton, W.D.: Simulation modeling and analysis. McGraw-Hall, New York (1991) 16. Cheng, T.C.E: Analysis of job flow time in a job shop. Journal of Operations Research Society 36(3), 225–230 (1985) 17. Cheng, T.C.E, Jiang, J.: Job shop scheduling for missed due dates performance. Computers and Industrial Engineering 34(2), 297–307 (1998) 18. Chang, F.C.R.: A study of factors affecting due date predictibility in a simulated dynamic job shop. Journal of Manufacturing Systems 13(6), 393–400 (1997) 19. Sha, D.Y., Liu, C.-H.: Using data mining for due date assignment in a dynamic job shop environment. Int. Adv. Manuf. Technol. 25, 1164–1174 (2005)

A Manufacturing-Environmental Model Using Bayesian Belief Networks for Assembly Design Decision Support* Wooi Ping Cheah1, Kyoung-Yun Kim2, Hyung-Jeong Yang1,**, Sook-Young Choi3, and Hyung-Jae Lee1 1

Dept. of Computer Science, Chonnam National University, Gwangju 500-757, South Korea [email protected], [email protected], [email protected] 2 Dept. of Indust. and Manuf. Eng., Wayne State University, Detroit, MI 48202, USA [email protected] 3 Dept. of Computer Education, Woosuk University, Samruy, Chonbuk, South Korea [email protected] Abstract. Assembly design decision making is to provide a solution of currently violating design by evaluating assembly design alternatives with the consideration of the assembly design decision (ADD) criteria and of the causal interactions with manufacturing-environmental factors. Even though existing assembly design support systems have a systematic mechanism for determining the decision-criterion weight, the system still has a limitation to capture the interactions between manufacturing-environmental factors and ADD criteria. Thus, we introduce in this paper, Bayesian belief networks (BBN) for the representation and reasoning of the manufacturing-environmental knowledge. BBN has a sound mathematical foundation and reasoning capability. It also has an efficient evidence propagation mechanism and a proven track record in industry-scale applications. However, it is less friendly and flexible, when used for knowledge acquisition. In this paper, we propose a methodology for the indirect knowledge acquisition, using fuzzy cognitive maps, and for the conversion of the representation into BBN. Keywords: Applications to manufacturing, assembly design, decision support, Bayesian belief networks, fuzzy cognitive maps.

1 Introduction An assembly design decision (ADD) problem occurs when the current assembly design violates assembly specifications, such as maximum allowance in surface straightness or maximum stress. An example is that of the corner joint in which a designer specifies a sharp edge of a corner joint as a weld seam; and a low weld penetration and high stress level around the weld seam is indicated by an assembly analysis. When a problem is indicated on the current assembly design, the designer *

This work was supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD, Basic Research Promotion Fund) (KRF-2006-003-D00511). ** Corresponding author. H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 374–383, 2007. © Springer-Verlag Berlin Heidelberg 2007

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must make a decision whether to accept the current joint or modify it, and whether the current joining method should be controlled or another joining method considered, if the joint must be modified. Assembly design decision making is to provide a solution for such a dilemma by evaluating the weights of various ADD criteria, such as assembly cost, assembly quality, and assembly design. Typically, assembly design decision making is a multi-disciplinary task. It requires negotiations between diverse stakeholders: manufacturing engineers, financial and marketing analysts, quality experts, design specialists; all must resolve trade-offs while maintaining performance goals. It involves consideration of interdependent behavior among different disciplines (i.e., causal interactions between various manufacturing-environmental factors, such as competitiveness and market demand). The task is complicated due to the complex nature of such interactions. The results are often subjective, causing biases on the final decision. Assembly design decision support provides an efficient methodology for capturing causal interactions between manufacturing-environmental factors and ADD criteria. It also provides an automated mechanism for determining ADD criterion weights based on the interactions [1]. In this paper, we introduce Bayesian belief network (BBN) for the representation and reasoning of the manufacturing-environmental knowledge. It has a sound mathematical foundation based on probability theory and a powerful reasoning capability. It supports predictive, diagnostic, and mixed modes of reasoning, and its results are consistent and unambiguous. It has an efficient evidence propagation mechanism based on conditional independence, with high scalability. It also has a proven track record in industry-scale applications as shown in the next section. However, when BBN is used as a front-end tool for knowledge acquisition, it is less friendly and flexible. Elicitation of root and conditional probability distributions is often a challenge with a BBN. Domain experts find it unnatural and difficult to specify causal relationships in terms of conditional probabilities, especially when the number becomes large. Therefore, in this paper, we propose a methodology for the indirect acquisition of domain knowledge using cognitive map (CM) with causal weights, called fuzzy cognitive map (FCM), as a front-end tool, and for the conversion of the representation into a BBN.

2 Related Work There has been much research on decision supporting in manufacturing domain. Subru et al. used genetic algorithms for a design-manufacturing-supplier decision problem for an agile manufacturing environment [2]. Rekiek et al. proposed a method to treat the resource planning for the assembly line problem [3]. In their work, designer’s preferences were captured by adjusting the weight of the different objectives. Zha proposed a neuro-fuzzy approach for assemblability and assembly sequence evaluation [4] and also introduced knowledge intensive Petri net models to integrate design and assembly planning and utilized knowledge-based agents acting as decision supporting tools [5]. LeBacq et al. presented a methodology for the joining selection [6]. Their method was based on a questionnaire and a database including the characteristics of joining and the material. A Hierarchical Semantic Net model was presented by Kim [1] to allow design alternatives to be systematically evaluated. However, this model has a limitation to capture the interactions between manufacturing-environmental factors and assembly design decision criteria.

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BBN is a well established method for reasoning under uncertainty, using a graphical structure to represent causal relationships and probability calculus to quantify these relationships and update beliefs given new information. BBN has had considerable industry-scale applications: Microsoft’s Lumiere project [7] for inferring the goals and needs of software users; MIT’s HDP project [8] for differential therapy of cardiovascular disorders; and HP’s SACSO project [9] for printer trouble shooting. Even though there are many applications of BBN in various decision support systems, to the best of our knowledge, there is no existing research ever applied BBN to assembly design decision support. So far the closest to our work is the application of BBN in change impact analysis in the domain of architecture design [10]. A CM [11] is a collection of nodes connected by some causal links or edges. The nodes represent variables of a domain. The links represent the direction of influence. Links have a sign, which can be positive (a promoting effect) or negative (an inhibitory effect). An FCM [12] is a “fuzzified” version of cognitive map, which allows causal links to have a value in [–1, 1]. It also allows feedback, which adds a temporal aspect to its operation.

3 Capturing Causal Knowledge with FCM Causal knowledge is important in decision making because decision problems are described and understood through causal connections. Cognitive mapping is an efficient methodology for capturing causal knowledge of domain experts. It represents knowledge visually and more descriptively, hence, it is more comprehensible, less timeconsuming and causes lesser inconvenience to experts during knowledge elicitation. Nadkarni et al. proposed a systematic approach for capturing causal knowledge from domain experts [13]. It includes a method for the elicitation of unstructured knowledge, with a set of open ended interview questions. It also includes a procedure for the subsequent derivation of environmental factors and initial causal structure. Figure 1 shows an initial CM for the assembly design environment, elicited from the domain experts based on this approach.

Fig. 1. Causal structure for the manufacturing-environmental factors and ADD criteria

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However, Nadkarni et al. did not propose a method for capturing causal strengths. Instead, they used conditional probability distributions to quantify the causal relationships between a node and the parents. This approach is less friendly and less natural to the domain experts. In our methodology, causal weights or values are elicited from the experts and represented using an FCM. Conditional probability distributions can be derived directly from the causal values in the FCM. In general, an FCM with n nodes can be described by an n×n adjacency matrix, Mij, whose elements, eij, are the causal value (representing causal strength) of the link directed out of Vi into Vj. Table 1 shows an adjacency matrix for the FCM. In the table, the causal value for the link from C2 to E2 is 0.8, which indicates a strong positive causality from C2 to E2. Table 1. Adjacency matrix for FCM.

4 Constructing BBN from FCM The migration involves two stages, qualitative and quantitative. Qualitative migration involves the transformation of the qualitative structure of FCM (i.e., the original “unfuzzified” or un-weighted CM into a BBN compatible qualitative causal structure). Quantitative migration involves the transformation of “fuzzified” causal weights or causal values into the conditional probability distributions in BBN. For each variable or node in the BBN compatible qualitative causal structure, there will be a conditional probability table (CPT) associated with it. 4.1 Building BBN Compatible Causal Structure from CM The initial CM is less structured due to the way knowledge is elicited from the experts. Conducting interviews using open ended questions often produces incomplete or redundant representation. Therefore, a more structured interview with guided questions is required to ensure the completeness and to eliminate redundancy in the representation. The initial CM requires modification to make it compatible with BBN by performing four operations: 1) ensuring conditional independency; 2) removing indirect relationships; 3) converting abductive links to deductive; and 4) eliminating circular relations [13]. The operations are elaborated below and the result is shown in Figure 2.

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Fig. 2. BBN compatible causal structure

In BBN, all the dependent nodes are to be linked with an arrow, so that when there is no link between two nodes, we can conclude that the nodes are conditionally independent. A systematic analysis of the CM yielded an additional positive link, from quality control to assembly quality. This is intuitive as increased quality control naturally improves the assembly quality. Another positive link, from quality control to assembly cost, is also added. It is logical to believe that the resources and effort spent in practicing quality control increases the assembly cost. Quality control was, somehow, not perceived as a direct factor to assembly cost. Instead, assembly quality was perceived as a direct factor. Hence, the direct causal link from quality control to assembly cost will replace the indirect link from assembly quality to assembly cost. In BBN, only variables with direct causal relationships are linked with an arrow directly. Hence, the links between variables which are indirectly related are to be removed. The indirectly related variables are to be separated as conditionally independent variables. The direct links between the following pairs of variables were removed: (E3, E2), (E3, E6), (E3, E8), (E4, E1), (E5, E4), (C1, E2), (C2, C1), (C2, E4), (C3, E4). Each of them is substituted by one or more indirect links which indicate the propagation of causal effects. For example, the positive link from quality control to competitiveness was removed. After the removal, quality control affects competitiveness positively through assembly design. It also affects competitiveness positively through assembly quality via (or without via) assembly design. It affects competitiveness negatively through sales price via assembly cost. Causal statements involving abductive reasoning are often represented by a link from effect to cause in CM. These causal links are to be converted as links from cause to effect (i.e., in the direction of causation). The reverse, effects to cause, relationships will be inferred by using the probabilistic inference mechanism of BBN. In Figure 1, there is a negative link from productivity to assembly design. The link is abductive and it is removed. Instead, a negative deductive link from assembly design to productivity is added in Figure 2. The rationale is that a better (often more complex) assembly design usually requires more time and effort, hence lower productivity. In CM, circular relations violate the acyclic graphical structure required in BBN, hence, they are to be removed. Figure 1 shows a two-way relation between assembly design and sales price. The link from assembly design to sales price represents the

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correct cause to effect relationship between the two variables. The positive abductive link, from sales price to assembly design is a redundant reciprocal relationship, which is removed. The positive abductive link, from assembly design to quality control is redundant and also removed. The two-way relation between competitor’s advertisement and market share can be resolved by separating the linkages into two different time frames. In the current time frame the concern is the negative effect of the competitor’s advertisement to the market share. In the future time frame the concern is the positive effect of the market share to the competitor’s advertisement. It is important to clearly separate events for future time frame from the current time frame to avoid ambiguity in the interpretation of the reasoning results. In this work, we confine our model to the current time frame of the decision being modeled, and we remove the link from market share to competitor’s advertisement. There is a loop between the three variables: quality control, assembly design, and assembly quality. The negative link from assembly quality to quality control represents the fact that high quality assembly will require less control and managing in the future; though, currently, the high quality assembly is the result of the high quality control. Since the link pertains to the future time frame, it is removed. 4.2 Constructing CPTs of BBN from Causal Weights of FCM The above four operations have modified the qualitative structure of the FCM making it compatible with BBN. The conversion has also changed the adjacency matrix and the result is shown in Table 2. This updated matrix is useful for constructing CPTs for the BBN. There are three steps involved in the construction of CPTs from causal weights: 1) summing the causal effects; 2) normalizing the tables; and 3) assigning probability to the opposite state. Table 2. BBN compatible adjacency matrix

Table 3(a) shows a result of summing the causal effects from three sources: E3, E5, and E7. It shows the probability distributions for C1, one for each configuration of states of its parents. For example, what is the probability C1 increases (or the probability it decreases) given E3, E5, and E7 all increase. From the updated adjacency matrix, in C1 column there is a positive effect of 0.6 from E3, a negative effect of 0.3 from E5, and a positive effect of 0.4 from E7. When E3, E5 and E7 all increase the resultant causal effect is the algebraic sum of the individual effects, i.e., (+0.6)+(–0.3)+(+0.4) = +0.7. When all decrease, the resultant causal effect is

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(a)

(b)

(c)

(–0.6)+(+0.3)+(–0.4) = –0.7 and so fourth. When the resultant causal effect is positive, it represents an effect to the ‘+’ state of C1, and a negative resultant causal effect represents an effect to the negative state of C1. After summing the causal effects, the value for the ‘+’ state of C1 is greater than 1 (i.e., 1.3), when both E3 and E7 increase but E5 decreases. The value for the ‘–’ state of C1 is also 1.3, when both E3 and E7 decrease but E5 increases. A probability value greater than 1 is not acceptable in probability theory. Therefore, a normalization process is necessary. Since we only want the relative strength, it is fine to modify the values, as long as their ratios remain unchanged. We normalize the probabilistic values by dividing each of them using the maximum value, which is greater than 1. In our example, we divide all the values by 1.3, and the result is shown in Table 3(b). In Table 3(b), when both E3 and E5 increase, E7 decreases. The three factors, collectively, produce a causal effect of 0.08, to the ‘–’ state of C1, which is the state of interest. We have no knowledge about the causal effect to the counterpart (i.e., the ‘+’ state of C1). However, in BBN, the ‘+’ state has to be assigned 0.92 (1 – 0.08). This causes a semantic problem because it implies that the collective effect from the three factors is more likely to cause an increase to C1 (0.92) than a decrease (0.08). This is commonly recognized as a limitation of the classical probability theory. We propose a simple and practical method which ensures the assigned probability is always smaller than the probability of the state of interest, though it does not eliminate the assignment of probability to the counterpart state. Without any knowledge, we assume a prior probability of 0.5 for both ‘+’ and ‘–’ states of a variable. The value of 0.5 indicates absolute uncertainty of their likelihood (i.e., the variable has the same chance for increase and decrease (fifty-fifty)). Once concrete evidence (complete certainty) is acquired for a particular state of interest, its probability immediately increases to 1, and the counterpart state immediately decreases to 0. Hence, the probability range of the state of interest is 0.5. The minimum probability is 0.5 and the maximum probability is 1. The counterpart state, on the other hand, stays within 0 and 0.5; hence, it is always less than the state of interest. Suppose we are 50% sure (0.5 initial probability) that a variable will increase (i.e., in between absolute uncertainty and absolute certainty). Based on our proposed method, the moderated probability should fall exactly in between 0.5 and 1, which is 0.75. It can be computed using a simple proportionality formula, as follow: Moderated Probability = (Initial Probability × Probability Range) + Minimum Probability.

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In the example related to the CPT of C1, the initial probability for the state of interest (i.e., the ‘–’ state) is 0.08. Applying the formula, we do the following moderation: (0.08 × 0.5) + 0.5 = 0.54. The probability for the counterpart (i.e., the ‘+’ state) is therefore 1 – 0.54 = 0.46. We perform the same moderation for all the other combinations, and the result is shown in Table 3(c).

5 Experimental Results A simulation is implemented using BBN software, Hugin Lite 6.7 [14], and the experimental results are discussed below. Initially, when no evidence has been added (i.e., no change of any manufacturing-environmental factors or no emphasis on any ADD criteria), the original marginal probability, for all the states is 0.5 (50%). It indicates that there is a 50% chance for all the states in the network. As manufacturing-environmental changes are introduced to the system, we want to predict their effect to the ADD criteria. Alternatively, as one or more ADD criteria are emphasized, we want to predict the effect to the manufacturing-environmental variables. Let us assume that there is an increase of 0.8 (initial probability) in the emphasis for assembly quality. This change is inserted as evidence to set the ‘+’ state of the node to 90% [(0.8 × 0.5) + 0.5 = 0.9]. Given this evidence, we can do a what-if analysis and predict that there is an 80% chance that the competitiveness will increase (see Figure 3). The impact flows further up to the market share but with a lesser effect, there is a 62.4% chance that it will increase. As such, the market share changes come from its indirect dependency on assembly quality. Another application is to diagnose possible causes and influences for a change or emphasis in assembly design. Given a domain variable, we want to reason about the factors which influence it. For instance, if we increase market share by 0.8 (i.e., to set

Fig. 3. Predictive BBN model

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its ‘+’ state to 90%), as in Figure 4, we observe that there is a 65.53% chance that competitiveness will increase, 59.66% chance that market demand will increase, and 68.41% chance that competitor’s advertisement will decrease. These three variables are regarded as direct factors or causes to market share. We can continue the analysis to all the ancestor nodes by tracing “downwards” as far as to assembly cost with the ‘+’ state probability of 56.52%, assembly quality 62.4%, and assembly design 63.15%. If increasing market share is our main concern, it is obvious that the competitor's advertisements have the greatest negative effect (–68.41%), and competitiveness the greatest positive effect (65.53%). Assembly design, in turn, has the greatest positive effect (63.15%) to the competitiveness.

Fig. 4. Diagnostic BBN model

In practice, a domain variable is rarely only a source or a target of a change. Instead, it is often a probable source causes a change to other variables, and at the same time a probable target affected by their changes. As an example, competitiveness has been identified as a probable (65.53%) cause to the increase of market share, as shown in Figure 4. It has, however, become a probable (80%) target affected by an increase in assembly quality, as shown in Figure 3. If both market share and assembly quality increase at the same time, the combining effect is cumulative. In other words, the combination of both the predictive and diagnostic inference will push up the probability of the increase in competitiveness to 87.99%. It will also change the posterior probability for the increase of both market share and assembly quality to 93.72%, higher than what was entered initially (i.e., 90%). Due to space limit, only a small system is discussed in this paper. A more realistic system can be implemented by extending the model with additional domain variables.

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6 Concluding Remarks When used as a front-end tool for knowledge acquisition, BBN is less friendly and flexible as compared to FCM. Elicitation of root and conditional CPTs from domain experts is often a challenge with BBN. Several methods have been established to facilitate the indirect acquisition of such knowledge from experts. One way is to use FCM as a front-end tool and then convert the representation to BBN as was discussed in this paper. Another way is to apply machine learning techniques to acquire the graph structure and the conditional probability distributions from data. Cycles are not permitted in the BBN formalism. The acyclic prohibition limits exact modeling of systems to those without feedback or bidirectional causality. Dynamic BBN is an ongoing area of research which seeks to model systems that change over time.

References 1. Kim, K.: Assembly Operation Tools for e-Product Design and Realization. Ph.D. Dissertation, University of Pittsburgh (2003) 2. Subru, R., Sanderson, A., Hocauğlu, C., Graves, R.: Evolutionary Decision Support for Distributed Virtual Design in Modular Product Manufacturing. Production Planning and Control 7, 627–642 (1999) 3. Rekiek, B., De Lit, P., Delchambre, A.: Hybrid Assembly Line Design and User’s Preferences. Int. J. of Production Research 5, 1095–1111 (2002) 4. Zha, X.: Neuro-Fuzzy Comprehensive Assemblability and Assembly Sequence Evaluation. Artificial Intelligence for Eng. Design, Analysis, and Manufacturing 5, 367–384 (2001) 5. Zha, X.: A Knowledge Intensive Multi-Agent Framework for Cooperative/Collaborative Design Modeling and Decision Support of Assemblies. Knowledge-Based Systems 8, 493–506 (2002) 6. LeBacq, C., Brechet, Y., Shercliff, H., Jeggy, T., Salvo, L.: Selection of Joining Methods in Mechanical Design. Materials and Design 23, 405–416 (2002) 7. Horvitz, E., Breese, J., Heckerman, D., Hovel, D., Rommelse, K.: The Lumiere Project Bayesian User Modeling for Inferring the Goals and Needs of Software Users. In: Proc. 14th Conf. Uncertainty in Artificial Intelligence, pp. 256–265 (1998) 8. Long, W.: Medical Diagnosis Using a Probabilistic Causal Network. Applied Artificial Intelligence 2(3), 367–383 (1989) 9. Skaanning, C., Jensen, F., Kjaerulff, U.: Printer Troubleshooting Using Bayesian Networks. In: Logananthara, R., Palm, G., Ali, M. (eds.) IEA/AIE 2000. LNCS (LNAI), vol. 1821, pp. 367–379. Springer, Heidelberg (2000) 10. Tang, A., Nicholson, A., Jin, Y., Han, J.: Using Bayesian Belief Networks for Change Impact Analysis in Architecture Design. To appear in J. Systems and Software, DOI: 10.1016/j.jss.2006.04.004 11. Axelrod, R.: Structure of Decision - The Cognitive Maps of Political Elites. Princeton Univ. Press, Princeton (1976) 12. Kosko, B.: Fuzzy Cognitive Maps. Int. J. Man-Machine Studies 1, 65–75 (1986) 13. Nadkarni, S., Shenoy, P.: A Causal Mapping Approach to Constructing Bayesian Networks. Decision Support Systems 2, 259–281 (2004) 14. Hugin Expert A/S: How to Get Started with Hugin Software (2006) URL: http:// www.hugin.com/Products_Services/Products/Demo/Lite/

Evaluation of Two Simultaneous Continuous Speech Recognition with ICA BSS and MFT-Based ASR Ryu Takeda, Shun’ichi Yamamoto, Kazunori Komatani, Tetsuya Ogata, and Hiroshi G. Okuno Graduate School of Informatics, Kyoto University, Japan {rtakeda, shunichi, komatani, ogata, okuno}@kuis.kyoto-u.ac.jp

Abstract. An adaptation of independent component analysis (ICA) and missing feature theory (MFT)-based ASR for two simultaneous continuous speech recognition is described. We have reported on the utility of a system with isolated word recognition, but the performance of the MFT-based ASR is affected by the configuration, such as an acoustic model. The system needs to be evaluated under a more general condition. It first separates the sound sources using ICA. Then, spectral distortion in the separated sounds is estimated to generate missing feature masks (MFMs). Finally, the separated sounds are recognized by MFT-based ASR. We estimate spectral distortion in the temporal-frequency domain in terms of feature vectors, and we generate MFMs. We tested an isolated word and the continuous speech recognition with a cepstral and spectral feature. The resulting system outperformed the baseline robot audition system by 13 and 6 points respectively on the spectral features.

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Robot audition systems need to be able to separate speech and recognize the separated speech signals because they ”hear” a mixture of sounds in the real world. We proposed integrating independent component analysis (ICA) and missing feature theory (MFT)-based automatic speech recognition (ASR) [1], to achive a robot audition system that does not assume any constraints on the surrounding environment. ICA only assumes mutual independence of component sound signals and does not need a priori information about such things as head-related transfer functions of robots. Because the mixture of sounds can never be completely separated by ICA or other technologies, post-processing of imperfectly separated or distorted signals is necessary. MFT-based ASRs only need an acoustic model to cope with the distortion of separated signals unlike multi-condition training methods, and they recognize the signal based on its reliability. We estimated the distortion by focusing on the amount of change in the features with noise by using separated signals. Some researchers have previously integrated speech separation and missing data techniques. Kolossa et al. integrated ICA and time-frequency masking and H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 384–394, 2007. c Springer-Verlag Berlin Heidelberg 2007 

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missing data techniques using a Mel-frequency cepastral coefficient (MFCC) feature [2]. Yamamoto et al. generated a time-frequency mask by using estimated noises from a post filter [3]. Kalle et al. reported that combining the MFCC and spectrum features is effective for reverberant speech [4]. Because missing data techniques are affected by the method used for signal separation, feature, the acoustic model, and the method for implementing MFT with ASR, we needed to validate our method under various conditions. Therefore, we tested our method with isolated word and continuous speech recognition and with spectral and cepstral features. The rest of this paper is organized as follows: Section 2 explains the ICA and the MFT-based ASR. Section 3 presents how missing feature masks (MFMs) can be automatically generated. Section 4 describes the experiments, Section 5 shows the results, and Section 6 concludes the paper.

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The overview of our system is shown in Fig. 1. We selected ICA for speech separation because it offers blind source separation and is useful in various environments with MFT-based ASR. 2.1

Speech Separation: ICA

ICA needs no information other than the independence of sound. We used the frequency domain representation instead of the temporal domain one because ICA converges faster in the frequency domain. Mixing Process for Speech Signals. We assumed that the signals would be observed by linearly mixing sound sources. This mixing process is expressed as x(t) =

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where x(t) = [x1 (t), . . . , xJ (t)]T is the observed signal vector, and s(t) = [s1 (t), ..., sI (t)]T is the source signal vector. In addition, a(n) = [aji (n)]ji is the mixing filter matrix of length N , where [X]ji denotes a matrix that includes the element X in the i-th row and the j-th column. We assume both the number of microphones, J, and the number of sound sources, I, are 2.

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Frequency-Domain ICA. We used frequency-domain (FD)-ICA. First, a short-time analysis of the signal we observed was conducted with a frame-byframe discrete Fourier transform (DFT) to obtain the observed vector, X(ω, f ) = [X1 (ω, f ), . . . , XJ (ω, f )] in each frequency bin, ω, and at each frame, f . The unmixing process can be formulated in a frequency bin as Y (ω, f ) = W (ω)X(ω, f ),

(2)

where Y (ω, f ) = [Y1 (ω, f ), . . . , YI (ω, f )] is the estimated source signal vector, and W represents a (2 by 2) unmixing matrix in a frequency bin. An algorithm based on the minimization of Kullback-Leibler divergence is often used on speech signals for estimating the unmixing matrix, W (ω), in Eq. (2). Therefore, we used the following iterative equation with non-holonomic constraints [5]. W j+1 (ω) = W j (ω) − α{off-diagφ(Y )Y h }W j (ω),

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where α is a step-size parameter that controls the speed of convergence, j expresses the value of the j th step in the iteration, and · denotes the timeaveraging operator. The operation, off-diag(X), replaces each diagonal element of matrix X with zero. The nonlinear function, φ(y), is defined as φ(yi ) = tanh(|yi |)ejθ(yi ) [6]. The problems of FD-ICA are ambiguities with scaling and permutation. We solved these with Murata’s method, i.e., by using the envelope of a power spectrum [7]. 2.2

Speech Recognition Based on Missing Feature Theory

The following two points are very important when we use MFT-based ASR because they are part of the main processing of speech recognition. 1. Features of speech recognition 2. Calculation of likelihood with reliability Features of MFT-Based ASR. The spectral feature, Mel scale log spectrum (MSLS), is used as the main feature in MFT-based speech recognition because the MFCC is unsuitable for identifying distorted features. We used MSLS (24 + Δ24) by applying an inverse discrete cosine transform (DCT) to the MFCC features. Calculation of Likelihood with Reliability. MFT-based ASR is a hidden Markov model (HMM)-based recognizer that assumes input consists of reliable and unreliable spectral features. We used the multi-band approach [8] which is a the marginalization approach that takes into account the reliability of the feature in calculating the likelihood. Let M (i) be an MFM vector, which represents the reliability of the i-th acoustic feature. The output probability, bj (x), is given by N  L   bj (x) = P (l|Sj ) exp M (i) log f (x(i)|l, Sj ) , (4) l=1

i=1

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Fig. 2. Detection of distortion in MSLS

where P (·) is a probability operator, x(i) is an acoustic feature vector, N is the size of the acoustic feature vector, Sj is the j th state, and f (xj |Sj ) is a mixture of L multivariate Gaussians in the j th state. In the marginalization approach [9], the output probability is calculated based on using knowledge about unreliable features. If knowledge about unreliable features is unavailable, the output probability equation is equivalent to Eq. (4).

3

Automatic Generation of Missing Feature Mask

The following three steps are needed to generate the missing feature mask. 1. Detection of the distortion in the feature domain 2. Setting of the reliability in accordance with the distortion 3. Generation of the MFM from the separated signals 3.1

Detection of Distortion

Distortion in the Feature Domain. Features for speech recognition should be irrelevant to scaling because they are affected by the volume of the speakers without it. First, we define the mapping of the vector feature-extraction function, F , from the time-frequency spectrum for the time-dimension feature. Here, we assume F is continuous and continuously differentiable. A feature with the scaling-free property satisfies the equation F (x) = F (α .∗ x),

(5)

where .∗ denotes the product of the corresponding element of the vectors, x ·y = (x0 y0 , x1 y1 , . . . , xn yn ). We now focus on the target signal, s, in the mixture signal, αx + βy, and express its feature as F (αx + βy) = F (x + (β ./α)y) = F (x + θ .∗ y).

(6)

We define the distortion of the feature of the signal and noise together, s + n, as D = F (s + n) − F (s)

(7)

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Detection of the Distortion. We suppose the feature changes almost completely monotonically with the noise. In particular, the feature has linearity, and the distortions in the feature are proportional to the difference in the distortions represented by the following equation. D = F (s + θn) − F (s)  θ ∂∂F s (s)n.

(8)

Then, if we have two features, F (s + α .∗ n) and F (s + β .∗ n), and the ratio between α and β is a constant, γ, we can detect the distortions by predictinging the ambiguity of the scaling based on the following approximation. D  F (s + α .∗ n) − F (s + β .∗ n)  F (s + α .∗ n) − F (s + γα .∗ n)  γ 3.2

∂F (s)α .∗ n. ∂s

(9)

Setting the Reliability

The mask to the distortion, D, is generated from the following approximation, D  |F (s + α .∗ n) − F (s + β .∗ n)| .

(10)

For this distortion, we generate an MFM, M , with a threshold, Tn , as follows:  1 |F (s + α .∗ n) − F (s + β .∗ n)| < Tn M= , (11) 0 otherwise where Tn is the threshold for the n-delta feature. MFM Generation from Signals Separated by ICA. ICA gives the following separated signals. Y (ω, t) = S(ω, t) + W (ω)E(ω)S(ω, t).

(12)

Here, W is the estimated unmixing matrix, E is an error matrix derived from E = H − W −1 , and H is a true mixing matrix. Because the estimation of the unmixing matrix is not perfect, error terms invariably exist. We use signals that are scaled to solve the problem of ambiguity in scaling. The scaled signal, y1 (ω, t), with g1 is expressed as g1 y1 (ω, t) = g1 ((1 − w11 e11 − w12 e21 )s1 (ω, t) − (w11 e12 + w12 e22 )s2 (ω, t))(13) (14) = g1 (1 + e1 ) s1 (ω, t) − eˆ1 g1 s2 (ω, t), where e1 and eˆ1 are error coefficients. We obtain y2 (ω, t) the same way. By calculating the following equation, we can get two signal and detect distortions from Eq. (9). yˆ1 (ω, t) = g1 y1 (ω, t) − γg2 y2 (ω, t).

(15)

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Table 1. Experimental conditions @ Experiment 1 Experiment 2 Test Set Male and Female (200 words) Male and Female (100 sent.) Training Set 10 Males, 12 Females, 216 words 100 Males, 100 Females, 150 sent. Acoustic Model Triphone: 3-state 4-mix. HMM Triphone: 3-state 8-mix. HMM Language Model Finite Grammar: 200 words Statistical Language: 20,000 words

4

Experiments

We used two omni-directional microphones installed as ears in the SIG2 humanoid robot (Fig. 3). We evaluated our method by getting the robot to recognize two simultaneous speech signals based on the following criteria. 1. Experiment 1: Isolated word recognition (a) The relation between parameters γ and T0 (b) The effect of ICA and MFM on Word Correct (WC) (c) The difference between MSLS and MFCC 2. Experiment 2: Continuous speech recognition (a) The effect of ICA and MFM on WC and Word Accuracy (WA) (b) The difference between MSLS and MFCC Isolated word recognition is an evaluation for practical use, and continuous speech recognition is an evaluation under a general condition. In these experiments, we determined (1) the relation between the two parameters, (2) the effect of ICA and MFM on WC and WA, and (3) the difference between MSLS and MFCC. 4.1

Recording Conditions

Two voices, one male and one female, were recorded simultaneously from loudspeakers placed 1.0 m from the robot, as shown in Fig. 4. Asymmetric and symmetric configurations were used because ICA-based separation is affected by the positions of the speakers. The angle, θ, between the two loudspeakers was 30, 60, or 90◦ . The female speaker was to the left of the robot, and the male was to the right. The room we used is 4×5 m, and has a reverberation time of 0.2–0.3 sec.

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100 90 80 70 60 50 40 30 20 10 0

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We used combinations of two different words selected from a set of 200 phonemically balanced Japanese words for Exp. 1, and we used two different sentences selected from a set of 100 newspaper articles and 50 phonemically balanced Japanese sentences for Exp. 2.

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4.2

Experimental Conditions

We used a multi-band Julian [8] as the MFT-based ASR. It uses a triphonebased acoustic model (3-state, 4-mixture) trained with 216 words of clean speech uttered by 22 male and female speakers for Exp. 1, and a triphone (3-state, 8-mixture) trained with 150 sentences clean speech uttered by 200 male and female speakers for Exp. 2. These training data sets do not include the data for the evaluation (open test). The number of HMM mixtures is different for Exp. 1 and Exp. 2, but this is not important for the purpose of these experiments. The statistical language model was used for Exp. 2, and it consists of 20,000 words, which were extracted from newspapers. Table 1 lists these conditions. The main conditions for ICA are a data-sampling rate of 16 kHz, a frame length of 2,048 points, and a frame shift of 512 points. After separating the signals in the frequency domain, we converted them to those in the time domain with the overlap-add method and extract the features because the best frame length is different for separation and feature extraction. The initial values for the unmixing matrix, W (ω), were given at random. We used 0.005 for Exp. 1 and 0.02 for Exp. 2 with MSLS and 0.01 for Exp. 1 and 0.2 for Exp. 2 with MFCC. We did not mask the delta parameters. Symmetric position：MFCC

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5 5.1

Results Isolated Word Recognition

Figure 5 shows the relation between the threshold, T , and the scaling factor, γ. The curve of an a priori mask is a convex function because if the threshold is too low, few of the features are used for recognition, and if the threshold is too high, the features include distorted ones. Figures 6 and 7 show the average improvement in the recognition, 24 points for the ICA and 13.3 points for the MFM with the MSLS, and with the MFCC, 24 points for the ICA and 6.2 points for the MFM. Clearly, We can see the MFM is effective when used after ICA by 8.2 points on average. If an a priori

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mask is adapted, the WC was improved by over 90%. With the MFM, the MSLS performs as well as the MFCC. In terms of absolute WC, MFCC + MFM is the best combination in Exp. 1. 5.2

Continuous Speech Recognition

Figures 8, 9, 10, and 11 show the results of each feature. ICA improved 11.0 point with the MSLS and 17.23 point with the MFCC, and the MFM improved 8.68 points and 1.89 points with the MSLS and the MFCC on average. In this experiment, the MFCC mask was not effective, unlike in the experimentt on isolated-word recognition. An a priori mask works well, but it is not sufficient compared with an limit of 80% WA. The difference in WC and WA between the auto-generated and a priori masks is very large. Therefore, generating an effective MFM under general conditions is difficult.

6

Conculusion

We integrated ICA and MFT-based ASR and generated an automatic MFM. The average word accuracy achieved with MFT-based ASR improved about 13 points for isolated word recognition and 5 points for continuous speech recognition. In the isolated word recognition, the automatically generated MFM for both the MSLS and the MFCC worked well, but even a priori mask did not work well in the continuous speech recognition. Because the large size of vocabulary means that the number of classes that should be recognized is very large, the possibility of false recognition caused by erros in the mask increases. In addition, since continuous speech recognition uses language constraints, we should also take into account this information in MFM generation. A more robust estimation of MFM and an improved MFT-based ASR are required for further investigation of the feature for speech recognition. In future work, we will generate generate more effective MFMs by adapting the distortion to the target speaker, and determining the best features for speech recognition.

References 1. Takeda, R., Yamamoto, S., Komatani, K., Ogata, T., Okuno, H.G.: Improving speech recognition of two simultaneous speech signals by integrating ica bss and automatic missing feature mask generation. In: Proceedings of International Conference on Spoken Language Processing (2006) 2. Kolossa, D., Klimas, A., Orglmeister, R.: Separation and robust recognition of noisy, convolutive speech mixtures using time-frequency masking and missing data techniques. In: IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, pp. 82–85 (2005) 3. Yamamoto, S., Valin, J.-M., Nakadai, K., Rouat, J., Michaud, F., Ogata, T., Okuno, H.G.: Enhanced robot speech recognition based on microphone array source separation and missing feature theory. In: Proceedings of IEEE International Conference on Intelligent Robots and Systems (2005)

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4. Palomaki, K.J., Brown, G.J., Barker, J.P.: Recognition of reverberant speech using full cepstral features and spectral missing data. In: Proceedings of International Conference on Acoustics, Speech and Signal Processing, pp. 289–292 (2006) 5. Choi, S., Amari, S., Cichocki, A., Liu, R.: Natural gradient learning with a nonholonomic constraint for blind deconvolution of multiple channels. In: Proceeding of International Workshop on ICA and BBS, pp. 371–376 (1999) 6. Sawada, H., Mukai, R., Araki, S., Makino, S.: Polar coordinate based nonlinear function for frequency-domain blind source separation. IEICE Trans. Fundamentals E86-A (3), 505–510 (2003) 7. Murata, N., Ikeda, S., Ziehe, A.: An approach to blind source separation based on temporal structure of speech signals. Neurocomputing 1–24 (2001) 8. Nishimura, Y., Shinozaki, T., Iwano, K., Furui, S.: Noise-robust speech recognition using multi-band spectral features. In: Proc. of 148th Acoustical Society of America Meetings (2004) 9. Cooke, M., Green, P., Josifovski, L., Vizinho, A.: Robust automatic speech recognition with missing and unreliable acoustic data. In: Speech Communication, vol. 34(3)

Knowledge Based Discovery in Systems Biology Using CF-Induction Andrei Doncescu1,3 , Katsumi Inoue1,2 , and Yoshitaka Yamamoto2 1

2

National Institute of Informatics 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan [email protected] Department of Informatics, Graduate University for Advanced Studies 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan [email protected] 3 LAAS-CNRS 31007 Toulouse, France [email protected]

Abstract. The cell is an entity composed of several thousand types of interacting proteins. Our goal is to comprehend the biological system using only the revelent information which means that we will be able to reduce or to indicate the main metabolites necessary to measure. In this paper, it is shown how the Artificial Intelligence description method functioning on the basis of Inductive Logic Programming can be used successfully to describe essential aspects of cellular regulation. The results obtained shows that the ILP tool CF-induction discovers the activities of enzymes on glycolyse metabolic pathway when only partial information about it has been used. This procedure is based on the filtering of the high processes to reduce the space search.

1

Introduction

Since the days of Norbert Wiener, the understanding of the systems at different levels has been a recurrent theme in biological science. The main interest to restart this kind of approach is due to the progress in molecular biology, particularly in genome sequencing. That was not possible in the days of Wiener when the molecular biology was an emerging field of research. Today’s advances in measurement, data acquisition and handling technologies provide a wealth of new data which can be used to improve existing models. Four properties give an insight into the understanding of a biological system: 1. 2. 3. 4.

System structure: network of gene expression and biochemical pathway System dynamics: the behavior of the system over the time The control method: minimize the one of desired function of the cells The design method: strategy to modify and construct biological systems having desired properties by finding the optimum conditions of cells development

H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 395–404, 2007. c Springer-Verlag Berlin Heidelberg 2007 

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The biological system studies in this article is the yeast Saccharomyces Cerevisiae. Yeast is one of the smallest eukaryotic systems sequenced that is unparallel for the level of molecular investigations accumulated and range of possible manipulations. It is an ideal target for comprehensive study at system-level.

2

Metabolic Pathways

Neuberg and his proposed scheme was the first researcher which introduced the notion of biochemical pathway [12]. But, it was far to be perfect. The boom of the fermentation is due to the discover of penicillin in the early 1940. With the application of continuous culture techniques, it became possible to study metabolism at defined physiological states. The cellular metabolism is defined as the huge set of biochemical reactions that occurs inside the living cell for growth and reproduction. In aerobic glucose-limited continuous cultures of S. cerevisiae, two distinct metabolic regimes are observed. At low dilution rate, which at steady state is equal to the specific growth rate, purely respiratory growth is observed. At a certain dilution rate, the critical dilution rate, aerobic fermentation sets in, and the metabolism is respiro-fermentative above this dilution rate. During respiratory growth the specific rate of CO2 production (rCO2) and O2 consumption (rO2) increase linearly with dilution rate and the respiratory quotient (RQ) is close to unity. Above the critical dilution rate, where the metabolism is respirofermentative, rCO2 increases sharply, whereas rO2 is constant or decreases and RQ is greater than one [12]. In this paper we investigate the metabolic pathway corresponding to the metabolism respiro-fermentative. In yeast fed-batch production is very important to recognize the physiological state of the micro-organism since reflects the activity of growth and ethanol production. The physiological states which could be identified on-line are :

Fig. 1. Metabolic Pathways

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oxidation, fermentation and the growth on two carbonate substrates glucose and ethanol. In the same time it is possible to trig the conditions unknown by the biologists which product a metabolite. This is possible in fed-batch productions due to the grate number of PID regulators involves in the control of: pH, temperature, oxygen. The main regulatory effects present inside the yeast are: 1. The Pasteur Effect: suppression of fermentative activity by respiration 2. The Crabtree effect: saturation of the respiratory system under excess of oxygen and sugar 3. The Glucose Effect: repression of uptake system at high concentration of glucose, which could be considered a particular case of Crabtree Effect. The Glucose Effect is studied in this paper using a logic description of the Metabolic Pathway Activity. In literature are different approaches for example Metabolic Flux Balancing. Metabolic Flux Balancing is one of the first approaches to determine the intracellular fluxes based on the measures of the extracellular fluxes. In fact it is necessary to measure the consummation of the substrate, the new products obtained like biomass and gas exchanges. However, this technique is not sufficient to determine all the intracellular fluxes of central metabolism without adding assumptions about the enzymatic activity and energetically yields which limit the validity of the results. The emerging field of data mining has recently attracted significant attention in metabolic analysis. This approach is interesting to determinate the metabolites tendencies but for the optimization of the system it is not suitable.

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Inductive Logic Programming: Background

An ILP system aims to output a rule that covers (entails) a set of positive observation or examples and does not cover a set of negative examples. In essence, ILP tries to synthesize a logic program, given background knowledge and examples which are described using logical clauses. A clause is a disjunction of literals, and is often denoted by the set of its disjuncts. A clause {A1 , . . . , Am , ¬B1 , . . . , ¬Bn }, where each Ai , Bj is an atom, is also written as B1 ∧ · · · ∧ Bn ⊃ A1 ∨ · · · ∨ Am . Any variable in a clause is assumed to be universally quantified at the front. A definite clause is a clause which contains only one positive literal. A positive (negative) clause is a clause whose disjuncts are all positive (negative) literals. A negative clause is often called an integrity constraint. A Horn clause is a definite clause or negative clause; otherwise it is non-Horn. The length of a clause is the number of literals it contains. A unit clause is a clause with the length 1. A clausal theory is a finite set of clauses. A clausal theory is full if it contains non-Horn clauses. On the other hand, a Horn program is a clausal theory containing Horn clauses only. Let B, E, and H be clausal theories, representing a background theory, (positive) examples, and a hypothesis, respectively. The most popular formalization

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of ILP is learning from entailment (or explanatory induction), in which the task is: given B and E, find H such that B ∧ H |= E and B ∧ H is consistent. On the other hand, in the case of abduction, E and H are usually called observations and an explanation, respectively, for the same task as induction.

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Integrating Induction and Abduction in CF-Induction

Both induction and abduction are ampliative reasoning, and agree with the logic to seek hypotheses which account for given observations or examples. That is, given a background theory B and observations (or positive examples) E, the task of induction and abduction is common in finding a hypothesis H such that B ∧ H |= E,

(1)

where B∧H is consistent. There are several discussions on the difference between abduction and induction in the philosophical and pragmatic levels. On the computational side, induction usually involves generalization, while abduction gives minimal explanations for individual observation. Inverse entailment (IE) is a logically principled way to compute abductive and inductive hypotheses H in (1) based on the logically equivalent transformation of the equation (1) to B ∧ ¬E |= ¬H. (2) The equation (2) says that, given B and E, any hypothesis H deductively follows from B ∧ ¬E in its negated form. The equation (2) is seen in literature, e.g., [4] for abduction and [8] for induction. The equation (2) is useful for computing abductive explanations of observations in abduction. This is because, without loss of generality, in abduction E is written as a ground atom, and each H is usually assumed to be a conjunction of literals. These conditions make abductive computation relatively easy, and consequence-finding algorithms [4] can be directly applied. In induction, however, E can be clauses and H is usually a general rule. Universally quantified rules for H cannot be easily obtained from the negation of consequences of B ∧ ¬E. Then, Muggleton [8] introduced a bridge formula U between B ∧ ¬E and ¬H: B ∧ ¬E |= U,

U |= ¬H.

As such a bridge formula U , Muggleton considers the conjunction of all unit clauses that are entailed by B ∧ ¬E. In this case, ¬U is a clause called the bottom clause ⊥(B, E). A hypothesis H is then constructed by generalizing a sub-clause of ⊥(B, E), i.e., H |= ⊥(B, E). This method with ⊥(B, E) is adopted in Progol, but it has turned out that it is incomplete for finding hypotheses satisfying (1). In [5], Inoue proposed a simple, yet powerful method to handle inverse entailment (2) for computing inductive hypotheses. The resulting method called CF-induction does not restrict the bridge formula U as the set of literals entailed

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by B ∧¬E, but consider the characteristic clauses [4] of B ∧¬E, which obviously generalizes the method of the bottom clause. CF-induction then realizes sound and complete hypothesis finding from full clausal theories, and not only definite clauses but also non-Horn clauses and integrity constraints can be constructed as H. In most previous inductive methods including Progol [8], there are syntactical restrictions such that: (i) each constructed hypothesis in H is usually assumed to be a single Horn clause, (ii) an example E is given as a single Horn clause, and (iii) a background theory B is a set of Horn or definite clauses. From the viewpoint of applications, these restrictions are due to the easiness for handling such formulas. An extension to multiple non-Horn clauses in B, E, and H is, however, useful in many applications. First, an extension allowing multiple clauses in either a hypothesis H or an observation E is essential in applications of abduction. In fact, recent work on abductive inference in metabolic pathways [13] uses an independent abductive procedure to obtain a set of literals that explain an observation. In general, there are multiple missing data to account for an observation. This abductive inference is independently computed in [13] not by Progol, and the inductive process for generalization takes place by Progol only after abductive hypotheses have been obtained. On the other hand, CF-induction can be used to compute abductive explanations simply by taking the bridge formula U as a deduced clause. CFinduction thus integrates induction and abduction from the viewpoint of inverse entailment through consequence-finding [5]. Second, an extension to non-Horn clauses in representation of B, E, H is also useful in many applications. For example, indefinite statements can be represented by disjunctions with more than one positive literals, and integrity constraints are usually represented as negative clauses. The clausal form is also useful to represent causality. For example, when we want to represent inhibition of reaction in a causal pathway network, positive and negative literals with the predicate like inhibited can be used in the premise of each causal rule, which results in a non-Horn clause (we will see an example afterwards). Again, the inductive machinery of CF-induction can handle all such extended classes. Third, introducing multiple, non-Horn clauses in a hypothesis H is an unifying extension that combines the first and second extensions. In this case, a hypothesis H forms a theory, which can also account for multiple observed data at once. In our application to metabolic pathways, given the background theory of network structures of a pathway and observations, we need a hypothesis H that explains the behavior of the metabolic system. In principle, such a hypothesis consists of multiple non-Horn clauses each of which represents a causal relation. CFinduction is thus particularly useful for this type of applications. Here the structure of representation is based on the specification of CFinduction program, which is compatible with the consequence-finding program SOLAR [10] and the TPTP format for theorem proving. SOLAR is a Java implementation of the tableaux variant of SOL resolution [4].

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For example, the input clauses can be described as input_clause(axiom1, bg, [-p(X), -q(X), r(X)]). input_clause(example1, obs, [r(a)]). production_field([predicates(pos_all), length < 3]). Here, axiom1 and example1 are ID names of clauses, and bg and obs represent background knowledge and observation, respectively. The axiom1 means p(x)∨q(x) ∨ r(x). Each clause is represented as a list of literals. The predicate production field indicates the production field of SOLAR, and this example allows it to generate consequences consisting of less than 2 positive literals. In this way, a production field can be used to specify an inductive bias in CFinduction. There is other meta information to control deduction in SOLAR such as the search strategy and the depth limit.In this case, CF-induction produces the abductive hypothesis: Hypotheses: [ [p(a)], [q(a)] ] The current CF-induction program has several generalizers, which, given a set T of clauses, produce a set S of clauses such that S |= T . These basic generalizers include anti-instantiation, reverse Skolemization, Plotkin’s least generalization, and dropping literals (see [5]). Glucose

Glucose-P Alcohol Pyruvate decarboxylase

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CF-Induction Reasoning About Metabolic Pathways

Cell activity is usually represented by an intrinsic network connecting involved biochemical species called metabolites. Pathways of the network are called metabolic pathways. We shall limit ourselves to simple metabolic pathways which are made up of sequences of mono-molecular enzyme-catalyzed reactions of the form Xs → Xp , where Xs is the concentration of the substrate and Xp is the product. Those reactions can be inhibited by the presence of other metabolites in the networks. Glycolysis is a metabolic pathway found universally in biological systems. It is the metabolic pathway which converts glucose via a series of reactions to 2

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molecules of pyruvate. As a result of these reactions, a small amount of ATP and NADH are produced. Most of the metabolic energy derived from glucose comes from the entry of pyruvate into the citric acid cycle and oxidative phosphorylation. These pathways occur under aerobic conditions. Under anaerobic conditions, pyruvate can be converted to lactate in muscle or ethanol in yeast. We first explain the metabolic pathway of Pyruvate represented by Fig. 2 using logical clauses. We notice that the branch point on the level of Acetaldehyde where respiration and fermentation are in competition using the same substrate is very important. The data from bioreactor allows us to consider that we are in a fermentation state, therefore an increasing of the ethanol concentration. In this case the input of our system is glucose and the concentrations are described by the next clauses: input_clause(e1,bg,[concentration(glucose,up)]). input_clause(e2,obs,[concentration(ethanol,up)]). input_clause(e3,obs,[concentration(pyruvate,up)]). The reactions associated with the metabolic pathway presented in the Fig. 2 are described by the next clauses : input_clause(reaction1,bg,[reaction(glucose,glucosep)]). input_clause(reaction2,bg,[reaction(glucosep,pyruvate)]). input_clause(reaction3,bg,[reaction(pyruvate,acetaldehide)]). input_clause(reaction4,bg,[reaction(acetaldehide,ethanol)]). input_clause(reaction5,bg,[reaction(acetaldehide,acetate)]). input_clause(reaction6,bg,[reaction(acetate,acetylcoa)]). input_clause(reaction7,bg,[reaction(pyruvate,acetylcoa)]). Based on biological knowledge and on our goal to discover the inhibitions in the case of ethanol production we have considered the next non-inhibition : input_clause(b2,bg,[-inhibited(pyruvate,ethanol)]). The clause above means that the only active pathway is from Pyruvate to Ethanol passing by the Acetaldehyde knot The causal rules that we have introduced contain the two clauses: input_clause(be0,bg,[-reaction(Y,X),-reaction(X,Z),inhibited(Y,X), -inhibited(X,Z),concentration(X,up)]). input_clause(be0,bg,[-reaction(Y,X),-reaction(X,Z),-inhibited(Y,X), -inhibited(X,Z),concentration(X,down)]).

which described from a very simpler manner the evolution of one metabolite when the reaction are inhibited or not. Using CF-induction with the generalizer dropping we have obtained the next result.

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B & H is consistent Hypotheses: [ [concentration(pyruvate, up), inhibited(pyruvate, acetylcoa), inhibited(pyruvate, acetaldehide), -concentration(glucose, up)] [concentration(pyruvate, up), -concentration(glucose, up)] [concentration(pyruvate, up), -concentration(glucosep, up), inhibited(glucose, glucosep), -concentration(glucose, up)] [concentration(pyruvate, up), -concentration(glucosep, up), -concentration(glucose, up)] [concentration(pyruvate, up), inhibited(pyruvate, acetylcoa), inhibited(pyruvate, acetaldehide), inhibited(acetaldehide, acetate), inhibited(acetaldehide, ethanol), -concentration(glucose, up)] [concentration(pyruvate, up), inhibited(pyruvate, acetylcoa), inhibited(pyruvate, acetaldehide), -concentration(acetaldehide, up), -concentration(glucose, up)] [concentration(ethanol, up), -concentration(glucose, up)] ]

the time for constructing CC = 3000 ms the time for generating Hypotheses = 150125 ms (the time for operating Dropping =150109 ms) (the time for consistency-checking =16 ms) Running time(ms) = 153125 ms. The most important result is that both reaction pyruvate-acetaldehyde and pyruvate-acetylCoA are not inhibited (activated) which confirms the biologists knowledge concerning this important knot: pyruvate. The only action which is not discover by CF-induction is between Acetyl CoA and Acetate. But, from the results concerning concentrations (Glucose-P, Pyruvate, Acetaldehyde, Ethanol) and from the experimental data concerning the production of Acetate we deduce that the pathway from Acetate to Acetyl CoA is inhibited. The last hypothesis is exactly the Crabtree effect presented in the introduction of this paper, which means that a strong concentration of pyruvate will increase the capacity of the microorganism to produce ethanol. From a general view point these results have indicated that CF-induction discovers the qualitative level of concentrations of metabolites and the inhibitions of the pyruvate metabolic pathway.

6

Conclusion

The challenge now is to transfer and adapt the developed methodology from technical to more complex biological systems. The logical models are the most

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precise representation of knowledge because they have a unique and objective interpretation and they do not permit any vague statement. In this paper, we have studied an architecture based on CF-induction for explanation of metabolic mechanisms. Given the background theory of the network structure of a pathway and observations, a set of non-Horn clauses constructed by CF-induction as a hypothesis can explain the behavior of the metabolic system. This approach is interesting to determinate not only the apparition of the inhibitions but also the prediction of the tendencies of different metabolites. The most important result is related to the pathway Pyruvate-Ethanol where the variation of the metabolite Acetaldehyde and the non-inhibitions PyruvateAcetaldehyde and Acetaldehyde-Pyruvate are discovered by CF-induction. At our knowledge is for the first time that an Intelligent System based on Inductive Logic Programming is able to explain the evolution of a metabolic pathway from an incomplete level of description related to essential aspects of cellular regulation.

Acknowledgement The authors want to thank to JSPS for financing of this work and particulary to Oliver Ray for his remarks and encouragements.

References 1. Breiman, L.: Bagging predictors. Machine Learning 26, 123–140 (1996) 2. Chassagnole, C., Rodrigues, J.C., Doncescu, A., Yang, L.T.: Differential evolutionary algorithms for in vivo dynamic analysis of glycolysis and pentose phosphate pathway in Escherichia Coli. In: Zomaya, A. (ed.) Parallel Computing in Bioinformatics and Computational Biology, Jossey-Bass an imprint of Wiley Book. ISBN 0-471-71848, -3 3. Doncescu, A., Yamamoto, Y., Inoue, K.: Biological Systems Analysis using Inductive Logic Programming. In: The 2007 IEEE International Symposium on Bioinformatics and Life Science Computing BLSC07, Niagara Fall, Ontario, Canada (2007) 4. Inoue, K.: Linear resolution for consequence finding. Artificial Intelligence 56, 301– 353 (1992) 5. Inoue, K.: Induction as consequence finding. Machine Learning 55, 109–135 (2004) 6. Juvan, P., Demsar, J., Shaulsky, G., Zupan, B.: GenePath: from mutation to genetic networks and back. Nucleic Acids Research 33, 749–752 (2005) 7. King, R., Whelan, K., Jones, F., Reiser, P., Bryant, C., Muggleton, S., Kell, D., Oliver, S.: Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427, 247–252 (2004) 8. Muggleton, S.: Inverse entailment and Progol. New Gen. Comput. 13, 245–862 (1995) 9. Muggleton, S., Firth, J.: CProgol4.4: a tutorial introduction. Report of Department of Computer Science, University of York 10. Nabeshima, H., Iwanuma, K., Inoue, K.: SOLAR: a consequence finding system for advanced reasoning. In: Mayer, M.C., Pirri, F. (eds.) TABLEAUX 2003. LNCS, vol. 2796, pp. 257–263. Springer, Heidelberg (2003)

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11. Schugerl, K., Bellgardt, K.H. (eds.): Bioreaction Engineering: Modeling and Control. Springer, Heidelberg (2000) 12. Stephanopoulos, G., Aristidou, A., Nielsen, J.: Metabolic engineering. Academic Press, London (1998) 13. Tamaddoni-Nezhad, A., Chaleil, R., Kakas, A., Muggleton, S.: Application of abductive ILP to learning metabolic network inhibition from temporal data. Machine Learning 64, 209–230 (2006)

Environment Recognition System for Biped Walking Robot Using Vision Based Sensor Fusion Tae-Koo Kang1 , Heejun Song2 , Dongwon Kim3 , and Gwi-Tae Park4 Department of Electrical Engineering, Korea University, 1, 5-ka, Anam-dong, Seongbuk-ku, Seoul 136-701, Korea {tkkang,nyaong7,upground,gtpark}@korea.ac.kr

Abstract. This paper addresses the method of environment recognition specialized for biped walking robot. Biped walking robot should have the ability to autonomously recognize its surrounding environment and make right decisions in corresponding to its situation. In the realization of the vision system for biped walking robot, two algorithms have been largely suggested, they are; object detection system with unknown objects, and obstacle recognition system. By using the techniques mentioned above, a biped walking robot becomes to be available to autonomously move and execute various user-assigned tasks in an unknown environment. From the results of experiments, the proposed environment recognition system can be said highly available to be applied to biped walking robot walking and operated in the real world.

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With the aid of development in biped walking robot technologies, it is expected that humanoid robots which are more human friendly and operated in real human living environments will appear in the near future. They will exist in various forms such as servant robots, cleaning robots or entertaining robots, etc. However, there are still remaining problems which keep biped walking robot technologies to be familiarized to human. First of all, most of the previous researches on biped walking robot have only focused on theoretical walking algorithm itself. Obviously, the ability of walking is the most fundamental function. However, humanoid robots which are able to walk only in previously known (programmed) environments could not possibly be utilized in real world situations. Therefore, algorithms enabling biped walking robot to autonomously determine its actions and paths in unknown environments should be developed in advance that humanoids serve human beings in real lives. So many researches are ongoing in moving object detection and obstacle recognition area. The most common method for detecting a moving object in an image is background subtraction in the literatures of vision systems[1]. In recent years, color-based object detection methods which use the characteristics of objects represented by color histograms have been widely used in wheeled and biped walking robot researches[2]. However, those are not appropriate methods for biped walking robots since the whole H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 405–414, 2007. c Springer-Verlag Berlin Heidelberg 2007 

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background moves with the target object when a robot walks unlike the cases of using fixed cameras. Moreover, it is needed to have the predetermined color information of target object to use color-based methods. Optical flow method is a commonly used technique for the motion area extraction in the mobile robot or vehicle[3]. It groups the optical flow vectors that are associated with the same motion or structure. So it can be used to detect independently moving object even in the presence of camera motion. The purpose of this paper is building a system for recognizing environments of robot and making appropriate decisions against the change of environments. In this paper, a method for moving object detection using clustering based optical flow is proposed. The obstacle recognition method is also developed to make a robot to be provided proper information to climb up and down or avoid obstacles. Unlike the detection process of moving object, those obstacles do not move. Consequently it is not possible to detect them by the method for moving object detection. To overcome the problem, hierarchical SVM(Support Vector Machines) is proposed to classify the obstacles such as walls, stairs or slopes. SVM tends to perform well when applied to data outside the training set[4]. Indeed, it has been reported that SVM-based approaches are able to significantly outperform competing methods in many applications. This paper is organized as follows. In Chapter 2, the proposed environment recognition system is introduced. More specifically, the concepts of moving object detection system and obstacle recognition system for a biped walking robot are illustrated. In Chapter 3, the results of experiments focusing on verifying the performances of the proposed system are presented. They are object detection tests and obstacle avoiding/climbing tests using the whole proposed system. Also, this chapter gives the ways of utilizing the proposed environment recognition system in biped walking robot. They are distance measuring to a moving object and adaptive walking trajectory planning for walking in unknown environments. Chapter 4 concludes the paper by presenting the contributions.

2

Environment Recognition System

The overall system is constructed as illustrated in Fig. 1. The system largely consists of two parts, a biped walking robot and a Host PC. Those are connected with a Bluetooth wireless communication module using RS-232C. Since the realization of a biped walking robot which autonomously moves in an unknown environment as well as executes assigned tasks by a user is the main purpose of the system, the robot is equipped with several kinds of sensors which gather the information of the surrounding environments. Consequently, the robot is basically designed to move autonomously without any control from the user by recognizing the environment using sensors. For instance, the robot can control its posture stability by itself, by using gyro sensors and actuators in the waist joints.

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Fig. 1. Overall system architecture

2.1

Vision System for Environment Recognition

In environment recognition system, environment conditions for a biped walking robot are classified into three categories: even or uneven surface, moving object and obstacles in a broad sense. Fig. 2 shows the image processing methods in the environment recognition system built for the biped walking robot. As shown in Fig. 2, this system is composed of two parts: moving object detection and obstacle recognition. In the moving object detection part, the clustering based optical flow method is proposed due to the always non-stationary background so that the background subtraction method detects the whole region of input image as foreground. Roughly speaking as shown in Fig. 2, the moving object detection part is composed of Optical flow, Feature mapping, FCM(Fuzzy C-Means). The proposed moving object detection method is illustrated more detail in Chapter 2.2. In the obstacle detection part, the environment recognition system provides not only the information of existence of objects and obstacles, but also the details of them. For instance, when the system detects a stairs in front of the robot, it estimates the distance to the stairs, the height and width of a stair so that the robot can calculate the corresponding motion trajectory to go up the stairs. The obstacle recognition process is composed of Adaboost[5], PCA(Principal Component Analysis)[6], and hierarchical SVM(Support Vector Machines). The proposed obstacle recognition method is illustrated in Chapter 2.3. 2.2

Moving Object Detection System

Optical Flow for Non-stationary Background. The optical flow is an efficient algorithm for extracting moving components or detection of moving objects in images. However, the original optical flow method is not applicable in case of

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Fig. 2. Vision system for environment recognitiion

biped walking robot, because the whole image varies so that all the points in an image have the optical flow components of velocities and directions. Therefore, moving objects as well as background are considered that they are moving in such case. To resolve this problem, a clustering based the optical flow method is proposed to eliminate background components by using fuzzy clustering method to the optical flow components. Even in case of a fixed camera, there are some extracted features to be detected. However, it can be empirically known that the components of a moving object and ones of background are possibly classified into different groups. Therefore, in case that an object is moving in an image, the whole set of extracted motion features from the optical flow field can be classified into two groups. However, a problem still remains, that is, how to determine which group consists of the object. To overcome this problem, it is assumed that the features in a moving object have a larger distribution than ones in the background. To divide the features of each group, FCM algorithm is applied to the set in the feature space[7]. In addition, it would not be necessary to divide the object and the background in case of non-moving object. Therefore, the distance between the centers belonging to each group is calculated by mahalanobis norm[8] and it is considered as one group when the distance is small enough. The detailed description of the procedure is given in Fig 3. By using the proposed method, moving objects in images can be detected in most cases. 2.3

Obstacle Recognition System

The whole procedure of obstacle recognition system is illustrated in the left column of Fig. 2. The obstacle recognition system classifies an obstacle which a robot faces while walking and determines the details of the obstacle so that the robot is enabled to autonomously determine its behavior and generate the appropriate trajectory. This must be a mandatory system to realize humanoid robots since the current walking robots are only possible to walk in pre-programmed known environments.

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Fig. 3. Procedure of the moving object detection

Obstacle Region Extraction. In the obstacle recognition system, boosted classifier used a set of Haar filters as features is used as an obstacle region extractor[9]. A weak classifier is not capable of detecting a rotated or translated input image. However, once a boosted classifier is generated, it is able to adaptively detect the obstacles even when they are rotated or translated. Feature Extraction in Obstacle Region. PCA is known as a useful technique to extract dominant features or reduce the dimensionality of large data sets in image processing and data mining and can also be used to find signals in noisy data in signal processing. In some cases, the dimension of the input is too large, but the components in the input are highly correlated (redundant), PCA is useful to reduce the dimension of the input. We extracted the PCs from the original image(320X240). Hence, the finally resulting arrays contain 50 or 25 dimensional data containing the principal information of the original input image. These arrays are split into training and test dataset and used to train and test using SVM. Obstacle Recognition. To recognize and classify the obstacles which a biped walking robot faces while walking, a hierarchical SVM is implemented to construct an efficient classifier. In this paper, we categorized obstacle into two groups, obstacles which a biped walking robot can climb up and ones which should be avoided. In addition, the former group can be classified into two groups again, slope and stairs, since the walking characteristics of them are quite different. Consequently, there exist at least four kinds of obstacles including an even surface. The use of the original SVM is not appropriate in this case. Therefore, a hierarchical SVM is proposed to classify a variety of obstacles, more than two kinds. The structure of the proposed hierarchical SVM is depicted in Fig. 4.When an obstacle is detected by the vision and ultrasonic sensors installed in the

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Fig. 4. Procedure of the obstacle recognition

robot, the input image is processed by the procedures represented in Fig. 4 in advance to being applied to SVM classifier. In the classification process, a SVM classifier trained to classify even surfaces and walls is applied to the extracted features of the input image at the first stage. It determines whether the robot can climb up the obstacle or not, and returns the possibility of climbing up to the robot by using the Bluetooth communication. Then the robot modifies its motion trajectory in a way of avoiding in case of walls or other unclimbable obstacles. If the obstacle is classified as climbable by the first SVM classifier, the features are applied to the SVM second classifier. It classifies the object into the categories of stairs or a slope. Then the recognition system determines the more detailed information such as the height and width of a stair and the inclining degree of a slope, according to the information obtained from infrared sensors. Also, the determined result is transmitted to the robot and the robot generates a corresponding trajectory to the obstacle.

3

Experimental Results

We evaluate the performances of the proposed moving object detection and obstacle recognition. The detailed results of the experiment are given as follows. 3.1

Moving Object Detection Experiment

The proposed moving object detection algorithm is evaluated with real-time video streams. There are 100 video streams with human moving used for the

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Fig. 5. Object detection results - Fixed camera(left), Camera in a biped robot(right)

test and each video stream consists of 600 frames. Final results of processing the clustering based optical flow method are presented in Fig. 5. The centers of moving human are marked with circles. From Fig. 5, it can be known that the proposed method effectively detects moving objects in both cases of fixed camera and moving camera. The evaluation of detection accuracy is given in Table 1. Table 1. Accuracy test results of the moving object detection Camera

Num features 200 Fixed 300 400 200 Moving 300 400 Average

Processing time (ms) 18.2 21.2 23.3 17.8 21.4 23.2 20.9

Accuracy 91.78 93.14 92.52 85.38 88.43 87.59 89.81

Considering the proposed algorithm is performed in every frame so that there is no possibility of being affected by occlusions or losing objects while detecting. In addition, the average processing time is 20.9 ms and the camera carries out to send images at the speed of 30 frames per second. Therefore, the proposed algorithm can be concluded to be effective for moving object detection system in biped robot walking. 3.2

Obstacle Recognition Experiment

The proposed obstacle recognition system is evaluated by applying 100 of 10 second video streams at the speed of 30 frames per second in each test category. (30000 frames in total) Table 2 gives the experimental result of the proposed system. The classification accuracy is measured by calculating the ratio of correct classification for the 30000 input images. From the evaluation in Table 2, the proposed obstacle recognition algorithm shows appropriate processing time, approximately 22 ms and it is enough to be

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Adaboost PCA Ada win size Num PC 25 25x25 50

25 30x30 50 Average

SVM Accuracy Processing Time SVM kernel Wall Slope Stairs (ms) linear 80.1 83.1 92.9 22.38 polynomial 85.2 83.4 95.5 22.76 RBF 88.1 87.2 97.6 23.21 linear 85.1 84.2 93.4 23.35 polynomial 86.2 85.9 95.7 24.12 RBF 87.3 86.2 97.8 24.06 linear 84.1 84.1 93.1 23.78 polynomial 86.1 85.9 95.5 24.35 RBF 87.6 86.6 97.8 24.89 linear 84.9 84.6 94.1 24.43 polynomial 86.8 87.2 95.9 25.32 RBF 88.4 86.7 98.1 25.47 85.8 85.4 95.6 22.16

Fig. 6. Environment recognition results and the corresponding robot walkings

carried out in real time with camera at the transmission speed of 30 frames per second. The results of accuracy tests show differences by the types of obstacles. In case of wall and slope, the region detection accuracy by Adaboost is relatively

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high. However, they also have high false alarm rate so that the total accuracies of both cases are about 85 percent, which is not satisfactory. On the contrary, the classification accuracy of stairs is approximately 10 percent higher than the ones of other obstacles. In addition, cases with larger window size, larger number of principle components and RBF kernel show little better results for the cases of all types of obstacles. Fig. 6 shows the obstacle recognition for a wall, a slope, stairs and corresponding actual robot walking respectively. The left of each figure shows the region detected by Adaboost algorithm and it is marked with a rectangle. The right of each figure shows the actual robot walking respectively.

4

Conclusion

We present the systems of environment recognition for biped walking robot. For the realization of humanoid robot, they are the mandatory conditions to make a robot autonomously recognize its surrounding environment and adaptively walk by generating its motion trajectories. Therefore, this paper has the meaning of developing aid technologies for biped robot walking control. The environment recognition system is realized by combining sensory data obtained from a walking robot including image data taken by a single camera. The problems in developing vision system in biped walking robot operated in a real world are derived from the fact that the condition for the vision system of a biped walking robot quite differs from the one of a fixed camera or a camera mounted on a wheeled robot. Moving object detection system by using modified optical flow method and obstacle recognition system by using a hierarchical Support Vector Machines are proposed in this paper. Those systems are realized and verified their effectiveness with a number of experiments by implementing them into a biped walking robot. Moreover, there is a need of developing a stand alone vision system which works in a biped walking robot system itself, because the proposed system totally depends on the Host PC.

References 1. Li, L., Huang, W., Yu-Hua Gu, I., Tian, Q.: Statistical Modeling of Complex Backgrounds for Foreground Object Detection. IEEE Transactions on Image Processing. 13, 1459–1472 (2004) 2. Fieguth, P., Terzopoulos, D.: Color-based Tracking of Heads and Other Mobile Objects at Video Frame Rates. In: Proceeding of IEEE Computer Vision and Pattern Recognition, pp. 21–27 (1997) 3. Lee, D.: The Optic Flow Field: The Foundation of Vision. Philosophical Transactions of the Royal Society of London. Series B. Biological Sciences 290, 169–178 (1980) 4. Muller, K., Mika, R., Ratsch, S., Tsuda, G., Scholkopf, K.: An Introduction to Kernel-Based Learning Algorithms. IEEE Transactions on Neural Networks 12, 181– 201 (2001) 5. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511–518 (2001)

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6. Jolliffe, I.T.: Principal Component Analysis. Springer, New-York (1986) 7. Cannon, R., Dave, L., Bezdek, J.V.: Efficient implementation of the fuzzy c-means clustering algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 8, 248–255 (1986) 8. Deer, P.J., Eklund, P.W.: A study of parameter values for a Mahalanobis Distance fuzzy classifier. Fuzzy Sets and Systems 137, 191–213 (2003) 9. Lienhart, R., Maydt, J.: An Extended Set of Haar-like Features for Rapid Object Detec-tion. In: Proceedings of IEEE International Conference on Image Processing, vol. 137, pp. 900–903 (2002)

Design of a SOA-Oriented E-Diagnostics System for Hydroelectric Generating Sets Liangliang Zhan1 , Yongchuan Zhang1 , Jianzhong Zhou1 , Yucheng Peng2 , and Zheng Li3 1

2

School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, P.R. China [email protected] School of Energy and Power Engineering, Huazhong University of Science and Technology 3 Harbin Electric Machinery Co., Ltd.

Abstract. In order to resolve existing problems such as low efficiency, high cost and lack of technical resource in current maintenance, it is necessary to realize remote diagnosis for hydroelectric generating sets (HGSs). In this work, basing on the Service-Oriented Architecture (SOA) and Web Services technology, a SOA-oriented E-diagnostics system for HGSs (HGS-SES) is proposed, the framework of HGS-SES is constructed, a layout of the system’s hardware settings is described, the key modules of the system and a specific diagnostic procedure are given. HGS-SES makes rapid and convenient information transmission for services function and diagnosis decision-making, develops a dynamic network diagnostic platform for HGSs, and has broad prospects for farther research. Keywords: SOA, Web Services, E-diagnostics system, Hydroelectric generating sets.

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Introduction

Along with continuous progress of Chinese hydropower technology,the development trend of hydroelectric generating sets is increasingly towards high head, high speed, high efficiency and big capacity. With improved accuracy, increased crew size and structure complexity in building HGS, the running of HGS is easy to cause more problems[1]. Once faults occur, because of advanced technology, complex construction, great overhaul workload, and more technology fields involved in HGS, the on-site diagnostic system and technical staffs in a hydropower plant can hardly resolved them independently. Experts of relevant fields are often to be resorted. In China, most experts of HGS are in universities, research institutes and hydro-generator manufactories. Because of geographical and trade 

This work is supported by the Important Project of National Natural Science Foundation of China (NSFC) (No.: 50539140), the Project of National Natural Science Foundation of China (NSFC) (No.:50579022), and the research funds of University and college PhD discipline of China (No.:20050487062).

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limitation, contact among them is very loose, which makes actual technical resource seriously short. At the same time, there is considerable potential resource which can’t be fully exploited. When more than one HGS fault occur, experts are weighed down with them. Considerably increasing financial expense on various aspects of hydropower plants, faults still can not be ruled out in time[2]. Thus, it is necessary to establish an internet-based remote fault diagnosis system. Up to now, many plants have their own HGS monitoring systems or fault diagnostic systems. In order to maximize using existing resources, improve diagnostic accuracy and reliability, and decrease HGS-SES developing costs, it is necessary to integrate the exiting systems of all hydropower plants. Since developers of those systems are different, development languages, deployed platforms and communication protocols are likely to be significant differences, which made most systems heterogeneous[3]. How to exchange and integrate information in a heterogeneous environment has become a key issue of integration. To fulfill the requirements of e-diagnostics system and remedy the shortcomings of existing diagnostic systems,an SOA-oriented E-diagnostics system for hydroelectric generating sets (HGS-SES)has been proposed in this paper.It is a new way to provide remote diagnostic services for HGS faults.

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Service-oriented architecture (SOA) is a way of designing a software system to provide services to either end-user applications or other services through published and discoverable interfaces. SOA is not a new notion; it is important at this time because of the emerging Web services technology. Web services can be seen as a new network distributed computing model for the Web[4].Its related technologies mainly include the Extensible Markup Language (XML), Simple Object Access Protocol (SOAP), Web Services Description Language (WSDL), Universal Description, Discovery and Integration (UDDI). Web services aim to provide efficient and seamless applications for all kinds of internet communication [5,6]. They are adaptive, self-describing, modular application procedures, Service Description Service Registry

Find WSDL+UDDI

Publish WSDL+UDDI

Service Service Requester

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Fig. 1. Architecture of Web Services

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Fig. 2. Sketch map of HGS-SES

which can be published, located, and dynamically invoked across Web[7]. As shown in Fig.1, the Web services framework describes the relation-ship between the three roles (service provider, service requester, and service agent) and the three operations (deploy, find out, and bind)[8]. Basing on SOA, the HGS-SES should provide an open architecture, allowing both the existing legacy applications and the new leading-edge applications to be rapidly and seamlessly integrated into the system. Consolidating the function of the participating, the system should be capable of providing integrated business services to various types of utility users, ranging from technical staffs in hydropower plants to experts on remote places. The relationship among different objects in HGS-SES is shown in Fig.2.

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Framework of HGS-SES

According to the targets of HGS-SES and combining running situation of hydropower plants, the framework of HGS-SES is shown in Fig.3, which consists of four main components, such as the internet UDDI registry node, the specialist or technical staff visit module, the on-site fault diagnosis subsystem (OFDS) of a hydropower plant and the remote fault diagnosis subsystem (RFDS) of remote diagnosis center. HGS-SES publishes its service on the internet UDDI registry node, which may facilitate users, such as hydropower plants and experts, to search and visit. Experts and technical staffs can use Web browser or client model to visit HGS-SES,which can increase visit flexibility. OFDS of hydropower plants is made up of the on-site data acquisition module (ODAM), the on-site diagnostic module (ODM), the local diagnostic databases (LDD) and the client module. ODAM and ODM are divided basing on different

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service objects. Through ODAM, ODM can activate corresponding application procedure and acquire HGS running data. RFDS of the remote diagnosis center is made up of the remote diagnosis integrated management module (RDIMM), the remote diagnostic database (RDD), the diagnosis center UDDI and the client module. The main function of RFDS is to provide the interface of HGS-SES, manage services registry of subsystems, and respond to users’ request. In HGS-SES, the relationship between the service requester and the service provider is often relative. For example,hydropower plant and remote diagnostic center can serve not only as service requesters, but also as service providers.

Experts &Technical Staffs Web Browser

Hydropower Plant (1,2,ĂĂn) Hydroelectric Generator Set (1,2,ĂĂ n) Data Analysis Component

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Fig. 3. Framework of HGS-SES

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Hardware Layout of HGS-SES

According to the framework of HGS-SES, the actual targets of fault diagnosis and the objective system condition, the system hardware layout is shown in Fig.4. Because the layout of hydropower plant is representative, only a generic model is given. In the hardware layout, the Web services interface proxies are placed in Web server, and services management modules are placed in corresponding application servers, such as local diagnostic services management in on-site diagnostic server (ODS), data acquisition management in on-site data acquisition server (ODAS), and remote fault diagnosis center services management in remote diagnosis server (RDS). The workstations in and out of subsystems access HGS-SES through Web services interface proxies, who can realize sharing Web services between the structure of browser/server (B/S) and client/server (C/S), and avoid repeated business logic development of the traditional functions of browser/client/server (B/C/S) structure. Firewalls isolated subsystems out of the world outside to protect system information and data security. In order to

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Fig. 4. Hardware layout of HGS-SES

facilitate managing the Web services registry of subsystems, a private UDDI server is placed in the RFDS. Through it, Web services registry, categorization and use can be achieved.

5 5.1

Key Modules and Technology of HGS-SES Services Management Module

Shown in Fig.6, services management module is designed as a common module which could be adopted by the remote diagnosis center services management, on-site diagnosis services management and on-site data acquisition services management. The main function of service management is to wrap application programs, integrate Web services according to users’ requests, and response them. The sub-module functions are described below: 1. SOAP interface is designed to communicate information with Web server; 2. Workflow engine automatically distributes the information from the soap interface to the corresponding Web service adapter according to business rules; 3. Transaction management engine acts as the coordinator of distributed transaction; 4. Web service adapter mainly provides an interface to different component technology, such as CORBA, RMI and DCOM, and so on; 5. Application integration console implements the configuration and management, and accomplishes the dynamic configuration of users, including workflow management, business management, application resource management and current interface management; 6. Monitor console manages and monitors Web services, including dynamic monitoring and management; 7. Information console provides data and module service for the transaction service console and monitor console, and accomplishes management and maintenance of information security lifecycle, including shared information management, shared module management and data operation management.

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General Framework of Web Services Management Module Web Services Application Integration Service Engine Application Integration Console

Web-Services Adapter

CORBA

Web-Services Adapter

RMI

Web-Services Adapter

DCOM

SOAP Interface

Monitoring Console Workflow Engine

Information Console

Transaction Management Engine

Fig. 5. General framework of Web Services management

5.2

Diagnosis Center UDDI

The structure of diagnosis center UDDI service is shown in Fig.7. Relative to the Internet UDDI, the diagnosis center UDDI is a private UDDI of HGS-SES.

On-site Data Acquisition Service of HGS 2

ĂĂ

On-site Data Acquisition Service of HGS n

Data Interactive Service

Data Analysis Service

Hydropower Plant n Service

Diagnosis Center Service

On-site Data Acquisition Service of HGS 1

Fault Diagnosis Service

Remote Diagnosis Service Hydropower Plant 1 Service

Hydropower Plant 2 Service

ĂĂ

Maintenance Decision Service

On-site Diagnosis Service

Staff Training Service Service Registry Database

Diagnosis Center UDDI Service

Integration Rule Base

Fig. 6. Structure of the diagnostic center UDDI

Orchestration engine is the core module of the Diagnosis center UDDI service [9], which can manage registry, modification and deletion of Web services, and also can search and call Web services according to users’ request and need. It connects to a service registry database and an integration rule base, shown in Fig.8. The service registry is a private UDDI registry designed to store the information of participating business services. The integration rule base is built to store the integration business logic, which specifies how to respond to each type of request. By separating such integration meta-data from the integration process, a generic orchestration engine can be built to drive the process. In response to each received request, the orchestration engine searches the integration rule base for the matched integration rule and then executes its specified integration steps.

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Orchestration Engine

Integration Rule Base

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Service Request

Service Registry

Fig. 7. Implementation of the orchestration engine

6

Faults Diagnosis Workflow

When a HGS fault occurs, first, the hydropower plant technical staffs adopt OFDS for diagnosis; then, set a request to the RFDS if OFDS can not solve the problem, with the assistance of RFDS to handle the fault. Fig.8 indicates an information interactive process among different parts of the system in a fault diagnosis. The specific steps are as follows: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.

When a fault occurs, ODAS acquires the error message from HGS; ODAS passes the error message to ODS; ODS searches LDD for the diagnostic solution based on the error message; The local technical staffs provide the diagnostic result and maintenance decision combining with ODS; If no diagnostic solution of the occurring fault is found locally, or the fault cannot be remedied, ODAS delivers the error message to RDS. The local technical staffs search LDD for the diagnostic solution based on the error message for ”self-diagnosis”; If there is an explicit solution, then deliver it to ODS. The local technical staffs maintain HGS basing on the solution from ODS; If the solution is correct, ODS saves it to LDD as a new case; If still no diagnostic solution is found, then request the RDS to notify experts and technical stuffs to make a date for the remote cooperative fault diagnosis; RDS requests experts and HGS maintenance staffs to attend the remote cooperative diagnosis; According to ideas of the remote cooperative diagnosis, RDS gives the diagnostic conclusion and maintenance decision, and returns them to ODS; The local technical staffs maintain the HGS according to the diagnostic conclusion and maintenance decision; If success, ODS saves it to LDD as a new case; ODS returns the main-tenancy information to RDS; According to the feedback information of ODS, RDS decides whether to save the diagnosis information to RDD as a new case.

If there are still problems in the HGS, according to the feedback information, the whole system will repeat step 11 to step 15 until a reasonable diagnostic result is given.

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HGS Experts 1. Acquire Fault Information 11. Request Cooperative Diagnosis

HGS LDD

4(8,13). Provide Diagnosis Conclusion and Maintenance Decision

5. Deliver Fault Information

ODS

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6. Search Fault Solution 16. Save the New Case

2. Deliver Fault Information

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9(14). Save the New Case

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RDD

11. Request Cooperative Diagnosis

7(12). Provide Diagnosis Conclusion and Maintenance Decision 10(15). Report the Diagnostic Status

HGS Maintenance Staffs

Internet

Fig. 8. Example of a fault diagnosis process

7

Summary and Conclusion

Aiming at problems existing in the fault diagnosis process of HGSs, this paper presents a Web services based SOA-oriented E-diagnostics system, discusses how to design and implement it in system architecture, hardware layout,key functional modules,fault diagnosis process and other aspects. In HGS-SES, SOAP is used as the basic messaging protocol, the remote diagnosis center serves as the institution of management and coordination, devices of hydropower plants, maintenance staffs and experts of HGS will be put together to provide a full range of services for fault diagnosis and decision making. With the constant improvement of Web services known as the 3rd revolution in the information technology, HGS-SES will have broad application prospects.

References 1. He, Y., Ren, J., Chen, W.: Remote condition monitoring, tracking analysis and fault diagnosis system for hydropower sets. JTsinghua Univ(Sci and Tech) 46(5), 629–632 (2006) 2. Zhu, J., Lai, X.: Design and implementation of an internet based remote monitoring and diagnosis system for hydroelectric generating set. Power System Technology 28(24), 5–9 (2004) 3. Yu, R., Ye, L., Zhang, Y.: Intelligent control-maintenance-technical management system(ICMMS) and its application in power system, Part One Ideology, Constitution and Characteristic. Automation of Electric Power Systems 23(23), 50–54 (1999) 4. World Wide Web Consortium, WSDL Web-service Description Language [Online]. Available: http://www.w3.org/TR/wsdl 5. Sham, P., Jenngs, M.: Protocol Engineering for Web Services Conversations. Engineering Applications of Atificial Intelligence 18(2), 237–254 (2005) 6. Lu, J., He, Y.: A web product data management system based on simple object access protocol. Journal of Chongqing University-Eng.Ed. 2(1), 57–62 (2003)

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7. Dustdar, S., Gall, H.: Software configuration, distribution, and deployment of WebServices. SEKE’02, July 15-19, 2002. Ischia, Italy, pp. 649–656 (2002) 8. Gottschalk, K., Graham, S., et al.: Introduction to Web services architecture. IBM Systems Journal 41(2), 12–18 (2002) 9. Zhu, J.: Web Services Provide the Power to Integrate. IEEE Power and Energy Magazine, 40–49 (November/December 2003)

A Systematic Layout Planning of Visualizing Devices on a Non-rectangular Plane by Genetic Heuristics Chir-Ho Chang1 and Jin-Ling Lin2 1 Department of Industrial Engineering Management, LungHwa University of Science and Technology, Taipei, Taiwan, 333, R.O.C [email protected] 2 Department of Information Management Shih Hsin University , Taipei, Taiwan, 116, R.O.C [email protected]

Abstract. As the new era of RFID (Radio Frequency Identification) has come, it makes the visualizing of a plane possible. The proposed research focuses on how to plan the locations of RFID readers in a non-rectangular plane such that regions with or without forbidden blocks can be fully monitored by using a well developed RFID system under the spending of a minimum cost. An algorithm constitutes of three phases is used to obtain the optimal devices’ layout planning. In the beginning, we scrutinize a non-rectangular plane and its forbidden blocks so that a general grid-scheme can be applied. Secondly, the linear programming (LP) approach is applied to decide the number of RFID readers needed. At last, a hybrid genetic algorithm (GA) is used to find the appropriate locations for these designated number of RFID readers. The overall cost of deploying RFID readers and the total monitored region of the proposed RFID system are recorded. They are two key performance indexes to evaluate the efficiency of the proposed method. Simulation results show the proposed method has high efficiency on dealing RFID readers’ planning problems in visualizing a non-rectangular plane. Keywords: RFID, Layout Planning, Genetic Algorithm, Heuristic Rules.

1 Introduction As the radio frequency identification (RFID) technology has recalled its second wind, most people realize the RFID era has finally arrived [1,5]. For warehouses, supermarkets, shopping malls, and a lot of planar spaces, users care not only about whether objects are inside or outside, but also where the exact locations of specific objects under monitored. Therefore, a fully visualized plane with the help of a systematic layout planning (SLP) RFID system becomes important in real world applications. A planar space to be monitored is not necessarily with a rectangular shape. Tracing the locations of interested objects more efficiently in a non-rectangular plane has practicable values. H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 424–433, 2007. © Springer-Verlag Berlin Heidelberg 2007

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The proposed research focuses on how to allocate RFID readers on a nonrectangular plane such that all selected regions of a plane can be monitored by the proposed SLP-RFID system with the spending of a minimum cost. The number of RFID readers and the monitored region of RFID readers are the major issues in the planning of such a plane to be visualized. As we know, linear programming (LP) in operations research has some advantages in solving optimal problems with linear constraints and linear objectives. And, many of us believe genetic algorithm (GA) work well in finding quasi-optimal solutions for non-linear and non-polynomial problems. Therefore, LP technology is used to find the optimal number of RFID readers, and then GA is applied to decide the locations of these RFID readers on a non-rectangular plane.

2 Problem Definition How to determine the locations of RFID readers in a non-rectangular plane such that some interested regions with or without the forbidden blocks of a planar space can be fully monitored was considered in this paper. A cost-saving SLP-RFID system was developed to achieve this goal. We deal with a non-rectangular shaped plane with some forbidden blocks scattered inside. In order to reduce the cost of building such an RFID system, we expect the number of required RFID readers the less the better. In the mean time, the total feasible region under surveillance by the SLP-RFID system can be as wide as possible. A mathematical model was built and two objectives were used in this study. More details are described as follows. 2.1 Assumptions and Inputs In order to describe our work clearly and present the proposed method concisely, several assumptions (reasonable enough) on the non-rectangular working plane, scattered forbidden blocks, and a SLP-RFID system are used. a)

b)

General plane with/without forbidden blocks o A forbidden block can occupy its area inside or outside a plane, but they are all required to remain inside a minimum rectangle which specifies the plane. o When a forbidden block stay inside a plane, it means the block is an intrinsically unfeasible region which is not necessary to be monitored by any means. o Overlapped forbidden blocks will be treated as a contiguous forbidden block. Theoretically, overlapping is not allowed for any two forbidden blocks. RFID system o The cost of building the proposed SLP-RFID system consists of purchasing RFID readers which are used to monitor the plane only. o For simplicity, only one single type of RFID reader is used in each run of the proposed system. The cost and the scanning region of a RFID reader remain the same throughout cases studied. Combinatorial tags, mixed

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brands, crossover bandwidths, and multiple detection frequency are not allowed in the proposed SLP-RFID system. Before using the proposed method to optimally allocate RFID readers, the following information is necessary a)

b)

ο The information of a plane ο The information of forbidden blocks inside the specified plane RFID readers ο The cost of a single RFID reader ο The radius of the detecting range of a RFID reader Plane and forbidden blocks

2.2 Problem Model The objectives of solving the proposed problem are of two-aspects. One of them is to minimize the total cost of this SLP-RFID system. And the other is to maximize the monitoring region of the SLP-RFID system for a number of given non-forbidden blocks inside the specified plane. The multiple objectives problem can be split into two single objective problems. And the result comes from the first problem, which minimizes cost of the SLP-RFID system, is used to solve the second problem. A corresponding mathematical model with the proposed method is shown in Table 1 and notations used are listed as followings, the ith RFID reader RRk: the center of ith RFID reader cRRi: dRR: the detecting diameter of the specified RFID reader rRR: the radius of a specified RFID reader’s detecting range lRR: the list of locations for RFID readers nRR: the number of RFID readers the ith forbidden block FAi: FAiLen: the length of the ith forbidden block FAiWid: the width of the ith forbidden block RFAi: the minimum rectangle which can contain a forbidden block i (FAi) nFA: the number of contiguous forbidden blocks the area of a forbidden block i aFAi: RP: the minimum rectangle which can contain the specified plane RPLen: the length of RP RPWid: the width of RP RP[i,j]: the basic element of RP in location (i,j) MR(listRR,Rect): a function to compute the area under surveillance. (The area was monitored by the proposed SLP-RFID system and only feasible region inside a rectangle plane Rect was counted.) FR(Rect): a function to compute the feasible region inside a rectangle plane Rect

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Table 1. Mathematic model of the proposed problem

Frist objective: minimize the cost of an RFID system Minimize: Cost = nRR Subject to: 2 nRR * π * ( rRR )

≥ RPLen

* RPWid

−

nFA

∑

aFA

i =1

i

Second objective: maximize monitoring region of RFID system MR ( lRR , RP ) Maximize: Monitoring

Subject to:

Re gion

=

FR ( RP )

* 100

RP ≥ 1

The function MR can be done by the procedure shown in Table 2. Table 3 shows the procedure of function FR. Table 2. The procedure of function MR

Step 1 monitoringRegion = 0; Step 2 for (i=0; i U nif orm[0, 1], then Tk -- Sol = NSol , and fSol = fNSol . Incrementk by 1, and compute Tk , the temperature at plateau k. If the stopping criterion is not satisfied, go to Step 5.

In Step 5, the acceptance of an uphill move is controlled by the probability exp( −Δ Tk ). The probability that an uphill move of size Δ is accepted diminishes as the temperature declines. For a fixed Tk , small uphill moves have higher probabilities of acceptance than large ones. In this implementation of SA, the temperature of the annealing process is decreased geometrically; that is, at plateau k, Tk = 0.9Tk−1 . The initial temperature T0 is a function of the probability of acceptance |Δ|

τ0 = e− T0 , where Δ is the average variation of the fitness of 100 randomly generated solutions. τ0 is, in turn, a function of the quality of the initial solution. τ0 = 0.2 when the initial solution is “good”, and τ0 = 0.6 otherwise. The initial solution is “good”if its percent deviation from a computed lower bound is less than 25%. Setting τ0 = 0.2 makes the algorithm very selective starting its first

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Table 1. First problem set Instance a1 a2 a3 a4 b1 b2 b3 b4 c1 c2 c3 c4

S |G| W (m) {P, M, T , XT } 7 1.0 {P, M, T , XT } 7 1.0 {P, M, T , XT } 7 1.0 {P, M, T , XT } 7 1.0 {P, M, T , XT } 11 1.6 {P, M, T , XT } 11 1.2 {P, M, T , XT } 11 1.6 {P, M, T , XT } 11 1.2 {XP, P, M, T , XT , XXT } 20 2.0 {XP, P, M, T , XT } 20 2.5 {XP, P, M, T , XT , XXT } 20 2.0 {XP, P, M, T , XT } 20 2.5

qs qP = 2, qM = 5, qT = 2, qXT = 1 qP = 10, qM = 25, qT = 10, qXT = qP = 20, qM = 50, qT = 20, qXT = qP = 10, qM = 25, qT = 10, qXT = qP = 6, qM = 6, qT = 2, qXT = 2 qP = 20, qM = 30, qT = 20, qXT = qP = 12, qM = 12, qT = 18, qXT = qP = 20, qM = 30, qT = 20, qXT = qXP = 18, qP = 30, qM = 12, qT = qXP = 24, qP = 24, qM = 42, qT = qXP = 18, qP = 30, qM = 12, qT = qXP = 24, qP = 24, qM = 42, qT =

5 30 15 5 18 15 18, qXT 24, qXT 18, qXT 24, qXT

= = = =

L∗ 1.40 7.00 22.00 11.00 4.00 26.67 25.5 37.34 6, qXXT = 6 99.00 6 90.00 12, qXXT = 12 136.5 12 100.8

Table 2. Computational Results for first problem set Instance a1 a2 a3 a4 b1 b2 b3 b4 c1 c2 c3 c4

L∗ 1.40 7.00 22.00 11.00 4.00 26.66 25.50 37.33 99.00 90.00 136.50 100.80

ΔSA ΔGA ΔGAn 3.57 3.57 3.57 1.07 1.07 1.07 0.68 0.45 0.11 0.91 0.68 0.45 1.88 2.50 1.87 0.50 0.87 0.50 6.18 2.05 0.29 1.92 1.72 1.58 4.95 3.03 2.42 5.81 2.66 0.69 4.29 2.96 2.96 6.67 2.77 2.38

iterations whereas setting τ0 = 0.6 makes the algorithm accept uphill moves more often during the first iterations. A neighbor is obtained by randomly moving a size s ∈ S from a random section i, i = 1, . . . , p in the current solution Sol to a different existing section j, j = 1, . . . , p, i = j while maintaining demand feasibility. Removing a size s from its section i creates a residual demand rs = hi ∗ Ois . Restoring demand feasibility without creating an additional section requires moving size s to a section j whose hj is a divider of rs . When such a section j is identified, its corresponding Ojs is updated; i.e., Ojs = Ojs + hrsj . If no such section exists, a different size from a different section is considered for a move. The size M of the plateau or of the neighborhood is set to 12 accepted solutions. The algorithm is stopped if the best current solution is not improved for 3 consecutive plateaus.

5

Computational Results

The purpose of the computational results is twofold. First, we evaluate the performance of GAn. Second, we assess the need for combining the COP and TDL problems into a single CT problem. GAn and the sequential TDL algorithm are coded in Fortran and run on a Pentium IV, 1.7 GHz and 256 Mb of RAM. The computational results for the instances of Table 1 are displayed in Table 2. Columns 1 and 2 display the instance and its corresponding optimal length. Columns 3 - 5 report the deviation from L∗ of the SA, GA and GAn solutions. ΔSA is computed based on the best solution obtained when replicating SA six times with each replication initiated with a randomly generated solution.

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S 4 3 4

|G| W (m) 21 1.5 21 1.5 18 1.1

qs L∗ h h l 10-20-20-10 144.00 5 30 12.00 6-12-12 78.00 5 30 12.00 6-24-24-12 87.8 5 20 8.00

Table 4. Computational results for the industrial problems Instance L∗ ΔSA ΔGA ΔGAn I1 144.00 0 0 5 78.00 5 7 11 I2 87.00 0 1 4 I3

Our experimentation shows that SA’s solution is not very sensitive to the initial solution. Yet, starting SA from a very bad solution will not lead to a global optimum. Furthermore, increasing GA’s population size does not necessarily improve the quality of GA’s solution. GA’s local optimum is generally obtained randomly during the initial population or is the result of crossover; i.e., it is the result of diversification rather than of intensification. Starting GA with a population whose individuals are of high quality leads to premature stagnation; hindering the escape from local optima. These conclusions highlight the need for diversification and intensification of the search as in GAn. The results of Column 4 are consistently better than those reported in Column 3, demonstrating the need for the intensification search. (Note that both GA and GAn were run with an identical population size and number of generations.) Second, we assess the performance of the proposed approach on real life industrial problems where the garments are a collection of non-convex irregular pieces. Problems I1 − I3, summarized in Table 3, were obtained from a mediumsized local apparel manufacturer along with the “adopted” cut order plan and the corresponding layout for each of its sections. Column 1 of Table 3 displays the instance number. Columns 2-4 indicate the number of ordered sizes n, the number of pieces per garment |G|, and the width of the fabric W. Columns 5 and 6 display respectively the ordered quantity per size qs , s ∈ S, and the best known solution L∗ . Finally, columns 7-9 display respectively h, h, and l the minimum and maximum ply height and the maximum length of a section. The computational results for the industrial instances are reported in Table 4. Column 2 indicates the best known length L∗ . Column 3 tallies ΔSA , the minimum percent improvement of total length when SA is replicated six times starting each replication with a random initial solution. Column 4 gives ΔGA , the percent improvement of total length when GA is applied without any intensification strategy. Finally, Column 5 displays ΔGAn , the percent improvement of the total length when GAn is applied. The results show that the solutions

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obtained by human experts/specialized software can be improved. They further demonstrate the need to combine COP and TDL into a single problem. Finally, they emphasize the role of hybridization in refining the search.

6

Conclusion

This paper tackles a real life problem that is of sizeable importance to the apparel manufacturing industries. This problem consists of finding the optimal cut order plan for a given order subject to the industrial set of constraints such as maximum and minimum ply height, maximum length of a section, etc. The problem is solved using a hybrid heuristic which diversifies the search by undertaking a global search via genetic algorithms and intensifies the search using local search via simulated annealing. The computational results highlight the need for hybridization, and assesses the percent improvements that industry can incur by adopting the proposed approach.

References 1. Blazewicz, J., Hawryluk, P., Walkowiak, R.: Using a tabu search approach for solving the two dimensional irregular cutting problem, Annals of Operations Research 41, 313–325 (1994) 2. Dowsland, K.A., Dowsland, W.: Solution approaches to irregular nesting problems. European Journal of Operational Research 84, 506–521 (1995) 3. Li, Z., Milenkovic, V.: Compaction and separation algorithms for non-convex polygons and their applications. European Journal of Operational Research 84, 539–561 (1995) 4. Dighe, R., Jakiela, M.J.: Solving pattern nesting problems with GA employing task decomposition and contact detection. Evolutionary Computation 3, 239–266 (1996) 5. ElOmri, M´ethode d’optimisation dans un contexte productique, Ph.D. Dissertation, Universit´e de Bordeaux1 (1992) 6. Hifi, M., M’Hallah, R.: A best-local position procedure-based heuristic for the twodimensional layout problem. Studia Informatica Universalis. International Journal on Informatics (Special Issue on Cutting, Packing and Knapsacking) 2(1), 33–56 (2002) 7. Jacobs-Blecha, C., Ammons, J.C., Schutte, A., Smith, T.: Cut order planning for apparel manufacturing. IIE Transactions 30, 79–90 (1996) 8. Jakobs, S.: On the genetic algorithms for the packing of polygons. European Journal of Operational Research 88, 165–181 (1996) 9. M’Hallah, R., Bouziri, A., Jilani, W.A.: Layout of Two Dimensional Shapes Using Genetic Algorithms. In: Del Pobil, A.P., Mira, J., Ali, M. (eds.) Lecture Notes in Artificial Intelligence, subseries of Lecture Notes in Computer Science, pp. 403–411 (2001) 10. Talbi, E.G.: A Taxonomy of Hybrid Metaheuristics. Journal of Heuristics 8, 541– 564 (2002)

Supervised Adaptive Control of Unknown Nonlinear Systems Using Fuzzily Blended Time-Varying Canonical Model Yau-Zen Chang1 and Zhi-Ren Tsai2 1

Department of Mechanical Engineering, Chang Gung University, Tao-Yuan 33302, Taiwan [email protected] 2 Department of Electrical Engineering, Chang Gung University, Tao-Yuan 33302, Taiwan [email protected]

Abstract. In spite of the prosperous literature in adaptive control, application of this promising control strategy has been restricted by the lack of assurance in closed-loop stability. This paper proposes an adaptive control architecture, which is augmented by a supervising controller, to enhance the robustness of an adaptive PID control system in the face of exaggerated variation in system parameters, disturbances, or parameter drift in the adaptation law. Importantly, the supervising controller is designed based on an on-line identified model in a fuzzily blended time-varying canonical form. This model largely simplified the identification process, and the design of both the supervising controller and the adaptation law. Numerical studies of the tracking control of an uncertain Duffing–Holmes system demonstrate the effectiveness of the proposed control strategy. Keywords: Supervised Control, Fuzzily Blended Time-Varying Canonical Model, On-line Identification.

1 Introduction The techniques of adaptive control and robust control have been under intensive investigation to account for plant variation and uncertainty in nonlinear systems. In the approach of adaptive control, the idea of combining system identification, either directly or indirectly, and modifying control parameters during on-line control course has been successfully applied to many nonlinear and time-varying plants. However, installation of practical adaptive control systems is rare due to the lack of guarantee in long-term closed-loop stability in the face of exaggerated variation in system parameters, disturbances, or parameter drift in the adaptation law. [1],[2] proposed a supervising controller to enhance the robustness of an adaptive controller. However, the controller is designed based on the assumption of complete knowledge in plant dynamics. Clearly, this assumption violates practical situations of H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 464–472, 2007. © Springer-Verlag Berlin Heidelberg 2007

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most nonlinear systems. In this paper, a supervising controller is designed based on an on-line identified fuzzy model of the plants under control, which are either unknown in system parameters or partially uncertain in system dynamics. The model used to identify the nonlinear and time-varying plants is in a fuzzily blended time-varying canonical form. As will be seen in the following derivation, this model largely simplified the identification process and the design of both the supervising controller and a PID adaptation law. Parameters in the model are identified on-line by the Simplex method [4]. The method is an efficient optimization strategy but strongly depends on initial conditions. This shortage can be alleviated by initializing the identified parameters with results of accurate off-line identification, specifically, the parallel genetic algorithms [3]. Furthermore, the adaptation law of the PID adaptive controller is designed using the modified projection method.

2 Problem Formulation The plants under investigation are unknown SISO nonlinear systems, which are disturbed and can be described in the following canonical form, S : ⎧ x1 = x2 ⎪ x = x 3 ⎪ 2 ⎪# S: ⎨ ⎪ x n −1 = xn ⎪ x n = f ( x ) + Δf (t ) + g ( x ) ⋅ u(t ) + w(t ) ⎪ ⎩ y = x1 ,

(1)

where x = [ x1 x2 ... xn ]T , the parameters of f (x ) and g (x) are not exactly known, and w(t ) is external disturbance. Besides, Δf (t ) denote unknown dynamic terms, which is bounded such that Δf ( t ) ≤ ΔfU ∈ R + with ΔfU being a known constant. The task is to design a robust adaptive controller that can drive the system output, y , to follow the output, r, of a reference model: y d = Ar ⋅ y d + r (t ) .

(2)

In the face of parameter variation, model uncertainty, and disturbances, we propose the following time-vary fuzzy canonical model, F , to identify the unknown plant. L F : x = h ⋅ ⎧⎨[a 1] ⋅ ⎡ x ⎤ + b ⋅ u + v ⎫⎬ + e (t ) , ∑ mod n i i i ⎢δ (t )⎥ i i =1 ⎣ ⎦ ⎩ ⎭

where δ (t ) = I c cos(t ) j , ∑ j j =0

L ⎧ ⎫ ⎡ x ⎤ emod (t ) = f ( x ) + Δf (t ) + g ( x ) ⋅ u + w − ∑ hi ⋅ ⎨[ai 1] ⋅ ⎢ + bi ⋅ u + vi ⎬ , ⎥ i =1 ⎣δ (t )⎦ ⎩ ⎭

(3)

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( n −1) E = [e e ... e( n −1) ]T = [( yd − y) ( yd − y ) ... ( yd − y ( n −1) )]T , and ( n −1) Yd = [ yd y d ... yd ]T .

The parameters of F , hi , ai , bi , vi , and c j , to be identified on-line were initialized by accurate off-line identification using the parallel genetic algorithm, a kind of evolutionary global optimization techniques. Started with accurate initial values, the parameters are constantly updated by the simplex algorithm [4]. Firstly, we define an ideal feedback control law, u * , as L

L

L

i =1

i =1

i =1

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(n)

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where the vector, K = [ k0

k1 ... kn −1 ]T ,

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is designed such that all roots of s n + kn −1 ⋅ s n −1 + ... + k0 = 0, are in the open left-half complex plane. We have the following inequality: L ⎡ L ⎤ (n) u * ≤ ( ∑ hi ⋅ bi ) −1 ⋅ ⎢ ∑ hi ⋅ ( ai ⋅ x + vi ) + δ + eU + y d + K T ⋅ E ⎥, i =1 ⎣ i =1 ⎦

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3 Supervisory Controller Design Next, let the control input be given by

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(7) de(t ) = θ T ⋅ ξ ( e) , dt

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de(t ) T . f θ = [ K P , K I , K D ]T , and ξ (e) = [e(t ), ∫ e(t ) ⋅ dt , ] uS is the extra supervisory 0 dt controller that is fired only when magnitude of the state vector exceeds some predefined bound. The approach guarantees stability of the system. Based on the concept of sliding mode control, an adaptation law using the simplex method is proposed below to minimize a sliding condition for updating the PID control gains. By substituting (7) into (3), we have, in view of (5), t

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= yd

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This implies that L

e ( n ) = − K T ⋅ E + ∑ hi ⋅ bi ⋅ (u * − u PID − u S ) .

(10)

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i

which is in companion form. From (10) and (11), we have E = Ac ⋅ E + Bc ⋅ (u * −u PID − u S ) .

(12)

Next, we define the Lyapunov function candidate VE = 2−1 ⋅ ( E T ⋅ P ⋅ E ) ,

(13)

where P is a positive and definite symmetric matrix which satisfies the Lyapunov equation Ac ⋅ P + P ⋅ Ac = −Q , T

(14)

where Q is also a positive definite symmetric matrix. In the following, we choose Q such that λmin (Q) > 0 , where λmin (Q ) denotes the minimum Eigen-value of Q . Furthermore, we define VM = 2−1 ⋅ λmin ( P) ⋅ (M x − Yd (t ) ∞ ) 2 .

(15)

Note that, if x ≥ M x ≥ Yd (t ) = supt ≥ 0 Yd (t ) , we have, from (13), ∞ 2 VE ≥ 2−1 ⋅ λmin ( P) ⋅ E ≥ 2−1 ⋅ λmin ( P) ⋅ ( x − Yd (t ) )2 ≥ 2−1 ⋅ λmin ( P) ⋅ (M x − Yd (t ) ∞ )2 = VM . (16)

we have that x < M x . Moreover, the derivative of VE along the trajectories of the closed-loop system (12) satisfies Hence, if

VE < VM ,

T VE = 2 −1 ⋅ [ E T ⋅ ( Ac ⋅ P + P ⋅ Ac ) ⋅ E + 2 ⋅ E T ⋅ P ⋅ Bc ⋅ (u * −u PID − u S )] .

(17)

By (14), we have VE = 2 −1 ⋅ [− E T ⋅ Q ⋅ E + 2 ⋅ E T ⋅ P ⋅ Bc ⋅ (u * −u PID − u S )] ≤ 2−1 ⋅ [− E T ⋅ Q ⋅ E + 2 ⋅ E T ⋅ P ⋅ Bc ⋅ ( u * + u PID ) − 2 ⋅ E T ⋅ P ⋅ Bc ⋅ uS ].

(18)

The supervisory controller is then designed, inferring (18) and (6), as L ⎧⎪ ⎡ L ⎤ ⎫⎪ , (n) u S = I * ⋅ sgn( E T ⋅ P ⋅ Bc ) ⋅ ⎨ u PID + ( ∑ hi ⋅ bi ) −1 ⋅ ⎢ ∑ hi ⋅ ( ai ⋅ x + vi ) + δ + eU + y d + K T ⋅ E ⎥ ⎬ ⎪⎩ i =1 ⎣ i =1 ⎦ ⎪⎭

(19)

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where the indicator function, I * , is defined as: I * = ⎧1 if VE ≥ VM . From (6) and (19), ⎨ ⎩0

if VE < VM

it is guaranteed, referring to [1] and [2], that VE < 0 if VE ≥ VM .

4 Adaptation Law with Projection for the PID Controller In order to derive the adaptation law for θ , we define the optimal parameter vector, θ * , by u PID * = θ *T ⋅ξ ,

(20)

such that the approximation error δu = u PI D * −u * ,

(21)

is minimized, where δu ≤ δu~ . Next, let us consider another Lyapunov function candidate, which contains tracking error and the deviation between θ * and θ : Vθ = 2VE + γ −1 ⋅ (θ * −θ )T ⋅ (θ * −θ ) = E T ⋅ P ⋅ E + γ −1 ⋅ (θ * −θ )T ⋅ (θ * −θ ) ,

(22)

where γ > 0 is the tuning rate which determines the convergence speed. By (17) and (22), we have Vθ = E T ⋅ P ⋅ E + E T ⋅ P ⋅ E − 2γ −1 ⋅ (θ * −θ )T ⋅ θ = − E T ⋅ Q ⋅ E − 2 E T ⋅ P ⋅ Bc ⋅ (u PI D + u S − u PID * +δu ) − 2γ −1 ⋅ (θ * −θ )T ⋅ θ

≤ − E T ⋅ Q ⋅ E − 2 E T ⋅ P ⋅ Bc ⋅ (u PI D − uPI D * +δu ) − 2γ −1 ⋅ (θ * −θ )T ⋅ θ = − E T ⋅ Q ⋅ E − 2 E T ⋅ P ⋅ Bc ⋅ [(θ − θ *)T ⋅ ξ + δu ] − 2γ −1 ⋅ (θ * −θ )T ⋅ θ = − E T ⋅ Q ⋅ E − 2 E T ⋅ P ⋅ Bc ⋅ (θ − θ *)T ⋅ ξ − 2γ −1 ⋅ (θ * −θ )T ⋅ θ − 2 E T ⋅ P ⋅ Bc ⋅ δu = − E T ⋅ Q ⋅ E + 2(θ * −θ )T ⋅ ξ ⋅ E T ⋅ P ⋅ Bc − 2γ −1 ⋅ (θ * −θ )T ⋅ θ − 2 E T ⋅ P ⋅ Bc ⋅ δu = − E T ⋅ Q ⋅ E − 2γ −1 ⋅ (θ * −θ )T ⋅ (θ − γ ⋅ E T ⋅ P ⋅ Bc ⋅ ξ ) − 2 E T ⋅ P ⋅ Bc ⋅ δu . (23)

Hence, if the adaptation law is defined as ⎧θ = γ ⋅ E T ⋅ P ⋅ Bc ⋅ ξ , if ( θ < M θ ) or ( θ = M θ and θT ⋅θ ≤ 0), ⎪ θ ⎨ T T T ⎪θ = γ ⋅ E ⋅ P ⋅ Bc ⋅ ξ − γ ⋅ E ⋅ P ⋅ Bc ⋅ M 2 ⋅θ ⋅ ξ , otherwise, θ ⎩

(24)

we have Vθ ≤ − E T ⋅ Q ⋅ E − 2 E T ⋅ P ⋅ Bc ⋅ δu .

(25)

Furthermore, by (25) and assuming ET ⋅ P ⋅ Bc ≤ e~ , we have: 2 e. Vθ ≤ − E T ⋅ Q ⋅ E + 2 E T ⋅ P ⋅ Bc ⋅ δu ≤ −λmin (Q ) ⋅ E + 2δu~ ⋅ ~

(26)

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This guarantees that Vθ < 0 if E > 2δu~ ⋅ ~e . It is clear that if (14) and (26) are satisfied, λmin (Q )

the system is UUB stable. Note that, if the initial PID control gains, θ , are not selected adequately, the resultant closed-loop system will exhibit overshooting transient behavior. In these circumstances, the supervisory controller, defined in (19), will play an important role that provides an extra input to pull the states back to the pre-specified state region and guarantees system stability. The overall block diagram of the augmented adaptive PID control system is depicted in Fig. 1. P

K

eU

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Fig. 1. The augmented adaptive PID control system

5 Case Study The system demonstrated is an uncertain Duffing–Holmes system with external disturbance, w(t ) , which can be described as follows: x1 (t ) = x2 (t ), ⎧ ⎪ x (t ) = x (t ) − 0.25 x (t ) − x 3 (t ) + Δf (t ) + w(t ) + u (t ), ⎪ 2 1 2 1 ⎨ y x1 , = ⎪ ⎪⎩ y d = Ar ⋅ yd + r (t ),

where Δf (t ) = 0.3 cos(t ) + 0.2ς (t ) ⋅ x ≤ 0.3 + 0.2 x ≡ ΔfU ; ς = [−1,1] ; w ∈ [−1.2,1.2] ;

⎡0

1⎤

. r (t ) = [0,2 cos(t )]T ; and Ar = ⎢ ⎥ ⎣− 1 − 2⎦

(27)

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During the preparation of data for off-line identification, the system is driven by white noise with magnitudes between [-15, 15]. Using the parallel genetic algorithms, we obtain the following time-varying fuzzy model of (27): THEN x2 = a1 ⋅ x + b1 ⋅ u + δ + v1 , Rule 1: IF x1 is about -1.5, Rule 2: IF x1 is about 0, THEN x2 = a2 ⋅ x + b2 ⋅ u + δ + v2 , THEN x2 = a3 ⋅ x + b3 ⋅ u + δ + v3 , Rule 3: IF x1 is about 1.5, where a1 = [-16.2688 - 0.2500] , a2 = [9.8556 - 0.2500] , a3 = [-16.2690 - 0.2500] , b1 = b2 = b3 = 1 , v1 = 2.9257 , v2 = 0 , v3 = -2.9257 , and δ = 0.298 cos( t ) .

Fig. 2 shows the three Gaussian membership functions used in the fuzzy model. 1

0.9

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Fig. 2. The Gaussian membership functions used in the fuzzy model, where Membership function 1 is for the antecedent part of Rule 1, Membership function 2 for Rule 2, and Membership function 3 for Rule 3

For the following study, we further define the constraint parameter, M x = 5 , and upper bounds of emod , eU = 0.02 . The initial control gains are selected as K P = 35 , K I = 45 , and K D = 35 . Fig. 3 shows the time history of the system output, y , when the control gains are fixed at these initial values. The simulation is based on initial states x1 (0) = 0.2 and x 2 ( 0) = 0.2 with the disturbance, w(t ) , being applied at t = 20 sec. Next, we choose K = [1 20]T , Q = ⎡2 0 ⎤ , and find the solution P = ⎡20.1 1 ⎤ to ⎢0 2 ⎥ ⎢ 1 0.1⎥ ⎣

⎦

⎣

⎦

construct the supervisory controller, u S . Fig. 4 shows the state trajectories of the plant,

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y , when both the adaptive law and the supervisory controller are applied. Fig. 5 shows the time history of the PID gains, demonstrating internal adaptation for the disturbance. Comparing Fig. 3 and Fig. 4, it is clear that the tracking performance of the augmented adaptive controller significantly outperform that of a constant gain PID controller. Also, the transient behavior during start and unexpected application of disturbance is bounded and acceptable.

Fig. 3. Comparison of the trajectories between the plant output, y (-.-), and the reference signal, yd (—), when a fixed-gain PID control law, K P = 35 , K I = 45 , and K D = 35 , is applied

Fig. 4. Comparison of the trajectories between the plant output, y (-.-), and the reference signal, yd (—), when both the adaptive law and the supervisory controller are applied

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6 Conclusion We propose new adaptive control architecture in this paper. The architecture is augmented by a supervising controller to enhance its robustness in the face of exaggerated variation in system parameters, disturbances, or parameter drift in the adaptation law. The supervising controller is designed based on an on-line identified model in a fuzzily blended time-varying canonical form. The identification strategy is the Simplex method initialized with other global optimization techniques, specifically, the parallel genetic algorithms. Numerical studies of the tracking control of an uncertain Duffing–Holmes system demonstrate the superior tracking performance of the proposed control strategy in the face of plant uncertainties and unexpected disturbances. Acknowledgments. The authors would like to thank the support of the National Science Council of Taiwan under contract NSC-95-2221-E-182-074 and Professor B.S. Chen for his valuable encouragement.

References 1. Wang, L.X.: Adaptive Fuzzy Systems and Control: Design and Stability Analysis. Prentice-Hall, New Jersey (1994) 2. Wang, L.X.: A Course in Fuzzy Systems and Control. Prentice-Hall, New Jersey (1997) 3. Chang, Y.Z., Chang, J., Huang, C.K.: Parallel Genetic Algorithms for a Neuro-control Problem. In: Int. Joint Conference on Neural Networks, pp. 10–16 (1999) 4. Bixby, R.E.: Implementing the Simplex Method: The Initial Basis. ORSA Journal on Computing 4(3), 267–284 (1992)

Multi-agent System with Hybrid Intelligence Using Neural Network and Fuzzy Inference Techniques Kevin I-Kai Wang, Waleed H. Abdulla, and Zoran Salcic Department of Electrical and Computer Engineering, University of Auckland. Private Bag 92019, Auckland, New Zealand {kevin.wang,w.abdulla,z.salcic}@auckland.ac.nz

Abstract. In this paper, a novel multi-agent control system incorporating hybrid intelligence and its physical testbed are presented. The physical testbed is equipped with a large number of embedded devices interconnected by three types of physical networks. It mimics a ubiquitous intelligent environment and allows real-time data collection and online system evaluation. Human control behaviours for different physical devices are analysed and classified into three categories. Physical devices are grouped based on their relevance and each group is assigned to a particular behaviour category. Each device group is independently modelled by either fuzzy inference or neural network agents according to the behaviour category. Comparative analysis shows that the proposed multi-agent control system with hybrid intelligence achieves significant improvement in control accuracy compared to other offline control systems.

1 Introduction With advanced embedded technology, computational devices ubiquitously merge with individuals’ daily activities. The demand for devices, applications, and processes to become more intelligent has increased dramatically due to insufficient availability of human supervisions and abundance of computational resources. Thus, intelligent systems are developed and are expected to be able to adapt, to predict and to have high level of autonomy [1]. More specifically, intelligent systems should be aware of the environment context, be able to model and adapt to user’s behaviour and respond on user’s behalf [2]. The MIT AI Lab started Intelligent Environment (IE) researches around mid 90s [3]. At that time, their research focus was to introduce intelligence via smart sensors and camera networks and can be considered as Human-Computer Interaction (HCI) and sensor network research. In 1999, a Multi-Agent System (MAS) called Metaglue which had no built-in intelligence, was developed to control the IE [4]. However, in the past few years, intelligent knowledge-based resource management [5] and reactive behaviour systems [6] had been developed and integrated into the MAS to introduce intelligence. The Adaptive Building Intelligence (ABI) project collaborated by several Swiss universities uses MAS approach to provide general building services rather than H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 473–482, 2007. © Springer-Verlag Berlin Heidelberg 2007

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personal space services [7]. The University of Essex focuses on online learning of personalised behaviour using Fuzzy Inference System (FIS) [8]. In University of Colorado, Artificial Neural Networks (ANNs) are used to control the lighting and heating services of a normal residential environment involving different types of living spaces such dining room, living room and bathroom [9]. There are also many other research efforts such as the Microsoft Smart House [10], IBM BlueSpace [11] and MASSIHN project [12]. However, most of them focus on integrating working behaviour and device automation into the control system. These control systems neither capture or model the human behaviour nor adapt to human needs, and do not reveal the true meaning of intelligence. In this paper, user control behaviours occurred on different physical devices are analysed. Based on the analysis, two soft computing techniques, neural network and Multi-Agent Fuzzy Inference System (MAFIS) are used to model different control behaviours. A novel Multi-Agent Neural Net Fuzzy Inference System (MANNFIS) is developed to merge the two techniques into one multi-agent control system. In addition to the high level software control system, a complete system architecture including middleware layer and underlying hardware infrastructure, has been proposed. A physical testbed named Distributed Embedded Intelligence Room (DEIR) has been constructed according to the system architecture to enable real-time system evaluation and data collection. The paper is laid out as follows. Section 2 introduces the physical infrastructure and system architecture of the testbed. Section 3 explains the soft computing techniques used and the rationales of using those techniques. Section 4 compares the performances of the proposed control system with other contemporary control systems. Section 5 gives ongoing research directions for the project and section 6 concludes the paper.

2 Distributed Embedded Intelligence Room (DEIR) Distributed Embedded Intelligence Room (DEIR) is the physical testbed designed by the Embedded Systems Research Group of the University of Auckland [13]. As shown in Fig. 1, DEIR resembles the target environment such as personal office, single studio accommodation, and convalescent nursing room. DEIR is equipped with

Fig. 1. Distributed Embedded Intelligence Room (DEIR)

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a number of embedded sensors including light intensity, temperature, pressure, smoke, and motion sensors to monitor the environment states. It also contains a number of actuators for automating windows, blinds, dimmable lights and house appliances. The underlying physical infrastructure is comprised of three different device networks for connecting various types of sensors and devices. Middleware is incorporated in the software counterpart of the devices, the device agents, to integrate hybrid physical networks and to improve system flexibility on future extensions. High level multi-agent control system communicates with the device agents through middleware channels to exchange context information and to issue control commands.

Fig. 2. DEIR system architecture

2.1 DEIR Physical Infrastructure Refer to Fig. 2, three physical networks namely IP network, LonWorks network [14] and RS-485 network are used to interconnect all the devices implemented in DEIR. Each network has its own protocol, control software and control hardware. In LonWorks network, all devices are connected to the iLon100 gateway server which communicates with the control software, the LNS server. In RS-485 network, devices are separately grouped and controlled by smart hardware switches. Each smart switch has a M16C microcontroller, a infra-red receiver, and a RS-485 network connection. The devices can be controlled through traditional switch interface, infra-red remote control and RS-485 network commands. All the smart switches are connected to another RS-485 gateway server to exchange RS-485 messages with the software device agents. Different to LonWorks and RS-485 networks, IP network is not solely used as a device network, but it is also used by the middleware to integrate other physical networks. The middleware layer allows new device networks to be added into the system architecture easily. Wireless device networks such as Zigbee and Bluetooth are possible extensions for the system.

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2.2 Middleware and Multi-agent Platform In DEIR, Universal Plug and Play (UPnP) is used as the middleware. The implementation of UPnP protocol can be considered in two parts: the control point and device agents. Control point agent is the server component which keeps all the information of registered device agents and acts as a software interface between high level control and device agents. Device agent is the client component which links the control point agent with corresponding network control software such as LNS server or sends out commands directly as in RS-485 network. The UPnP protocol is implemented using CyberGarage UPnP Java API [15]. In order to integrate all the software entities smoothly, a widely used and tested agent platform, JADE (Java Agent DEvelopment Framework), was selected [16]. Agents of JADE are developed using the provided Java API which enables UPnP components to be implemented as JADE agents directly. JADE also provide extensive plug-ins such as J2ME support, which helps in developing mobile agent applications.

3 Hybrid Learning Techniques The ultimate goal of the proposed multi-agent control system is to capture user habits and to provide autonomous monitoring and controlling services in ubiquitous intelligent spaces. In the previous multi-agent control system, Multi-Agent Fuzzy Inference System (MAFIS) was the only technique used to capture and to model user control behaviours [13]. It was found that MAFIS can not satisfactorily model all kinds of control behaviours. To improve control accuracy, another modelling technique, neural network, is introduced in the system to model part of the user behaviours. The control behaviours are analysed to justify the use of MAFIS and neural network and to assign each device with the most suitable modelling technique. The proposed multi-agent control system incorporating hybrid intelligence is referred as the Multi-Agent Neural Net Fuzzy Inference System (MANNFIS). 3.1 Device Behaviour Categorisation In order to select the optimal modelling technique, user control behaviours need to be categorised based on realistic data analysis. Based on the analysis, three behaviour categories are defined in MANNFIS. The first category involves common reactive control behaviours which rarely change among different users. This type of behaviour can easily be modelled using a traditional rule-based system. Security control such as autonomous door lock in absence of occupants is a good example. Due to the fact that this type of behaviour contains minimum uncertainties and the corresponding modelling technique requires no learning, they will not be included in the discussions. The second category involves daily routine behaviours that constantly follow certain environment context(s). This type of behaviour is typically user dependent but contains low uncertainties. In the past experiments, curtain devices show the exact characteristics of this category. Fig. 3 shows the curtain control behaviour collected

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over 3 consecutive days. It can be depicted that the curtain is turned on (i.e. it is closed) when external light intensity drops below five. Despite the fact that different users might control the curtain with different environmental parameters or parameter values, such personal preferences should not vary very often. Thus, a feed-forward

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neural network is a suitable tool to classify (or model) the device status according the relevant environmental parameters. The last category consists of behaviours that follow certain trend roughly but could be highly non-deterministic. Typical device that characterises this type of behaviour is the dimmable light as shown in Fig. 4. There is a rough trend to increase the light level when external light intensity drops such that the ambient light level is maintained. With great tolerance of uncertainties, MAFIS has been proved to handle this type of devices with great accuracy in the previous multi-agent control system [13]. In MANNFIS, devices are grouped with relevant environmental parameters to enhance the quality of model. Each device group is pre-assigned to a particular behaviour category according to the analysis. Based on the category, different modelling techniques are applied to different device groups. Detailed device groupings are shown in Fig. 5. The same groupings are used to execute the performance evaluation, which is discussed in Section 4.

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Fig. 5. Device Grouping in MANNFIS

Fig. 6. MANNFIS architecture

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3.2 Multi-Agent Neural Net Fuzzy Inference System (MANNFIS) As shown in Fig. 6, MANNFIS is a combination of neural network and MAFIS. The neural network implemented in MANNFIS is the most common feed-forward MultiLayer Perceptron (MLP) [17]. Numerical data that represents user control behaviours is used to train the neural network with back propagation algorithm. Detailed implementation can be found in [17]. MAFIS, on the other hand, was the first soft computing technique selected in DEIR project due to its great tolerance of uncertainties. The learning technique used in MAFIS is adopted from the Adaptive Online Fuzzy Inference System (AOFIS) developed at the University of Essex [8]. The numerical data are first processed by double clustering algorithm which generates fuzzy granules [18]. Fuzzy granules are then quantified using Gaussian membership functions. Eventually, fuzzy rules are extracted based on the input data and Gaussian membership functions, using the extended Mendel Wang’s method [19]. The implementation of MAFIS was provided in our previous publication [15].

4 Performance Analysis To examine the control accuracy of MANNFIS, a comparative analysis with other offline control systems has been performed. In order to conduct a fair comparison, the dataset provided by our colleagues at the University of Essex is used to evaluate all the control systems. The dataset used for the analysis contains seven input features, namely internal light sensor, external light sensor, internal temperature sensor, external temperature sensor, chair pressure sensor, bed pressure sensor and time; and ten output features including 4 dimmable lights, blinds, desk light, bed light, heater and two PC applications: MS Word and MS Media Player. This particular dataset contains 408 data instances collected over three consecutive days monitoring real user activities. The data instances are split into 272 data instances in the training set and 136 data instances in the testing set. Before evaluating the system performance, the main difference between MANNFIS and other control systems should be addressed. MANNFIS is a multi-agent based system rather than a centralised system as the others. Refer to Fig. 5, output devices are grouped with their relevant input devices where each device group can be modelled and controlled independently in parallel. The Scaled Root Mean Square Error (SRMSE) is used to measure the control accuracy. The traditional RMSE is scaled by the dynamic range of the output to take into consideration the different dynamic ranges of output devices. As shown in Fig. 7, both MLP and MAFIS perform well in modelling their assigned device groups. On the other hand, the computational efficiency (i.e. the execution time) for MLP is much higher compare to MAFIS due to two reasons. First, MAFIS is used to model control behaviours with high uncertainties, and usually works on a multi-dimensional feature space. Second, MAFIS requires fair amount of fuzzy sets to achieve good control accuracy. Nevertheless, by taking advantages of the multi-agent architecture, device groups can be further divided to make MAFIS more suitable for embedded applications. Fig. 8 shows the performance comparison with other control systems including AOFIS, Genetic Programming (GP), Adaptive Network-based Fuzzy Inference System (ANFIS), MLP and MAFIS. By introducing neural network into the

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control system, MANNFIS achieves up to 94% control accuracy, which is 15% improvements in control accuracy compared to its predecessor, MAFIS. It is also clear that MANNFIS outperforms other control systems by generating around 50% less control errors. Group 1

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Group 5

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MAFIS 13

MAFIS 15

MAFIS 20

MAFIS 15

MLP 1

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Fig. 7. Performance evaluation of MANNFIS MANNFIS (Average)

MAFIS (Average)

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0.0624

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Fig. 8. Offline system performance comparison

5 Future Works So far, the algorithms implemented in and the evaluation performed on MANNFIS and its predecessor MAFIS are all offline. The next step is to incorporate online adaptation ability in the algorithms and perform real-time data collection and system performance evaluation. With sufficient real-time data, a more thorough user control behaviour analysis could be carried out. Eventually, an algorithm should be developed to autonomously classify behaviours into different categories and to assign the most suitable modelling and controlling techniques.

6 Conclusions In this paper, a physical intelligent environment testbed, DEIR, and a multi-agent control system with hybrid intelligence, MANNFIS, are presented. DEIR consists of a large number of embedded devices, which are interconnected by hybrid physical networks. Extra layer of middleware is introduced in the system architecture to integrate hybrid physical networks and to improve system flexibility for future extension. DEIR resembles a true ubiquitous intelligent environment which allows real-time data collection and online system evaluation. MANNFIS is the top level control in DEIR system architecture, implemented using JADE agent platform. It employs two types of learning techniques, namely MAFIS and MLP, to model different types of human control behaviours. Devices to be controlled are grouped in terms of their relevance and modelled by either MAFIS or MLP at the same time. Performance analysis shows that both MAFIS and MLP achieve excellent accuracy in controlling the assigned device groups. The analysis also shows that MANNFIS outperforms other offline control systems by generating about 50% less control errors.

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Acknowledgement. This research is supported by UARC research grant 3604552 and top achiever doctoral scholarship. The authors would like to thank Dr. Faiyaz Doctor, Prof. Victor Callaghan and Prof. Hani Hagras for their kind contribution of providing the dataset for comparative analysis and various helps regarding to the use of their AOFIS learning technique.

References 1. Augusto, J.C., Nugent, C.D. (eds.): Designing Smart Homes. LNCS (LNAI), vol. 4008. Springer, Heidelberg (2006) 2. Ducatel, K., Bogdanowicz, M., Scapolo, F., Burgelman, J.-C.: Scenarios for Ambient Intelligence in 2010. Information Soc. Technol., Advisory Group (ISTAG), Inst. Prospective Technol. Studies (IPTS), Seville (2001) 3. Brooks, R.A.: The Intelligent Room Project. In: Second International Conference on Cognitive Technology, pp. 271–278 (1997) 4. Philips, B.A.: Metaglue: A Programming Language for Multi-Agent Systems. M.Eng. thesis, Massachusetts Institute of Technology, Cambridge, MA, USA (1999) 5. Gajos, K.: A Knowledge-Based Resource Management System For The Intelligent Room. M.Eng. thesis, Massachusetts Institute of Technology, Cambridge, MA, USA (2000) 6. Kulkarni, A.A.: A Reactive Behavioral System for the Intelligent Room. M.Eng. thesis, Massachusetts Institute of Technology, Cambridge, MA, USA (2002) 7. Rutishauser, U., Schaefer, A.: Adaptive Building Intelligence: A multi-Agent approach. Diploma thesis, University of Applied Science Rapperswil, Switzerland and Institute of Neuroinformatics, Swiss Federal Institute of Technology and University of Zurich, Switzerland (2002) 8. Doctor, F., Hagras, H., Callaghan, V.: A Fuzzy Embedded Agent-Based Approach for Realizing Ambient Intelligence in Intelligent Inhabited Environment. IEEE Trans. Sys. Man Cybern. 35(1), 55–65 (2005) 9. Mozer, M.: The neural network house: An environment that adapts to its inhabitants. In: Proc. Amer. Assoc. Artif. Intell. Spring Symp. Intell. Environ. pp. 110–114 (1998) 10. Brumitt, B., Cadiz, J.J.: Let There Be Light! Comparing Interfaces for Homes of the Future. Microsoft Research, Redmond, WA 98052, MSR-TR-2000-92 (2000) 11. Yoshihama, S., Chou, P., Wong, D.: Managing Behavior of Intelligent Environments. In: Proc. of the First IEEE Int. Conf. on Pervasive Comp. and Communications, pp. 330–337 (2003) 12. Tsai, C.F., Wu, H.C.: MASSIHN: A Multi-Agent Architecture for Intelligent Home Network Service. IEEE Trans. on Consumer Electronics 46, 505–514 (2002) 13. Wang, K.I., Abdulla, W.H., Salcic, Z.: Distributed Embedded Intelligence Room with Multi-agent Cooperative Learning. In: Ma, J., Jin, H., Yang, L.T., Tsai, J.J.-P. (eds.) UIC 2006. LNCS, vol. 4159, pp. 147–156. Springer, Heidelberg (2006) 14. Echelon Corporation, LonWorks Overview (February 2006) http://www.echelon.com/ solutions/overview/default.htm 15. Wang, K.I., Abdulla, W.H., Salcic, Z.: Multi-agent fuzzy inference control system for intelligent environments using JADE. In: Proc. of 2nd IET Int. Conf. on Intel. Environ. pp. 285–294 (2006) 16. Wang, K.I., Abdulla, W.H., Salcic, Z.: A Multi-Agent System for Intelligent Environments using JADE. In: IEE Int. Workshop on Intell. Environ. pp. 86–91 (2005)

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17. Kecman, V.: Learning and Soft Computing: Support Vector Machines. In: Neural Networks, and Fuzzy Logic Models, MIT Press, Cambridge (2001) 18. Castellano, G., Fanelli, A.M., Mencar, C.: Generation of interpretable fuzzy granules by a double clustering technique. Arch. Contr. Sci. 12(4), 397–410 (2002) 19. Wang, L.X.: The MW method completed: A flexible system approach to data minig. IEEE Trans. Fuzzy Syst. 11(6), 678–782 (2003)

Analysis of Log Files Applying Mining Techniques and Fuzzy Logic V´ıctor H. Escobar-Jeria1, Mar´ıa J. Mart´ın-Bautista2, , Daniel S´ anchez2 , 2 and Mar´ıa-Amparo Vila 1

2

Department of Informatics and Computer Science, Metropolitan Technological University of Santiago de Chile, Chile Department of Computer Science and Artificial Intelligence, University of Granada, Periodista Daniel Saucedo Aranda s/n, 18071, Granada, Spain [email protected]

Abstract. With the explosive growth of data available on the Internet, a recent area of investigation called Web Mining has arise. In this paper, we will study general aspects of this area, principally the process of Web Usage Mining where log files are analyzed. These files register the activity of the user when interact with the Web. In the Web Usage Mining, different techniques of mining to discover usage patterns from web data can be applied. We will also study applications of Fuzzy Logic in this area. Specially, we analyze fuzzy techniques such as fuzzy association rules or fuzzy clustering, featuring their functionality and advantages when examining a data set of logs from a web server. Finally, we give initial traces about the application of Fuzzy Logic to personalization and user profile construction. Keywords: Web Mining, Web Usage Mining, Fuzzy Logic, Fuzzy Association Rules, User Profiles.

1

Introduction

Today, the information is very important for companies, organizations and users, but it is more important that the information is precise, opportune and goodquality. With the development of technology, the growth of the information increases exponentially every day due to different factors; one of the most relevant is the Web. With the need to be able to manage huge quantities of data and discover new knowledge, a new area of study rises, the Knowledge Discovery in Databases or KDD. The main stage of KDD is the Data Mining process. Through mining techniques, non explicit knowledge can be found in data, usually stored in relational databases in a structured form [Agrawal et al., 1993]. Other research areas have raised with the coming of the Web and the explosion of documental information in companies and organizations. These areas are based on Data Mining, although the particularities of the data, specially the lack of structure, have 

Corresponding author.

H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 483–492, 2007. c Springer-Verlag Berlin Heidelberg 2007 

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implied specific features of the processes and techniques. These areas are called Text Mining and Web Mining, applied to documents and Web data, respectively. The investigation of the World Wide Web Web has been converted in one of the research fields further interesting in the latter time. As Kleinberg comments in [Kleinberg et al., 1999], few events of story of computing Web has had so much influence in the society like the arrival and growth of the World Wide Web. The Web grows and changes very rapidly, and its use has been extended not only to the information searching and retrieval but also to make commercial transactions. The competition in the e-commerce makes necessary the application of intelligent methods to store and to examine the information of sessions of Web users or potential customers. For this reason the user’s behavior and the user’s objectives are elements to obtain. The knowledge about the user is used not only to characterize to the user but also to discover generic tendencies for marketing purposes and for the web site improvement. These data about the users are collected from the activity of the user in the web site through log files. The objective is to mine web server logs to find relations among users about navigational aspects. The nature of the data in the log files and the information to predict such as time, user age, cultural level, etc. makes Fuzzy Logic a perfect tool to model this information. From all the techniques with Fuzzy Logic, we will extend our study in fuzzy association rules [Delgado et al., 2003]. Concretely, we present an application of fuzzy association rules in the area of Web Usage Mining. In this paper, we review some of the main applications of Fuzzy Logic to Web Mining. For this purpose, we first explain the three types of Web Mining: Web Content Mining, Web Structure Mining and Web Usage Mining. Then, we focus on Web Usage Mining including a study the personalization process and user profiles construction in Web. We overview the main applications of Fuzzy Logic found in the literature and present some preliminary experiments with Fuzzy Association Rules.

2

Web Mining

Nowadays, Internet and the Web play a very important role in the diffusion of information. Technological tools like the e-mail, the FTP, the e-business or simply reading the newspaper in the Web have meant a very important social change. However, there exist many challenges with respect to the obtaining of information from the Web. There are different situations that make difficult to get the needed information: the huge amount the data, the diversity of language, the quality of information, the distribution of data at different platforms and finally, one of the most important, the lack of structure in the data. These points, specially the unstructured data and the great heterogeneity, are also the main inconveniences of mining processes in the web. In these processes, data mining techniques are used to automatically discover and extract information from web documents and services [Etzioni, 1996].

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Cooley distinguishes three forms to understand Web Mining: from the point of view of the content, the structure and the use [Cooley et al., 1997]. Web Content Mining is the automatic discovery of patterns from the content in Web documents [Mitra and Pal., 2002],[Chakrabati, 2000] ; Web Structure Mining consists on studying the structures of link enter or intra documents to discover useful patterns of link structures [Chakrabati, 2000], [Delgado et al., 2002]; and finally, Web Usage Mining, that we will study in this article more detailed. We can define the Web Usage Mining as the process of automatic discovery of access patterns or use of web services, based on the user’s behavior when interact with the Web [Delgado et al., 2003]. At the following section we will focus on the study of the Web Usage Mining. 2.1

Web Usage Mining

At the present time, there is a lot of the sites dedicated to the e-commerce or to supply information. These sites need to learn every day about the clients or users that navigate through their pages so the services of the site can be improved. When the users interact with the Web, they leave digital tracks (IP, agents, cookies, etc) that the servers store automatically in a journal of accesses. This activity is principally stored in the log files of the web server, and it is usually completed with other sources of information such as the proxy server and the user’s machine. The log files contain information about the connection server or the user’s identity and authentication. This information collects the on-line user’s activities and reflects several kinds of behavioral different patterns. These navigational patterns can evidence user’s preferences, the on-line client’s behavior and the future directions of improvement of the web site. This information is used by the companies from the point of view of marketing, principally, where the assignment of a general profile to a user surfing in the web site based on his/her navigational behavior can be utilized to apply diverse measures, and to identify users with social groups. For the exploitation of different kinds of data, different techniques of pattern discovery can be used. The extracted knowledge can be used to execute tasks like prediction, personalization and improvement of the web site infrastructure. 2.2

Personalization and User Profiles in the Web

The ability of a web site to process the visits in a detailed level and to guide his/her clients or users through useful and pertinent information successfully, is becoming one of the crucial goals for any website nowadays. One of the forms to get this goal is through the personalization of Web. The personalization of Web can be seen from two points of view: the company and the user. The company’s point of view is related to the marketing and identification of demographic classes. The user’s point of view is related to navigational recommendations and obtaining of information. This process can be described as a group of actions done by the user in the navigation so that these

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actions can be processed to improve the web site according to user preferences [Mobasher, 2005]. Part of this information can be stored in what is called user profiles. User profiles can be defined as a representation of the knowledge about the user’s interesting information [Mart´ın-Bautista et al, 2002]. In [Mart´ın-Bautista et al, 2002] the authors propose two different types of profiles: the simple profiles, which are represented by data extracted from documents supposedly interesting for the user; and the extended profiles containing additional knowledge about the user such as the age, the language level, the country, among others. The extended profiles can be described then as a tuple of four variables. One of them is the demographic variable, that is related to social aspects like the user’s age range or the education level, among others. Other variable is the identification one, that is related to the information stored in the log files from the user server. Then, the clickstream variables which are related to the page weights, where if one page has a zero value, this implies that the page has not been visited. The last variable is related to the session simple profiles described previously [Mart´ın-Bautista et al, 2002]. For the obtaining of these profiles, clustering and association rules are applicable usually. Through clustering processes, an grouping of clients or data with similar characteristics is initially obtained automatically without having a previous classification. User profiles derived from these groups can be utilized to guide strategies of marketing according to the groups [Nasraoui et al., 1997]. The association rules discover associations and correlations among items where the presence of an item or a group of them in a transaction implies (with a confidence grade) the presence of other items [Carbonell et al., 1998]. One of the most direct applications of association rules to Web Usage Mining comes from the relations among visits of users with a certain navigational pattern to the web site. The principal inconvenience of handling of profiles in the Web is the lack of knowledge about the identity of the user. Two different situations can rise: first, the unregistered users where users’ profile can provide evidence of identity or associating with a social group. A general profile is then assigned to the user. Preferences stored in the profile can be applied to the web site for the user while she/he navigates. The second situation refers to the registered users. If a user is identified in some way, then the web site can change according to the user’s preferences. The system keeps the track of the user in her/his previous visits in the web site together with the users’ profile. This information is utilized to personalize the site. To characterize user groups with similar behavior a clustering method can be performed [Mart´ın-Bautista et al, 2002]. In order to have a more ample vision of Web Usage Mining and personalization, we will see some previous works in this area. 2.3

Related Work

In [Mobasher, 2005], an overview of the process of personalization based in Web Usage Mining is shown. Techniques of data mining such as clustering

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to discover groups of users are utilized. Furthermore, association rules can be used to find important relations among the articles the users are interested in, based on navigational site patterns. Other different proposal is found in [Mart´ın-Bautista et al, 2002], where the authors propose a scalable clustering methodology, inspired in the natural immunologic system with the facility to learn continuously and to adapt to coming new patterns. One of the most well-know system developed for personalization can be found in [Cooley et al., 1997]. The system called WebMiner is based on a behavior model of user’s navigation. By the grouping of web pages references, the system generate transactions, from which association rules are discovered. Other system related to personalization is presented in [Cernuzzi and Molas, 2004], where a study of Ridier’s Web site (http://www.rieder.net.py) is carried out. The log files of the web server are stored and analyzed. From the transactions, behavioral patterns are extracted to describe the users’ way of surfing using clustering and association rules. In [Wong et al., 2001] the authors propose a structure for a guided personalization and adaptation in the Web by means of user profiles and the accesses collected through the web log files. At the following section we will comment some proposals realized in this area connected with the Fuzzy Logic.

3

Different Aspects of Web Mining with Fuzzy Logic

In all these types of Web Mining as well as in traditional Data Mining, both from the data or from the technique point of view, optimization tools coming from Soft Computing have been applied such as Fuzzy Logic, Genetic Algorithms, Neural Networks and Rough Sets [Arotaritei and Mitra., 2004][H¨ ullermeier, 2005]. Fuzzy Logic helps to represent an user-oriented selection of data, giving flexibility to the system and producing more interpretable solutions [Mitra and Pal., 2002]. Recently, these techniques have been applied to other data areas such as document collections[Justicia et al., 2004] and the Web. In Web Mining, the main techniques utilized in the literature are Fuzzy Clustering and Fuzzy Association Rules. These are the techniques that we will use too to obtain generic navigational tendencies of the user and the construction user profiles. Fuzzy clustering algorithms such as the fuzzy c-means (FCM), with the fuzzyc trimmed medoids (FCTM), and fuzzy-c medians (FCLMedS) are used in [Mitra and Pal., 2002] for Web Content and Web Usage Mining. Another application with fuzzy clustering can be looked up in [Nasraoui et al., 1997] for Web Usage and Web Structure Mining. The authors apply an algorithm called CARD (Competitive Agglomeration of Relational Data) to group different sessions of users. For this purpose, not only the entries in log files are considered, but also the structure of the site and the URLs to calculate the similarity between two users’ sessions. The objective of this application is to identify users’ sessions from the users’ accesses to the Web site and its structure. Along with the fuzzy clustering, one of the techniques more utilized in Web Mining is fuzzy association rules. An application of this technique can be found

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in [Garofalakis et al, 1999], where a refinement of queries from an initial group of documents retrieved from Web is carried out. The textual transactions are constructed also with fuzzy values. The purpose of this work is to provide the system with an ability of reformulation of queries using mining technologies. Other approach using fuzzy association rules can be found in [Wong et al., 2001], where a proposal with a description of a system architecture to predict Web accesses is presented. The fuzzy association rules and the generation of a fuzzy index tree are utilized to improve the accuracy and the efficiency of predictions of access Web paths. 3.1

Fuzzy Logic and User Profiles

Fuzzy Logic [Zadeh, 1975] permits the manipulation and exploitation of incomplete data or with a grade of uncertainty, situation that is very frequent in the data to mine [Delgado et al., 2003]. Sometimes, we do not have explicit information of the users in log files besides the information got from the server. We can complete the user’s identity and authentication through another sources or inferring the information through techniques of mining. For example, we can infer in the education level of the user according to her/his navigation or may be according to the information that the users proportionate explicitly. Therefore, when the extended user profiles are constructed, there is information to manage related to different concepts about the user. Some of these concepts such as the age of the user are imprecise, since the system must approximate the data if the user does not proportionate it, or the patience of the user surfing through the site. These characteristics can be modeled by means of linguistic labels, for example {very low, low, regular, high, very high} [Mart´ın-Bautista et al, 2002]. We have seen different aspects and proposals realized in the area of Web Usage Mining, principally association rules and clustering techniques. Our investigation is based in these techniques with fuzzy logic and thus obtaining results more meaningful. So, fuzzy association rules permit to us to discover rules related with the navigational behavior of the user. At the following section, we will comment the Fuzzy Association Rules and experiments related with this technique. 3.2

Fuzzy Association Rules

The Association Rules look for relations or affinities among of groups of items or fields, generally in a relational database. Let I be a set of elements called “items” and let T be a set of elements called “transactions”, each transaction being a set of items. Let us consider two itemsets (sets of items) I1 , I2 ⊆ I, where I1 ∩ I2 = ∅. An association rule [Agrawal et al., 1993] I1 ⇒ I2 is an implication rule meaning that the apparition of itemset I 1 in a transaction implies the apparition of itemset I 2 in the same transaction. The reciprocal does not have to happen necessarily [Kraft et al., 2003]. I 1 and I 2 are called antecedent and

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consequent of the rule, respectively. The measures more utilized to describe the relations among antecedent and consequent of the association rules are the Support, and the Confidence. Support is the percentage of transactions where the rule holds, while confidence measures the strength of the rule as the percentage of transactions containing I1 , that contain I2 . Several authors have proposed fuzzy association rules as a generalization of association rules when data is fuzzy or has been previously fuzzyfied [Lee and Kwang, 1997],[Au and Chan, 1998],[Kuok et al., 1998], [Hong et al., 1999],[Delgado et al., 2003]. Fuzzy association rules can be extracted from a group of fuzzy transactions by means of an algorithm of extraction such as the algorithm APrioriTID [Agrawal et al., 1993]. A fuzzy transaction can be defined as a nonempty subset where τ˜ ⊆ I. For ˜ the membership degree of i in a fuzzy transaction τ˜ every i ∈ I we note τ (i) ˜0 ) the degree of inclusion of an itemset I0 ⊆ I in a [Etzioni, 1996]. We noteτ (I fuzzy transaction τ˜, defined in (1): τ˜(I0 ) = min τ˜(i)

(1)

i∈I

Therefore, fuzzy transactions manage imprecision and give more flexibility because they allow us to deal with intermediate values between 0 and 1 to represent the membership degree of the items to the transaction. For the evaluation of the performance of the association rules, we employ a semantic approach based on the evaluation of quantified sentenced [Zadeh, 1975]. A quantified sentence is an expression of the form ”Q of F are G” where F and G two fuzzy subsets of a finite set X, and Q is a relative fuzzy quantifier. Relative quantifiers are linguistic labels that can be represented by means or fuzzy sets on [0,1], such as ”most”, ”almost”, or ”many”. This way we can define the estimated measures of goodness of rules. Let us remark that families of support and confidence measures can be obtained depending on the evaluation method and the quantifier of our choice. We evaluate the sentences by means of the method GD [Delgado et al., 2000], which has been shown to verify good properties with better performance than others. The evaluation of ”Q of F are G” by means of GD is defined in (4), GDQ (

G )= F

 αi Δ( G F )

(αi − αi+1 )Q(

| (G ∪ F )αi | ) Fαi

(2)

Another interesting measure is the certainty factor of a fuzzy association rule [Delgado et al., 2000]. Given a A → C , the certainty factor takes values in [1, 1]. It is positive when the dependence between A and C is positive, 0 when there is independence and a negative value when the dependence is negative. We say that a fuzzy association rule is strong when its certainty factor and support are greater than two user-defined thresholds minCF and minSupp, respectively.

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Implementation and Experimentation

Once examined all the related issues with the web usage mining, which is the principal base for our implementation, we will explain briefly our application for the search of patterns through fuzzy association rules. The data to analyze is a subset of log files available for the ECML/PKDD Conference 2005 [ECML/PKDD 2005]. These files are in a CSV (Comma Separated Value) format. Table 1 shows a line of one of these log files, which is composed by 6 fields (identifier of shopping, date, IP, session, visited page, referenced page). Table 1. Entry line in a log file sample Id shop Date IP 11 Tue Jan 20 19:00:132004 213.235.141.105 Session Visited Page Referenced Page 1f75ccd2afbf87dc9abccde23f3 /dt/?c=11670 http://www.shop2.cz/ls/index.php

Once we have the data set for the analysis in a transactional form, we can decide which type of information can be obtained, based on the fields chosen to take part of the rule. For example, if the user chooses the fields of date and visited pages, the extracted knowledge can give us an idea about which pages have been more visited at certain hours. Also, if the user selects the fields of IP and visited pages, we could somehow identify the users that visit those pages. In order to be able to obtain all this information from the Web log files, we extract the association rules with the algorithm APrioriTID [Agrawal et al., 1993]. Any other Apriori like algorithm can be used, although we have chosen the AprioriTID for its capability of reducing the number of groups considered. We present an example of results that we can obtain to know the page that the users visit starting from an initial visited page. The form of the rules to extract would be: Initial visited page −→ Referenced page 1. dt/?c=11670 −→ http://www.shop2.cz/ls/index.php – Support = 0.6 – Confidence = 1.0 – FC = 1.0 2. dt/?c=12397 −→ http://www.shop7.cz/akce/kat=239 – Support = 0.2 – Confidence = 1.0 – FC = 1.0 These two rules have been extracted from a small set of transactions where rule 1 appears with a percentage of 60% and rule 2 appears with a percentage of 20%. In both cases, the confidence and the certainty factor are 1, which means

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that the users visiting the page in the antecedent also visit the page in the consequent. The results coming from these processes must be easy to interpret, since generally, the marketing purpose of this kind of applications implies that the final user is not very related to computing.

4

Conclusions and Future Work

In this article we have reviewed the main aspect of the Web Mining focusing in the Web Usage Mining area, including a short state of the art. We have also shown an application of fuzzy logic, where we analyze the information of the log files of Web server applying fuzzy association rules. Other aspect important in the article is the personalization, where the user behavior is modeled by means of a profile, where most of the elements are imprecise by nature. The future work will be go further in this area principally, continuing with the development of a tool that allows us to integrate other techniques of mining such as clustering and/or fuzzy clustering.

References Agrawal et al., 1993. Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993, ACM SIGMOD Conference, pp. 207–216 (1993) Arotaritei and Mitra., 2004. Arotaritei, D., Mitra, S.: Web Mining: a survey in the fuzzy framework. Fuzzy Sets and Systems (2000) Au and Chan, 1998. Au, W.H., Chan, K.C.C.: An effective algorithm for discovering fuzzy rules in relational databases. In: Proc. Of IEEE International Conference on Fuzzy Systems, vol. II, pp. 1314–1319 (1998) Carbonell et al., 1998. Carbonell, J., Carven, M., Fienberg, S., Mitchell, T., Yang, Y.: Report on the conald workshop on learning from text and the web. In: CONALD Workshop on Learning from Text and The Web (June 1998) Cernuzzi and Molas, 2004. Cernuzzi, L., Molas, M.L.: Integrando diferentes T´ecnicas de Data Mining en procesos de Web Usage Mining (2003) Cooley et al., 1997. Cooley, R., Mobasher, B., Srivastava, J.: Web mining: Grouping Web Page References into Transactions for Mining World Wide Web Browsing Patterns, pp. 1–11 (2000) Chakrabati, 2000. Chakrabati, S.: Data Mining for hypertext: A tutorial survey. ACM SIGKDD Explorations 1(2), 1–11 (2000) Delgado et al., 2000. Delgado, M., S´ anchez, D., Vila, M.A.: Fuzzy cardinality based evaluation of quantified sentences. Int. J. Aprox.Reasoning 3, 23 (2000) Delgado et al., 2002. Delgado, M., Mart´ın-Bautista, M.J., S´ anchez, D., Vila, M.A.: Mining Text Data: Special Features and Patterns. Pattern Detection and Discovery. In: Hand, D.J., Adams, N., Bolton, R. (eds.) Proceedings ESF Exploratory Workshop. Lecture Notes in Artificial Intelligence Series, pp. 140–153 (2002) Delgado et al., 2003. Delgado, M., Mar´ın, N., S´ anchez, D., Vila, M.A.: Fuzzy Association Rules: General Model and Applications. IEEE Transactions on Fuzzy Systems 11, 214–225 (2003)

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ECML/PKDD 2005. ECML/PKDD Conference 2005, Web Site. Porto, Portugal (2005) http://ecmlpkdd05.liacc.up.pt/ Etzioni, 1996. Etzioni, O.: The World Wide Web: Quagmire or gold mine. Comunications of the ACM 39, 65–68 (1996) Garofalakis et al, 1999. Garofalakis, M.N., Rastogi, R., Seshadri, S., Shim, K.: Data Mining and the web: Past, present nad future. In: WorkShop on Web Information and Data Managament, pp. 43–47 (1999) Hong et al., 1999. Hong, T.P., Kuo, C.S., Chi, S.C.: Mining association rules from quantitative data. Intelligent Data Analysis 3, 363–376 (1999) H¨ ullermeier, 2005. H¨ ullermeier, E.: Fuzzy methods in machine learning and data mining: Status and prospects. Fuzzy Sets and Systems 156(3), 387–406 (2005) Justicia et al., 2004. Justicia, C., Mart´ın-Bautista, M. J., S´ anchez, D.: Miner´ıa de textos: Aplicaciones con l´ ogica difusa. Actas del Congreso Espa˜ nol de Tecnolog´ıas con L´ ogica Difusa, Ja´en (In Spanish) (2004) Kleinberg et al., 1999. Kleinberg, J.M., Kumar, R., Raghavan, P.: The Web as a graph: measurements, models, and methods. In: Proceedings of the Fifth Annual International Computing and Combinatorics Conference (1999) Kraft et al., 2003. Kraft, D.H., Mart´ın-Bautista, M.J., Chen, J., Vila, M.A: Rules and fuzzy rules in text: concept, extraction and usage. International Journal of Approximate Reasoning 34, 145–161 (2003) Kuok et al., 1998. Kuok, C.-M., Fu, A., Wong, M.H.: Mining fuzzy association rules in databases. SIGMOD Record 27(1), 41–46 (1998) Lee and Kwang, 1997. Lee, J.H., Kwang, H.L.: An extension of association rules using fuzzy sets. In: Proc. of IFSA’97, Prague, Czech Republic (1997) Mart´ın-Bautista et al, 2002. Mart´ın-Bautista, M.J., Kraft, D.H., Vila, M.A., Chen, J., Cruz, J.: User profiles and fuzzy logic for Web retrieval issues. Soft Computing Journal 6(5), 365–372 (2004) Mitra and Pal., 2002. Mitra, S., Pal, S.K: Data Mining in Soft Computing Framework: A Survey. IEEE Transactions on Neural Networks, 3–14 (2002) Mobasher, 2005. Mobasher, B.: Web Usage Mining and Personalization. In: Singh, M.P. (ed.) Practical Handbook of Internet Computing, CRC Press, Boca Raton (2005) Nasraoui et al., 1997. Nasraoui, O., Frigui, H., Joshi, A., Krishnappuram, R.: Mining Web accses logs using relational competitive fuzzy clustering. In: Proceedings of springs Symposium On Natural Language Proccesing Form the www, Stanford, California. March 1997 (1997) Wong et al., 2001. Wong, C.: Shiu, S. and Pal, S.: Mining Fuzzy Association Rules for Web Access Case Adaptation. In: Workshop Proceedings of Soft Computing in Case-Based Reasoning Workshop, in conjunction with the 4th International Conference in Case-Based Reasoning, Vancouver, Canada, pp. 213–220 (2001) Zadeh, 1975. Zadeh, L.: The concept of linguistic variable and its application to approximate reasoning I. Information Sciences 8, 199–251 (1975)

Stability Analysis for Nonlinear Systems Subjected to External Force Ken Yeh1 , Cheng-Wu Chen2, , Shu-Hao Lin1 , Chen-Yuan Chen3 , Chung-Hung Tsai4 , and Jine-Lih Shen1 1

Department of Civil Engineering, De-Lin Institute of Technology, Tucheng, Taipei, Taiwan, R.O.C. 2 Department of Logistics Management, Shu-Te University, Yen Chau, Kaohsiung, Taiwan 82445, R.O.C [email protected] 3 Department of Management Information System, Yung-Ta institute of Technology and Commerce, Ping-Tung 909, Taiwan, R.O.C. 4 Center of Tour Geographical Information Systems, Taiwan Hospitality and Tourism College, Hualien, Taiwan, R.O.C.

Abstract. This paper considers a fuzzy Lyapunov method for stability analysis of nonlinear systems subjected to external forces. The nonlinear systems under external forces can be represented by Tagagi-Sugeno (T-S) fuzzy model. In order to design a nonlinear fuzzy controller to stabilize this nonlinear system, the parallel distributed compensation (PDC) scheme is used to construct a global fuzzy logic controller. We then propose the robustness design to ensure the modeling error is bounded and some stability conditions are derived based on the controlled systems. Based on the stability criterion, the nonlinear systems with external forces are guaranteed to be stable. This control problem can be reformulated into linear matrix inequalities (LMI) problem. Keywords: Fuzzy Lyapunov method, fuzzy control.

1

Introduction

Since Zadeh (1965) proposed a linguistic approach to simulate the thought process and judgement of human beings, there has been rapidly growing interest in FLC (fuzzy logic control) and many successful works on industrial and academic fields had been seeking (see [1], [2], [3], [4] ). However, FLC techniques suffer from one problem that the design of the FLC is difficult because few theoretical bases are available. Since then, some types of fuzzy models are developed such as Mamdani models, Takagi-Sugeno models, and neuro-fuzzy models etc. to increase accuracy and dimensionality and to simplify the structure of nonlinear systems. The advantage of fuzzy models over conventional mathematical models is the possibility of their being elaborated using much less information about a system. Takagi-Sugeno (T-S) model is first described in [5] which is 

Corresponding author.

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also calledTakagi-Sugeno-Kang model. This kind of fuzzy model can combine the flexibility of fuzzy logic theory and the rigorous mathematical analysis tools into a unified framework. For the reason that it employs linear models in the consequent parts, using conventional linear system theory for analysis becomes simple. Moreover, local dynamics in different state space regions are represented by a set of linear sub-models. The overall model of the system is then a fuzzy blending of these linear sub-models. Based on the T-S model, the parallel distributed compensation (PDC) concept was used to design the fuzzy controller of nonlinear systems [6]. In the PDC concept, the fuzzy controller shares the same fuzzy sets with the fuzzy model in the premise parts and each control rule is distributively designed for the corresponding rule of the fuzzy model. The overall nonlinear control system is not always stable even if all its subsystems are stable. Since then, some significant research efforts have been presented for stability analysis of fuzzy system and fuzzy controller design based on T-S model (for example, see [7], [8], [9], [10], [11] and the references therein). However, there are two issues we have to emphasize in this paper. One of the issues is that most of the above reported results show that the stability analysis is based TS fuzzy systems but not for nonlinear systems. That is, the T-S fuzzy model is used to represent practical nonlinear systems and controllers are designed only for fuzzy systems. However, the effect of modeling error between nonlinear system and T-S fuzzy model is not considered. The modeling error is a possible factor to influence the performance of the controlled systems. The other one of the issues is to extend the Lyapunov functions for stability issues of T-S type systems. Beside the above successful results in fuzzy control of T-S type fuzzy model, many papers considered single Lyapunov function for stability conditions of fuzzy control such as [12], [13], [14], [15], [16], [17]. These basically reduce the stability problem to linear matrix inequalities (LMIs) problems in terms of a common Lyapunov function. TheLMI theory is a new and rapidly growing field that provides a valuable alternative to the analytical method (for more details, see [18], [19] ). A variety of problems that arise in system and control theory can be reduced to a few standard convex or quasiconvex optimization problems involving the LMI. These resulting optimization problems can be easily solved by numerical computation, so LMI techniques are very efficient and practical tools for solving complex control problems. In order to avoid conservatism of stability and stabilization problems, multiple Lyapunov functions have been paid increasing attention e.g., ([20], [21], [22]). Therefore, here we propose the fuzzy Lyapunov approaches on stability analysis of nonlinear systems subjected to external forces. The organization of this paper is presented as follows: First, in order to model nonlinear systems with external forces, the T-S fuzzy modeling is briefly reviewed and the parallel distributed compensation (PDC) scheme is used to construct a global fuzzy logic controller. Then, the stability condition derived via fuzzy Lyapunov functions is proposed to guarantee the stability of the controlled system and the robustness is inspected by this condition.

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Recalled Model and Control Methods

The representation of a plant and controller via T-S type fuzzy model makes it easier to prove the stability of control systems due to the formulation of a locally linear structure. Therefore, T-S fuzzy modeling is employed in this paper to simplify the controller design problem and stability conditions are derived in terms of Lyapunov direct methods in combination with LMI. First of all, the T-S fuzzy model can have the form of a set of rules as well as membership functions represented below. R1: IF {z is F1 } THEN {γ = f1 (z)} R2: IF {z is F2 } THEN {γ = f2 (z)} .. . Rm: IF {z is Fm } THEN {γ = fm (z)} Then the output can be obtained on the basis of the grade of activation of the particular conclusions fi , i = 1, 2, . . . , m which is determined by following formula: m i=1 μFi (z)fi (z) γ=  m i=1 μFi (z) Where Fi is the fuzzy set; the functionfi (z) can be nonlinear. Then, a nonlinear system can be approximated by this T-S fuzzy model technique. The T-S model consists of a set of If-Then rules and each rule represents the local linear inputoutput relation of the nonlinear system. 2.1

System Description

A nonlinear system with external forces can be approximated by a T-S fuzzy model. Consider nonlinear systems that are represented as follow: x˙ = f (x(t), u(t) + φ(t)

(1)

where f is a nonlinear vector-valued function, t denotes time, x(t) ∈ Rn is the state vector, φ(t)denotes the external force, x(t)is ˙ derivative of x(t) , u(t) ∈ Rm is the input vector. Definition 1. (Khalil, 1992[23]): The solution of a dynamic system are said to be uniformly ultimately bounded (UUB) if there exist positive constants β and κ, and for every δ ∈ (0, κ) there is a positive constant T = T (δ) , such thatx(t0 ) < δ ⇒ x(t) ≤ β, ∀t ≥ t0 + T The T-S model consists of a set of If-Then rules. Each rule represents the local linear input-output relation of the nonlinear system and has the following form:

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A. T-S Fuzzy Model Plant Rule i: IF z1 (t) is Mi1 and . . . and zg (t) is Mig THEN x(t) ˙ = Ai (t) + Bi u(t) + φ(t), i = 1, 2, . . . , r

(2)

where state vectorxT (t) = [x1 (t), x2 (t), . . . , xn (t)] ∈ R1×n control inputuT (t) = [u1 (t), u2 (t), . . . , um (t)] ∈ R1×m and unknown disturbanceφT (t) = [φ1 (t), φ2 (t), . . . , φz (t)] ∈ R1×z with a known upper bound φupj (t) ≥ φj (t)  , Mip (p=1,2,. . . , g ) is the fuzzy set; γ is the rule number; z1 (t) . . . zg (t) are the premise variables; Ai ∈ Rn×n ,Bi ∈ Rn×m B. PDC Design The fuzzy controller rules have the same premise parts as those of the T-S model. Linear control rule i is derived based on the state equation (2) in the consequent part of the ith model rule. Control Rule i: IF z1 (t) is Mi1 and . . . and zg (t) is Mig THEN u(t) = −Fi x(t), i = 1, 2, . . . , r

(3)

where Fi is the local feedback gain matrix. The final control u is inferred using the Sum-Product reasoning method: u(t) = −

r ω (t)Fi x(t) i=1 r i i=1 ωi (t)

(4)

g Mip (zp ) where ωi is the activation degree of the ith rule, calculated as:ωi (t)=Πp=1 By substituting Eq.(4) into Eq. (2), the models of closed-loop control system are obtained as r r   x(t) ˙ = hi (t)hl (t)(Ai − Bi Fl )x(t) + φ(t) (5) i=1 l=1

Definition 2. (Lu et al., 1998 [24]): LMI Formulation of the Design Specifications The linear matrix inequality (LMI) is any constraint of the form F (ν) = F0 +

m 

νi Fi

(6)

i=1

where ν = [ν1 , ν2 . . . , ν0 ] ∈ Rm is the variable vector, and the symmetric matrices Fi = Fit ∈ Rn×n , i = 0, 1, . . . , m are given. It can be shown that the solution set {ν | F (ν) > 0} may be empty, but it is always convex. Thus, although (6) has no analytic solution in general, it can be solved numerically by efficient numerical algorithms. Many control problems can be reformulated into LMIs and solved efficiently by recently developed interior-point methods [18].

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Robustness Design of Controlled Systems Via Fuzzy Lyapunov Method

In this section, we will inspect the stability under the influence of modeling error between the T-S fuzzy model and the nonlinear system (1). In the beginning, the nonlinear system (1) can be described as follow: x(t) ˙ = f (x(t), u(t)) + φ(t) +

r r  

hi (t)hl (t)(Ai − Bi Fl )x(t)

i=1 l=1

=

r r  

hi (t)hl (t)(Ai − Bi Fl )x(t) + e(t) + φ(t)

(7)

i=1 l=1

r r where e(t) ≡ f (x(t), u(t)) − i=1 l=1 hi (t)hl (t)(Ai − Bi Fl )x(t). Suppose that there existsa bounding matrix ΔH such that   e(t) ≤ ri=1 rl=1 hi (t)hl (t)ΔHx(t)  and ΔH = δH where  δ ≤ 1. Then we can readily obtain eT (t)e(t) ≤ [Hx(t)]T [Hx(t)]

(8)

That means modeling error can be bounded by the matrix H. The proof of Eq. (8) and the procedures for determining δ and H can be referenced in [25]. Here we define a fuzzy Lyapunov function and consider the stability conditions for controlled system (7). Definition 3. Equation (9) is said to be a fuzzy Lyapunov function for the T-S fuzzy system if the time derivative of V (x(t)) is always negative at x(t) = 0. V (x(t)) =

r 

hi (t)xT (t)Pi x(t)

(9)

i=1

where Pi is a positive definite matrix. In this paper, in order to consider the term of the time derivative h˙ ρ (t) can be solved numerically, an upper bound of time derivative, i.e.h˙ ρ (t) ≤ φρ , is used. In the following, a stability criterion is proposed to guarantee the stability of the closed-loop nonlinear system with external forces. Before a stability condition is proposed, a useful concept is given below: Lemma 1. (Li and Souza, 1997[26] ; Chen et al., 2004[8] ): For any A, B∈ Rn and for any symmetric positive definite matrix G∈ Rn×n or R , we have − 2AT B ≤ AT GA + B T G−1 B

(10)

Theorem 1. The controlled system (7) is stable in the large if there exist common positive definite matrices such that the following inequality is satisfied: r  φρ Pρ + (Aj − Bj Fl )Pi + Pi (Aj − Bj Fl ) ρ=1

+α−1 Pi Pi + βH T (t)H(t) + β −1 Pi Pi < 0

(11)

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Proof. Consider the Lyapunov function candidate V (x(t)) =

r 

hi (t)T (t)Pi x(t)

i=1

The time derivative of V is V˙ (X(t)) =

=

r  ρ=1 r 

h˙ ρ (t)xT (t)Pρ x(t) +

r 

hi (t){x˙ T (t)Pi x(t) + xT (t)Pi x(t)} ˙

i=1

h˙ ρ (t)xT (t)Pρ x(t)

ρ=1

+

r 

r r   hi (t){[ hj (t)hl (t)(Aj − Bj Fl )x(t) + e(t) + φ(t)]T Pi x(t)}

i=1

+

r 

j=1 l=1 r r   hi (t){xT (t)Pi [ hj (t)hl (t)(Aj − Bj Fl )x(t) + e(t) + φ(t)]}

i=1

j=1 l=1

(12) Based on Lemma 1 and (12), we get V˙ (X(t)) ≤

r 

h˙ ρ (t)xT (t)Pρ x(t) + xT (t)[α−1 Pi Pi + β −1 Pi Pi ]x(t)

ρ=1

+αφT (t)φ(t) + βeT (t)e(t) r  r r   hi (t)hj (t)hl (t)xT (t) + i=1 j=1 l=1

{(Aj − Bj Fl )Pi + Pi (Aj − Bj Fl )}x(t) (13) Based on Eq. (8) and note that φupj (t) ≥ φj (t)  , we would have V˙ (X(t)) ≤ r  r r  

hi (t)hj (t)hl (t)xT (t)

i=1 j=1 l=1 r 

φρ Pρ + (Aj − Bj Fl )Pi + Pi (Aj − Bj Fl )

{

ρ=1

+α−1 Pi Pi + βH T (t)H(t) + β −1 Pi Pi }x(t) + α  φup (t) 2 (14) The nonlinear system with external force is UUB stable if Theorem 1 is satisfied. Based on stability condition of Theorem 1, the proof is thereby completed.

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Conclusions

This paper considered a fuzzy Lyapunov method to derive the stability condition for the nonlinear system with external force. The fuzzy Lyapunov function is defined by fuzzy blending quadratic Lyapunov functions. First, the nonlinear systems are represented by T-S fuzzy model. In order to ensure the system can be stabilized by proposed T-S fuzzy controller, the robustness design is proposed to overcome the modeling error. Then, Stability conditions of closed-loop controlled systems are derived based on fuzzy Lyapunov functions to avoid conservatism.

Acknowledgments This work was supported by the National Science Council of Republic of China under Grant No. NSC 95-2221-E-366-001 and NSC 95-2221-E-237-011 The authors wish to appreciate the constructive suggestions from anonymous reviewers to this paper.

References 1. Chang, S.S.L., Zadeh, L.A.: On fuzzy mapping and control. IEEE Trans. Syst. Man Cybern. 2, 30–34 (1972) 2. Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man, Cybern. 3, 28–44 (1973) 3. Kickert, W.J.M., Mamdani, E.H.: Analysis of a fuzzy logic controller. Fuzzy Sets Syst. 1, 29–44 (1978) 4. Braae, M., Rutherford, D.A.: Theoretical and linguistic aspects of the fuzzy logic controller. Automatic 15, 553–577 (1979) 5. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man, Cybern. 15, 116–132 (1985) 6. Wang, H.O., Tanaka, K., Griffin, M.F.: Parallel distributed compensation of nonlinear systems by Takagi and Sugenos fuzzy model. In: Proceedings of 4th IEEE International Conference on Fuzzy Systems, Yokohama, Japan, pp. 531–538 (1995) 7. Chen, X.J., Sun, Z.Q., He, Y.Y.: Analysis and design of fuzzy controller and fuzzy observer. IEEE Trans. Fuzzy Syst. 6, 41–51 (1998) 8. Chen, C.W., Chiang, W.L., Hsiao, F.H.: Stability analysis of T-S fuzzy Models for nonlinear multiple time-delay interconnected systems. Mathematics and Computers in Simulation 66, 523–537 (2004) 9. Guerra, T.M., Vermeriren, L.: Control law for Takagi-Sugeno fuzzy models. Fuzzy Sets Syst. 120, 95–108 (2001) 10. Li, J., Wang, H.O., Niemann, D., Tanaka, K.: Dynamic parallel distributed compensation for Takagi-Sugeno fuzzy systems: an LMI approach. Information Sciences 123, 201–221 (2000) 11. Wang, H.O., Tanaka, K., Griffin, M.F.: An approach to fuzzy control of nonlinear systems: stability and design issues. IEEE Trans. Fuzzy Syst. 4, 14–23 (1996) 12. Tanaka, K., Sugeno, M.: Stability analysis and design of fuzzy control systems. Fuzzy Sets Syst. 45, 135–156 (1992)

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13. Chen, C.VL., et al.: Analysis and design of fuzzy control systems. Fuzzy Sets Syst. 57, 125–140 (1993) 14. Wang, L.K., Leung, F.H.F., Tam, P.K.S.: Fuzzy model-based controller for inverted pendulum. Electron. Letters 32, 1683–1685 (1996) 15. Cao, S.G., Rees, N.W., Feng, G.: Stability analysis and @design for a class of continuous-time fuzzy control systems. Int. J. Control 64, 1069–1087 (1996) 16. Cuesta, F., Gordillo, F., Aracil, J., Ollero, A.: Stability analysis of nonlinear multivariable Takagi-Sugeno fuzzy control systems. IEEE Trans. Fuzzy Syst. 7, 508–520 (1999) 17. Chen, C.Y., Hsu, R.C., Chen, C.W.: Fuzzy logic derivation of neural network models with time delays in subsystems. Int. J. Artificial Intelligence Tools 14, 967–974 (2005) 18. Boyd, S., El Ghaoui, L., Feron, E., Balakrishnan, V.: Linear matrix inequalities in system and control theory. Philadelphia, PA: SIAM (1994) 19. Nesterov, Yu., Nemirovsky, A.: Interior-point polynomial methods in convex programming. SIAM, Philadelphia, PA (1994) 20. Tanaka, K., Hori, T., Wang, H.O.: A dual design problem via multiple Lyapunov functions. In: Proc. IEEE Int. Conf. Fuzzy Systems, pp. 388–391 (2001) 21. Tanaka, K., Hori, T., Wang, H.O.: A multiple Lyapunov function approach to stabilization of fuzzy control systems. IEEE Trans. Fuzzy Syst. 11, 582–589 (2003) 22. El-Farra, N.H., Mhaskar, P., Christofides, P.D.: Output feedback control of switched nonlinear systems using multiple Lyapunov functions. Systems and Control Letters 54, 1163–1182 (2005) 23. Khalil, H.K.: Nonlinear Systems. Macmilllan, London. U.K (1992) 24. Lu, L.T., Chiang, W.L., Tang, J.P.: Application of model reduction and LQG/LTR robust control methodology in active structure control. J. Eng. Mech. ASCE 124, 446–454 (1998) 25. Tseng, C.S., Chen, B.S.: Decentralized fuzzy model reference tracking control design for nonlinear interconnected systems. IEEE Trans. Fuzzy Syst. 9, 795–809 (2001) 26. Li, X., de Souza, C.E.: Criteria for robust stability and stabilization of uncertain linear systems with state delay. Automatica 33, 1657–1662 (1997)

Integrated Framework for Reverse Logistics Heng-Li Yang and Chen-Shu Wang Department of MIS, National Cheng-Chi University, 64, Sec. 2, Chihnan Rd., Mucha Dist, Taipei 116, Taiwan {yanh, 93356506}@nccu.edu.tw

Abstract. Although reverse logistics has been disregarded for many years, pressures from both environmental awareness and business sustainability have risen. Reverse logistical activities include return, repair and recycle products. Traditionally, since the information transparency of the entire supply chain is restricted, business is difficult to predict, and prepare for these reverse activities. This study presents an agent-based framework to increase the degree of information transparency. The cooperation between sensor and disposal agents helps predict reverse activities, avoid return, speed up repair and prepare for recycling behaviors. Keywords: Reverse Logistics, information transparency, agent-based system.

1 Introduction A complete supply chain concept typically includes forward and reverse logistics [16,17]. However, reverse logistics has been much less examined than forward logistics. Reverse logistics has recently emerged as crucial issues in both practices and academic studies [13,15,17]. Reverse logistics encompasses planning, implementing and controlling the efficient and cost-effective flow of raw materials, in-process inventory, finished goods and related information from the point of consumption to the point of origin to recapture value or dispose properly [16]. In the European Union, the Waste Electrical and Electronic Equipment (WEEE) directive, this came into force in August 2005, and the Restriction of Hazardous Substances (RoHS) directive, which came into force in 2006; requires companies to take responsibility for product that they sell throughout the product entire lifecycle [8]. Reverse logistics has become imperative for business [4]. Many previous studies have attempted to formulate mathematical models of reverse logistics. Among these studies, Min et al. presented a genetic algorithm model to deploy centralized return centers [12]. Klausner and Hendrickson explored the relationship between product return ratio and reverse logistics strategy [9]. Kulshreshtha and Sarangi examined the link between recycling and price discrimination [10]. Although these optimization models provide partial reverse logistic solutions, they include many assumptions that would not hold in reality. Since reverse logistic activities are too uncertain to formulize [7], the information about them should ideally be combined. Additionally, some studies have investigated this issue from the perspective of the entire supply H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 501–510, 2007. © Springer-Verlag Berlin Heidelberg 2007

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chain. For instance, Beamon extended the forward supply chain, and proposed the green supply chain concept [2]. Tibben-Lembake and Rogers discussed the distinction between forward and reverse logistics in multiple dimensions [17]. Mollenkopf and Closs discussed the hidden value of reverse logistics from the entire supply chain [13]. Richey et al. surveyed reverse logistics programs, and claimed that information is a critical factor [15]. Companies are increasingly utilizing reverse logistics as a business strategy [9]. For instance, loose return policies might give customers the impression of high product quality [16]. Additionally, a business may earn goodwill from socially or environment responsible behavior [4,13]. However, these reverse logistics strategies might lead to a large amount of returned and recycled merchandise. Businesses require additional information to resolve this vicious circle. Otherwise, the opaque information might invoke a huge bullwhip effect. As business obtains more information, they can predict and prepare, or even prevent bad effects in reverse activities. If the returned products are fashion merchandise, such as 3C electronic product or seasonal clothes, then the product remaining value might fall when they are sent back by the consumer to the producer site [8]. Therefore, if sufficient information is available to enable businesses to predict returns early, then business could properly prepare and reduce process time to maintain their remaining values. Additionally, due to the enforcement of WEEE or RoHS in European Union, manufacturers would become concerned with the recycle ratio at any time. This study considers these reverse logistic activities more actively. An agent-based model is presented to increase information transparency degree (ITD) of the entire supply chain management (SCM). A supply chain with a high ITD serves as an early warning system, and works very efficiently. A High ITD enhances information sharing within an entire supply chain management (SCM).

2 Problem Descriptions Previous studies [2,3,6,11,12,13,18] have categorized reverse logistic activities into three groups, as shown in Table 1, namely return, repair and recycle. In the process of forward logistics, suppliers provide raw material to manufacturers, who make products, which are then sent to customers, generally through distributors. Conversely, a customer might send a product back for return, repair or recycling. Additionally, manufacturers and suppliers also need to deal with defective or nonworking products. Recycling collectors need to dispose of these recycled products properly, and transfer reusable materials back to the supplier and manufacturer. This process is known as reverse logistics. These reverse activities have the following problems. (1) If a customer returns product to a distributor, then the distributor might stock returned products to a particular level, then send them back to manufacturer. However, this practice adversely affects the manufacturer, who has less time to process the returned products, thus the lowering their remaining value. (2) Recycling laws, such as WEEE and RoHS in the European Union, increase the importance of recycling activities. Businesses need to monitor recycle ratios, and raise them to comply with recycling laws. (3) Finally, in the repair aspect, repairing processing time should be reduced to maintain the image of a business.

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Table 1. The definition of reverse logistic activities

Activity Return

Definition Consumers return the products bought within certain period of time for any reason (rational or irrational).

Repair

Consumers send broken product to repair center (or original producer). Consumers send unvalued or unwanted product to recycling collectors.

Recycle

Result Depending on policies, customer may receive another identical new product, an equivalent product exchange or full money back. Customer generally would receive workable product back. Customers might or might not receive rewards.

All these problems are customer-centric and difficult to predict. However, if the ITD of the entire supply chain could be improved, then the prediction accuracy could be enhanced to enable the upstream and downstream enterprises of supply chain to be prepared early.

3 Proposed Framework This study assumes symbiosis in the entire supply chain system. The supply chain’s participants are assumed to share three databases, namely customer, product and transaction. The access permissions are as follows. (1) In the customer DB, the distributor can insert and update and all other participants can only query. (2) In the product DB, the manufacturer can insert and update and all other participants can only query. (3) In the transaction DB, the distributor can insert and update; manufacturer can update, and all other participants can only query. The shared data are updated regularly. This symbiosis assumption is consistent with the concept of strategic partners, in which innovative enterprises share sales data, customer buying patterns and future plans with their partners [14]. Since most reverse activities are triggered by customers, and are hard to predict accurately by conventional analytic approaches, heuristics and AI techniques might help [14]. In the forward supply chain, Piramuthu [5] developed an agent-based framework to automate supply chain configuration, and to improve the performance of the supply chain with dynamic configuration. However, to our knowledge, no study has applied agents to reverse activities. This study presents an agent-based approach. As illustrated in Figure 1, two agents, the sensor agent and the disposal agent, are integrated within the proposed framework. Since an agent could autonomously monitor the changing environment and react automatically to complete a goal, they might helpfully manage this customer-centric problem. 3.1 The Sensor Agent The sensor agent autonomously monitors the recent data, and transmits warning signals to the disposal agent at appropriate times. Additionally, it performs marketing

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Shared Data Center Distributor Customer DB

Disposal Agent for Return/Repair

Product DB

Product

Transaction DB

Product Return/Repair

Supplier Material

Manufacturer

Recycle

Product

Recycle

Sensor Agent

Customer

Recycle

Reuse Reuse

Recycling Collector

Disposal Agent for Recycle ---- Physical Material Flow …. Information Flow

Fig. 1. The proposed agent-based framework Table 2. Some rules for detecting reverse activities

Dimensions Customer

Product

Customer and Product Customer and Marketing Strategic Product and Marketing Strategic

Attributes (recency, frequency, monetary)=(H,H,M) Gender= Female and Education= High Size= Huge Price= High Hard to Operating Customer_Location= Moist and Product=3C Electric Equipments Customer_Income=Low and Market_Strategy= “Buy 1 get 1 free” Product_Size= Small and Market_Strategy= “Double Credit”

Return Ratio H

Repair Ratio -

Recycle Ratio M

H

M

-

L H -

H H

H -

L

-

-

H

-

L

Note: H=High, M=Moderate, L=Low

surveys if required. Since product returns might result in serious supply chain problems, return data should be monitored at least weekly. Conversely, since monitoring repair data is likely to be less urgent than monitoring return data, a monthly monitoring period might be sufficient. The possible product recycle time

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could be estimated from the product life cycle. Therefore, this study recommends enabling active database triggers in customer profile data to provide notice signals. The monitoring should follow rules to detect possible reverse activities. Table 2 lists some such rules. These rules come from heuristics and data mining results. The sensor agent should periodically perform data mining on the historical data or data warehouse. Some data mining techniques (e.g., those in Table 3) could be considered. The cluster analysis considers some transaction level attributes, e.g., recency, frequency and monetary (RFM) attributes, to cluster customer and discover the reverse activity patterns of customer demographic information. Additionally, in the product dimension, product characteristics could be adopted to cluster products rather than original product types. Furthermore, since some patterns might be cross clusters, the association analysis would take at least two cluster results from cluster analysis as inputs to discover the reverse patterns between these two inputs. For instance, some clusters of customers, who bought products, might be found to have high return ratios. These discovered patterns would be reviewed by experts, and then fed into the rule base of the sensor agent. Therefore, the sensor agent would have a learning capability to improve its own monitoring correctness. Table 3. Examples of data mining of sensor agent

Cluster Analysis Adopting transaction level attributes (e.g., Recency, Frequency, Monetary) to segment the customers. Then, observing demographic level (e.g., gender, education, income, location) attributes to discover reverse activity patterns of customer clusters. Adopting product properties (e.g., size, price, operation) to cluster product to discover reverse patterns of product properties. Association Analysis Finding reverse activity patterns between customer and product clusters. Identifying reverse activity patterns between customer clusters and marketing strategies. Detecting reverse activity patterns between product clusters and marketing strategies. Discovering reverse activity patterns among customer clusters, product clusters and marketing strategies. 3.2 The Disposal Agent After receiving signals from the sensor agent, the disposal agent recommends treatments by case-based reasoning (CBR) [1], and reference supplementary rules if necessary. The case base stores successful cases from previous experience. The rule base includes some supplementary heuristics from domain experts.

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Fig. 2. The case-based reasoning of disposal agent

While (Warning Signal) { Reasoning by cases and supplementary rules Switch (UF) Case: Moderate 1. Suggests some particular treatments 2. Disposal agent performs these treatments automatically Case: Influential 1. Suggests particular treatments to decision maker for preventing return and enhancing recycling ratio 2. Decision-maker refers these treatments and may revise. Case: Serious 1. Suggests treatments to decision-maker. 2. Schedule business processes to prepare for possible reverse activities. End Switch; Evaluate performance of suggestion; If the event performance is good, then retain to Case base}

Fig. 3. The disposal agent suggestion

As revealed in Figure 2, a warning signal consists of three parts {urgent degree (UF), signal flag (SF), trigger features}. SF could be “return”, “recycle”, or “repair”. UF indicates degrees of impact. In Figure 3, depending on the different UF, the system would have different actions. It compares {SF, trigger features} to those {CF, case features} of cases in case base, and retrieve the treatments of the fittest case to decision maker. It might refer to supplementary rules for detailed suggestions or other suggestions (if no suitable case could be found). Then, disposal agent might perform

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treatments automatically or suggest to decision-makers. It would cooperate with other systems, e.g., programs of scheduling, inventory management or quality checking. If the response of this problem solving is good, then the experiences may be annotated by human experts, and then retained in the case base as further references. Therefore, the disposal agent could have learning capability to improve its performance next time. 3.3 The Integrated System Framework As illustrated in Fig. 4, the framework has three stages. At stage I, the sensor agent monitors the data; predicts the possibilities of reverse activities, and transmits different warning signals to the disposal agent. The rule base comes from heuristics, and is periodically updated by data mining techniques (e.g., clustering and association analyses). At stage II, the disposal agent recommends feasible treatments from past cases and referencing rules. At stage III, for possible serious effects, disposal might further recommend or automatically initiate some related business process preparations (e.g., scheduling). Additionally, the disposal agent should notify the sensor agent of its treatment, and ask for a necessary follow-up. For instance, if a sensor agent discovers that the frequencies of customer complaint phones have risen, and predicts that the possible return rate is likely to increase, then the disposal agent recommends employing customer specialists to listen to customer concerns. After the treatment has been completed, the sensor agent performs a customer satisfaction survey to check whether the problems have been solved. The sensor agent also gives the disposal agent the evaluation feedback concerning the effectiveness of the treatment. Based on the feedback, the disposal agent adds annotations to the original case base, and recommends further treatment if needed. Shared Data Center Marketing Survey Customer Clusters Product Clusters

Stage I:

Rule Base

Predict Sensor Agent

Market Strategy Clusters Clustering Analysis

Association Analysis

Issue warning

Rule Base

Signals Stage II: Suggest Disposal Agent

Business Processes Stage III:

• Scheduling (engineer, process, etc)

Prepare

• Inventory Management (space, QC, etc)

Follow-up

•…

Fig. 4. The cooperation between sensor agent and disposal agent

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4 Illustrative Scenarios To understand the proposed framework clearly, the three classes of reverse logistic activity are described as follows. 4.1 Return Scenario According to the proposed framework, the sensor agent monitors the data, which are gathered from the consumer site and shared data center; performs weekly crossanalyses to diagnose the return probability, and transmits alarm signals. For instance, assume customer is making an increasing number of complaints, and that her (his) profile (Gender, Education)=(Female, High) matches one return pattern in Table 3. The sensor agent verifies the warrant period of the related transaction. If the guarantee period has expired, then a “moderate” signal is sent. Conversely, if the product is still under guarantee, then an “influential” signal is sent, while if the original transaction amount was also large, then a “serious” signal is flagged. The disposal agent then recommends appropriate treatments. For moderate signals, the disposal agent automatically sends an e-mail to a customer acknowledging the customer’s concerns. For “influential” signals, the disposal agent advises a customer specialist to contact the customer in order to prevent possible return. For serious signals, the disposal agent recommends performing related business processes such as preparing return stock-location. After the treatment is completed, the sensor agent should follow up the customer satisfaction and give feedback to the disposal agent. The proposed framework could provide an early warning to the manufacturer about possible returns, and additionally could summarize the top 10 return reasons for product re-design. The ITD would increase under this framework. 4.2 Repair Scenario Based on the proposed framework, the sensor agent would analyze the complaints from consumers monthly, and calculate the repair possibilities. For instance, suppose that some customers of electronic products live in the moist area, matching a rule in Table 2. The sensor agent judges, according to the past data, that some parts of these products might malfunction later. If these parts are normal materials, then a “moderate” signal is transmitted. If these parts contain special materials, then an “influential” signal is sent. If the repairing behaviors would require particular engineer skills, then a “serious” signal is flagged. The disposal agent recommends appropriate treatments to the decision maker. For a “moderate” signal, the disposal agent verifies the material stocks, and automatically schedules these repair requirements. For “influential” signals, the disposal agent recommends material procurements to the decision maker. In this case, owing to the longer repair period, the disposal agent arranges a temporary replacement product for customers. For serious signals, the disposal agent schedules another engineer, or recommends further training for engineers. After the treatment is completed for certain period (say one month), the sensor agent follows up customer opinions, and gives feedback to the

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disposal agent for further improvement. The ITD of the SCM is higher under the proposed framework than in other systems, enabling the repair center to prepare for possible repairs to accelerate the repair time. 4.3 Recycling Scenario According to the proposed framework, database triggers notify the sensor agent the possibilities for recycling when the product approaches the end its life. The product size and materials is checked. If the product materials are normal, then the sensor agent sends a moderate signal. If the products contain toxic or harmful materials, then the senor agent sends a “serious” signal. The disposal agent then recommends treatments to the decision maker. For a “moderate” signal, distributors are recommended to conduct relationship marketing to their customers to express concerns about their product usage. Additionally, some notification messages could be transmitted automatically to the recycling collector to raise the ratio of recycled material. For serious signals, the disposal agent should report to the decision maker to comply with WEEE and RoHS requirements. The proposed framework raises the ITD of SCM. Moreover, the recycling ratio could be expected to increase if the recycling promotion becomes more active.

5 Conclusions Reverse logistic activities have recently become a critical issue for both consumer and producer sites, but present some dilemmas. (1) Businesses are increasingly adopting loose return policy as strategy. However, in practice, the returned products are stocked by distributors, cannot be processed quickly by manufacturers to regain economic value quickly. (2) As new environmental laws are increasingly being enforced, recycling activities are additional burdens to the manufacturer, but are also social and environment responsibilities. Additionally, the recycling behaviors are not necessary for customer, for whom the reward is limited. Therefore, recycling is difficult to implement well in practice. (3) Repair is inconvenient for both customers and repair centers. Customers cannot use their products during the repair period. Thus, decreasing the repair time could improve customer’s satisfaction. However, without proper information, repair centers cannot schedule the required resources to shorten the repair time. This study presents an agent-based framework to improve information transparency degree of these reverse activities. A sensor agent operates like an early warning system to detect possible reverse activities actively. A disposal agent operates like a consultant, recommending treatments to decision maker, and even arouse related business processes automatically. The proposed framework is expected to increase the supply chain’s information transparency degree, and improve the performance of reverse supply chain activities. Future research will concentrate on implementing this framework, and on verifying its performance and effectiveness using real-world data and field studies.

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References 1. Aamodt, A., Plaza, E.: Case Based Reasoning: Foundational Issues, Methodological Variations, and System, Approaches. In: AI Communications, vol. 7(1), pp. 39–59. IOS Press, Amsterdam (1994) 2. Beamon, B.M.: Designing the Green Supply Chain. Logistics Information Management 12(4), 332–342 (1999) 3. Campbell, G.C.: Merchandise Returns: Retailer’s Viewpoint. Quarterly Review of Commerce 8(2), 141–151 (1941) 4. Carter, C.R.: Purchasing and Social Responsibility: A Replication and Extension. Journal of Supply Chain Management 40(4), 4–16 (2004) 5. Emerson, D., Piramuthu, S.: Agent Based Framework for Dynamic Supply Chain Configuration. In: Procedings of the 37th Hawaii International Conference on System Sciences, pp. 1–9 (2004) 6. Guide, D.R., Jayaraman, V., Srivastava, R., Beton, W.C.: Supply Chain Management for Recoverable Manufacturing Systems. Interface 30(3), 125–142 (2002) 7. Inderfurth, K.: Impact of Uncertainties on Recovery Behavior in a Remanufacturing Environment. International Journal of Physical Distribution & Logistics Management 35(5), 318–336 (2005) 8. Kempfer, L.M.: New Ideas for Handling Electronic Product Returns. Material Handling Management 60(5), 35–36 (2005) 9. Klausner, M., Hendrickson, C.T.: Reverse Logistics Strategy for Product Take Back. Interfaces 30(3), 156–165 (2000) 10. Kulshreshtha, P., Sarangi, S.: No Return, No Refund: An Analysis of Deposit Refund Systems. Journal of Economic Behavior & Organization 46, 379–394 (2001) 11. Lambert, A.J.D., Boelaarts, H.M., Splinter, M.A.M.: Optimal Recycling System Design: With an Application to Sophisticated Packaging Tools. Environmental and Resource Economics 28(3), 273–299 (2004) 12. Min, H., Ko, H.J., Ko, C.S.: A Genetic Algorithm Approach to Developing the MultiEchelon Reverse Logistics Network for Product Returns. Omega 34, 56–69 (2006) 13. Mollenkopf, D.A.: The Hidden Value in Reverse Logistics. Supply Chain Management Review 9(5), 34–43 (2005) 14. Papazoglou, M.P.: Agent-Oriented Technology in Support of E-Business. Communications of the ACM 44(4), 71–77 (2001) 15. Richey, R.G., Chen, H., Genchev, S.E., Daugherty, P.J.: Developing Effective Reverse Logistics Programs. Industrial Marketing Management 34, 830–840 (2005) 16. Rogers, D.S., Tibben-Lembke, R.S.: An Examination of Reverse Logistics Practices. Journal of Business Logistics 22(2), 129–148 (2001) 17. Tibben-Lembke, R.S., Rogers, D.S.: Differences Between Forward and Reverse Logistics in a Retail Environment. Supply Chain Management 7(5), 271–282 (2002) 18. Walton, S.V., Handfield, R.B., Melnyk, S.A.: The Green Supply Chain: Integrating Suppliers into Environmental Management Processes. International Journal of Purchasing and Materials Management 34(2), 2–11 (1998)

Screening Paper Formation Variations on Production Line Marcus Ejnarsson1, Carl Magnus Nilsson1 , and Antanas Verikas1,2 1

2

Intelligent Systems Laboratory, Halmstad University, Box 823, SE-301 18 Halmstad, Sweden [email protected], [email protected] Department of Applied Electronics, Kaunas University of Technology, Studentu 50, LT-513 68, Kaunas, Lithuania [email protected]

Abstract. This paper is concerned with a multi–resolution tool for screening paper formation variations in various frequency regions on production line. A paper web is illuminated by two red diode lasers and the reflected light recorded as two time series of high resolution measurements constitute the input signal to the papermaking process monitoring system. The time series are divided into blocks and each block is analyzed separately. The task is treated as kernel based novelty detection applied to a multi–resolution time series representation obtained from the band-pass filtering of the Fourier power spectrum of the series. The frequency content of each frequency region is characterized by a feature vector, which is transformed using the canonical correlation analysis and then categorized into the inlier or outlier class by the novelty detector. The ratio of outlying data points, significantly exceeding the predetermined value, indicates abnormalities in the paper formation. The tools developed are used for online paper formation monitoring in a paper mill.

1

Introduction

To stand the high competition in the market related to the papermaking industry, companies are striving to get the best possible return from their equipment. Therefore, ensuring manufacturing of products of a desired constant quality that meets customer’s specifications offers a significant advantage for the companies. To assure the high constant quality of the end-products, a producer must have a possibility to objectively measure and monitor the quality of the products. Various defects can be encountered in paper. The most common ones are holes, impurities, too big variation of the paper formation, uneven paper shrinkage profile, etcetera. Tools utilized for paper production monitoring depend on a task. The paper shrinkage profile is usually assessed off-line. One of such techniques can be found in [1]. Image analysis based commercial systems exist for detecting holes and relatively large-size-impurities. The degree of small-size-impurities is usually assessed at a pulp level by analyzing images taken from pulp samples [2,3]. The mass distribution on the sheet—formation—is commonly assessed H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 511–520, 2007. c Springer-Verlag Berlin Heidelberg 2007 

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off-line by using the β-radiography [4]. However, the technique is rather time consuming. Therefore, various image analysis based techniques are often used instead [5,6,7,8,9]. Too big variation of the paper formation is one of the most common paper deficiencies encountered. This study is also concerned with monitoring this type of paper deficiencies. In a modern paper machine, the paper production process runs at about 30 m/s speed. The high production speed entails snapshot or coarse grid measurements. The sensors are typically mounted on a head traversing the web. The typical transverse time for the head is about 30 seconds. Approximately 700 meters of paper would have passed through the paper machine during that time. Variability of the paper structure has been studied by a number of researchers [8,9,10] and it was found that such a course sampling strategy works well for assuring the stability of the paper machine itself. However, it is not always adequate for assuring low variations in the paper structure. Most paper produced is used for printing. Printed colour pictures are made of small halftone dots of primary colours. Fig. 1 presents an example of an enlarged view of a small part of a colour picture printed using cyan, magenta, yellow and black primary inks.

Fig. 1. An enlarged view of a small part of a colour picture

The quality of such halftone printed colour pictures depends to a great extent on the quality of printed dots, which in turn is dependent on the paper formation. The size of the printed dots is only a fraction of a millimeter. Thus, to assess the printability of the sheet of paper the very local paper properties ought to be monitored on-line. This requires a high measurement capacity. A large amount of data must be continuously acquired and processed. Obviously, the interpretation of the data measured becomes more complex and time-critical. On the other hand, it is also important to detect low frequency variations of paper formation. Thus, a paper formation on-line monitoring system used should cover a broad range of formation variation frequencies. Creation and investigation of such a system is the main objective of this study. The system aims detecting abnormalities in various frequency regions ranging from millimeters to several meters. The abnormalities detected in different frequency regions give an indication to the paper maker about specific disturbances in the paper production process. To obtain an on-line fine characterization of the

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paper structure at a paper mill, a paper web running at about 30 m/s speed is illuminated by a red diode laser, the reflected light is collected by a photo-detector and recorded as a time series of high resolution measurements. Two such sensors mounted at about 50 mm distance from each other are utilized. The two time series constitute the input signal to the papermaking process monitoring system.

2

The Approach

The time series obtained from each detector is first divided into consequent blocks of a predetermined length and the fast Fourier transform (FFT) is applied to each block. Let us assume that F (u) is the Fourier transform of the time series f (x) and P (u) is the Fourier power spectrum: P (u) = F (u)2 = R2 (u) + I 2 (u)

(1)

where R(u) and I(u) are the real and imaginary parts of F (u), respectively. To enable monitoring in various frequency regions, the frequency axis is divided into several frequency regions Ri of different average frequency. The following way of partitioning has been applied: u ∈ Ri if iW ≤ log2 (u) < (i + 1)W

(2)

√ log2 [N/(2 2)] W = (3) Nr − 1 with Nr being the number of regions of different average frequency and N is the number of data points in the signal. The partitioning can be viewed as band selection in the frequency domain. Other way of partitioning can be utilized. The frequency content of the region a is characterized by a set of k features—a feature vector xa ∈ Rk . The features used will be presented shortly. Having such representations for all the frequency regions, the task of screening paper formation variations in various frequency regions is treated as a novelty detection problem using the obtained representations. A separate novelty detector is trained for each frequency region. The novelty detector is trained so that xa calculated using a time series block exhibiting too high formation variations in the frequency region a is classified by the detector as a novel data point. Time series obtained from the two sensors are analyzed by separate detectors and decisions obtained from the detectors are aggregated. A kernel based novelty detector was adopted in this work. The optimal values of the parameter vector α∗ of the detector are found by maximizing the following objective function [11]

where

W (α) =

N  i=1

αi κ(xai , xai ) −

N N  

αi αj κ(xai , xaj )

(4)

i=1 j=1

N subject to i=1 αi = 1 and 0 ≤ αi ≤ 1/νN , i = 1, ..., N , with κ(xa , xa ) being a kernel and N is the number of data points used to train the detector. The optimal

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parameter 0 < ν < 1 value is usually found by cross-validation. In this study, the parameter has been assigned the value equal to the expected fraction of outlying—novel—data points, known from the apriori process knowledge. Then, in the operating phase, the ratio of outlying data points, significantly exceeding the value of ν, indicates abnormalities in the paper formation. The function f (xa ) used to categorize the data point xa into the novel or non-novel class is given by: 

f (x ) = H κ(x , x ) − 2 a

a

a

N 

α∗i κ(xai , xa ) + T



(5)

i=1

where the parameters α∗i and T are found by maximizing Eq. 4 and the Heaviside function H[y(xa )] = 1 if y(xa ) ≥ 0 and −1 otherwise. The data point xa is assigned to the novel class if H[y(xa )] = 1 while H[y(xa )] = −1 means that the data point comes from the non-novel class. A comprehensive description of the detector can be found in [12]. 2.1

Features

Four measures characterizing the region frequency content are extracted. The measures utilized are: the average region energy E i , the normalized maximum region energy Emi , the Chi-square χi , and the entropy Mi of the Fourier power. The average region energy is given by Ei =

1  P (u) Ni

(6)

u∈Ri

where P (u) is the Fourier power at frequency u and Ni is the number of distinct frequencies in the region Ri . The normalized maximum region energy Emi is computed according to the following equation: Emi =

1 max P (u) E i u∈Ri

(7)

The Chi-square value χi in the ith frequency region is computed as χi = Ni2

  u∈Ri



1 2 Pi (u) − Ni u∈Ri Pi (u)

(8)

The Chi-square measures the difference between the power spectrum of the region Ri and the ”white noise spectrum”. The entropy measure Mi we use is given by  1 Mi = − Pin (u) log Pin (u) (9) log Ni u∈Ri

The E i , Emi , χi , and Mi values computed for the Ri region constitute the measurement vector zi .

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515

Transforming the Features

In this study, we are interested in paper formation variations occurring in the web in “machine direction” (MD). The sensor used monitors a very narrow track of paper in MD. To increase the robustness of the analysis, two sensors monitoring two parallel tracks are used. Nonetheless our interest in paper formation variations occurring in the machine direction only, formation variations in the cross-web direction (CD) are always present. Thus, signals obtained from the two sensors depend on the measuring positions chosen in CD. To mitigate the influence of the measuring positions chosen and the disparity in parameters of the two sensors on the analysis results, the data vectors z1i and z2i , where the indices 1 and 2 refer to the two sensors, are transformed into a new coordinate system. Components of the corresponding data vectors x1i and x2i in the new coordinate system are given by the linear or nonlinear combination of the components z11 , ..., z41 and z12 , ..., z42 , respectively. The linear/nonlinear transformation applied is found by employing the canonical correlation analysis (CCA) or kernel canonical correlation analysis (KCCA), respectively. The classical CCA describes linear relations between variables. If we have two data sets Z 1 and Z 2 containing the same number of data points N , the transformation implemented by the CCA is such that correlation between the new variables x11 and x21 given by the linear combination x11 = w1T z1 and x21 = w2T z2 , where T stands for the transpose, is maximized. The new directions w1 and w2 (canonical vectors) are found as a solution to the following eigenvector problems: −1 T 1 2 1 C−1 Z11 CZ12 CZ22 CZ12 w − λ w = 0 −1 T 2 2 2 C−1 Z22 CZ12 CZ11 CZ12 w − λ w = 0

(10) (11)

where CZ11 , CZ12 , and CZ22 are covariance matrices of the corresponding variables. If the rank of CZ12 is p, then p solution triples (λj , wj1 , wj2 ) can be obtained. In the case of KCCA, the data are first projected into a high-dimensional feature space φ : z = (z1 , ..., z4 ) → φ(z) using the kernel trick κ(z, y) = φ(z), φ(y) , where  stands for the inner product and CCA is then performed in the new feature space. In this work, we use the regularized KCCA. In the regularized KCCA case, the jth canonical vectors can be found as a solution to the following eigenvalue problems [13]: (CT1 C1 + γ1 I)−1 CT1 C2 (CT2 C2 + γ2 I)−1 CT2 C1 ψ j = λ2 ψ j

(12)

(CT2 C2 + γ2 I)−1 CT2 C1 (CT1 C1 + γ1 I)−1 CT1 C2 ξ j = λ2 ξj

(13)

where γ1 , γ2 > 0 are the ridge parameters chosen experimentally, I is the identity matrix, and C1 = K(z1 , z1 )A1 , with K being the kernel matrix. The matrix A1 is found using the eigendecomposition K(z1 , z1 ) = VΛVT and is given by the first d columns of VΛ−1/2 . The matrix C2 is found likewise. The kernel canonical variates x1j and x2j are then given by x1j = K(z1 , z1 )A1 ψ j and x2j = K(z2 , z2 )A2 ξ j .

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Experimental Setup

The data acquisition system consists of a commercial PC with a National Instruments Data Acquisition Card (DAQ) and the LabVIEW 7 software. Two assembled optical surface reflection sensors are mounted between the calendering part and the winder, see Fig. 2.

Fig. 2. Reflection sensor design (left) and arrangement (right)

The external encoder signal from the paper machine is used to keep the spatial distance δ = 0.1 mm between adjacent samples, independent of the paper production speed. The sensors, both identically constructed, consist of a red diode laser with a wavelength of 670 nm and an output power less than 5 mW . The light from the laser is directed, at a right angle, to the paper web surface and the reflected light is recorded at an angle of 60o from the angle of incidence. The reflected light is guided by a pair of lenses into an optical fibre and the other end of the fibre connects the sensor to the DAQ, through a high speed photodiode amplifier.

4

The Data Set

The data set used in the experimental investigations has been acquired at a Swedish paper mill and corresponds to 9200 km of newsprint divided into M = 9200 blocks. In this study, we subdivided each block into Nr = 14 frequency regions. The data set available has been randomly divided into the training (50%), validation (25%), and the test (25%) sets. The abnormalities screened were located in the wavelength region of 0.2 − 0.4 mm and 1.6 − 3.2 m.

5

Parameters

The Gaussian kernel κ(xi , xj ) = exp{−||xi − xj ||/σ}, governed by the Gaussian width parameter σ, has been utilized in the kernel novelty detector. The width

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parameter σ was selected from the interval given by 0.1 and 0.9 quintile of the ||xi − xj || statistic. To perform the KCCA, the polynomial kernel has been employed. The polynomial degree d of the kernel has been chosen experimentally and was found to be d = 2. The data modelling approach adopted in this work aims at building a model that categorizes ν percent of the training data points as outliers. Then, in the operating phase, the fact of observing the percentage of outlying data points, significantly exceeding the value of ν, indicates abnormalities in paper formation. Both the apriori process knowledge and cross-validation tests have been used to find a suitable value of ν. The choice of the appropriate value of σ has been based on cross-validation aiming to obtain approximately the same percentage of outliers in both the learning and validation data sets. The polynomial degree and the ridge parameters γ1 and γ2 were found by cross-validation aiming to retain the determined ratio ν of outliers in the validation set and to maximize the correlation between the outputs of detectors processing signals of the two sensors. All the parameters have been determined separately for each frequency region.

6

Results

First, a test has been run to asses the correlation in different frequency regions between the feature values as well as the values of the detector outputs computed using signals from the two sensors. Three cases were explored, namely, without preprocessing (original), preprocessing by CCA, and preprocessing by KCCA. The usefulness of four different feature sets: {E i , Emi }, {E i , Emi , χi }, {E i , Emi , Mi }, and {E i , Emi , χi , Mi } has been investigated. Table 1 presents the values of correlation between the detector outputs computed for the three feature types in the highest frequency region. Table 1. Correlation between the detector outputs in the highest frequency region Features

Feature set

N# features

Correlation

Original CCA KCCA

E i , Emi , χi , Mi E i , Emi , χi , Mi E i , Emi , χi , Mi

4 2 2

0.15 0.30 0.70

In Table 1, the column “Feature set” presents the set of original features providing the highest correlation. The column “N# features” gives the number of original, CCA, or KCCA components providing the highest correlation presented in Table 1. The results of the test computed using data from the lowest frequency region are summarized in Table 2. As it can be seen from Table 1 and Table 2, in both the low and the high frequency regions, the KCCA-based preprocessing of sensor signals results into

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Table 2. Correlation between the detector outputs in the lowest frequency region Features

Feature set

N# features

Correlation

Original CCA KCCA

E i , Emi , χi , Mi E i , Emi , χi , Mi E i , Emi , χi , Mi

4 2 2

0.10 0.25 0.45

the most similar behaviour of detectors analyzing signals coming from the two sensors. Therefore, the KCCA-based preprocessing was utilized in further tests. In the next experiment the behaviour of the detectors in paper formation variation monitoring has been studied. The outputs of detectors analyzing signals coming from the two sensors were aggregated into a committee output via averaging. The usefulness of two- and four-member committees has been explored. In a four-member committee, in addition to the two detectors, the short term moving average of the detector outputs is also utilized. Given the data point xat , the detector operating in the ath frequency region outputs the real valued signal y(xat ) and the binary decision f (xat ). The short term moving average τta is then given by  1 a τta = τt−1 (P − 1) + y(xat ) (14) P with P being the size of the averaging window. In this test, the value of ν has been set ν = 0.05; it is assumed that there are about 5% of outliers in the training data. Since data from the learning, validation and test sets come from the same distribution, it is expected that approximately the same percentage of outliers is also present in the validation and test sets. Table 3 presents the data categorization results obtained from the committees for the learning, validation, and test set data representing the highest (the upper part of the table) and the lowest (the lower part of the table) frequency region. In the table, Committee 2 and Committee 4 stand for a committee made of two and four members, respectively. As it can be seen from Table 3, the committee made of four members provides the lower discrepancy between the data categorization results obtained for the three sets of data. Similar results were obtained for the data recorded from other frequency regions. It is obvious that assessing the data categorization Table 3. The percentage of the highest and lowest frequency region data categorized as outliers Detector Committee Committee Committee Committee

2 4 2 4

Learning set

Validation set

Test set

3.7 4.3 4.5 3.8

5.5 5.0 4.0 4.1

3.0 3.5 3.5 3.6

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results is not an easy task, since the “ground truth” is not known. Therefore, an experiment was conducted using data, where it was apriori known that at some point process disturbances, resulting into more pronounced paper formation variations in some frequency regions, took place. The result of the test, given by the Committee 4 output, is shown in the main window of the software developed. The disturbances manifest themselves in the detector output values steadily exceeding the categorization threshold. The wavelength of the paper formation variations monitored in this experiment ranges from 0.2 to 0.4 mm.

Fig. 3. Committee 4 output in the highest frequency region

Fig. 3 illustrates the user interface of the software developed. The value of ν, the sensitivity level, and the frequency regions to be visualized are the parameters that can be chosen by a user. The sensitivity level defines the degree to which the moving average of the percentage of outliers found is to exceed the ν value for the warning message to be generated.

7

Conclusions

A method and a tool for detecting and monitoring paper formation variations online in various frequency regions has been developed. To increase the robustness of the analysis, two optical sensors recording the reflected light from a paper web have been used to generate the input data. To characterize the region frequency content, four Fourier power spectrum based features are extracted in each frequency region. Having the representation, the task is treated as a kernel based novelty detection. Two detectors (one for each sensor), the outputs of which

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are aggregated into a committee output, are used in each frequency region. The experimental investigations performed have shown that all the four features are useful for obtaining a concise and informative representation of the frequency content. It has been found that the use of a new feature space obtained based on the canonical correlation analysis significantly improves the agreement between the analysis results obtained from two detectors operating in each frequency region. A software implementing the method was developed and used for online paper formation monitoring at a Swedish paper mill.

References 1. Kaestner, A., Nilsson, C.M.: Estimating the relative shrinkage profile of newsprint. Optical Engineering 42(5), 1467–1475 (2003) 2. Verikas, A., Malmqvist, K., Bergman, L., Engstrand, P.: Colour speck counter for assessing the dirt level in secondary fibre pulps. Journal of Pulp and Paper Science 29(7), 220–224 (2003) 3. Bacauskiene, M., Verikas, A.: The evidence theory based post-processing of colour images. Informatica 15(3), 315–328 (2004) 4. Norman, B., Wahren, D.: The measurement of mass distribution in paper sheet using a beta radiographic method. Svensk Papperstidning 77(11), 397–406 (1974) 5. Trepanier, R.J., Jordan, B.D., Nguyen, N.G.: Specific perimeter: a statistic for assessing formation and print quality by image analysis. TAPPI Journal 81, 191– 196 (1998) 6. Bouydain, M., Colom, J.F., Navarro, R., Pladellorens, J.: Determination of paper formation by Fourier analysis of light transmission images. Appita Journal 54(2), 103–105 (2001) 7. Turtinen, M., Pietikainen, M., Silven, O., Maenpaa, T., Niskanen, M.: Paper characterisation by texture using visualisation-based training. International Journal of Advanced Manufacturing Technology 22(11-12), 890–898 (2003) 8. Keller, D.S., Lewalle, J., Luner, P.: Wavelet Analysis of Simulated Paper Formation. Paperi ja Puu 81(7), 499–505 (1999) 9. Nesic, Z., Davies, M., Dumont, G.: Paper Machine Data Analysis and Compression using Wavelets. Tappi Journal 80(10), 191–204 (1997) 10. Timberlake, A., Strom, E.: Do You Know What Causes the Variability in the Paper You Produce? In: 2004 Paper Summit, Spring Technical & International Environmental Conference, TAPPI Proceedings (2004) 11. Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004) 12. Ejnarsson, M., Nilsson, C.M., Verikas, A.: A kernel based multi-resolution time series analysis for screening deficiencies in paper production. In: Wang, J., Yi, Z., Zurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3973, pp. 1111–1116. Springer, Heidelberg (2006) 13. Kuss, M., Graepel, T.: The geometry of kernel canonical correlation analysis. Technical Report 108, Max Planck Institute for Biological Cybernetics (2003)

Multi-modal Data Integration Using Graph for Collaborative Assembly Design Information Sharing and Reuse* Hyung-Jae Lee1, Kyoung-Yun Kim2, Hyung-Jeong Yang1,**, Soo-Hyung Kim1, and Sook-Young Choi3 1

Dept. of Computer Science, Chonnam University, 300 Young-Bong, Gwangju, Korea [email protected],{hjyang,shkim}@chonnam.ac.kr 2 Dept. of Industry and Manufacturing Engineering, Wayne State University, Detroit, MI 48202, USA [email protected] 3 Dept. of Computer Education, Woosuk University, Samruy, Chonbuk, Korea [email protected]

Abstract. Collaborative design has been recognized an alternative environment for product design in which multidisciplinary participants are naturally involving. Reuse of product design information has long been recognized as one of core requirements for efficient collaborative product design. This paper addresses integration of multi-modal data using a graph for an assembly design information sharing and reuse in the collaborative environment. In the system, assembly product images obtained from multi-modal devices are utilized to share and to reuse design information. The proposed system conducts the segmentation of an assembly product image by using a labeling method and generates an attribute relation graph (ARG) that represents properties of segmented regions and their relationships. The generated ARG is extended by integrating corresponding part/assembly information. In this manner, the integration of multimodal data has been realized to retrieve assembly design information using a product image. Keywords: ARG, Design Information, CAD/CAM, Image segmentation, Image Retrieval.

1 Introduction Recently, many manufacturing companies have paid attention to collaborative environment to reduce total product development time and to increase efficiency of the product development process [1]. In a collaborative environment, multidisciplinary (e.g., design, marketing, and distribution) stakeholders participate locally and remotely in overall product development processes; therefore, to realize smooth *

This research is partially supported by the Korea Research Foundation Grant funded by the Korean Government (MOEHRD, Basic Research Promotion Fund) (KRF-2006-003-D00511) and by KOCCA as the result of the research project for 2007 C.N.U culture technology development. ** Corresponding author. H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 521–530, 2007. © Springer-Verlag Berlin Heidelberg 2007

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communication in the collaborative environment, an efficient communication protocol should be established. Since a product image is an intuitive data type, it can be used by various stakeholders as a front-end medium. More than 75% of product design activities have been conducted repeatedly due to the lack of product design information/knowledge reuse. Therefore, it has been long recognized as a critical problem in product design [2]. Previous research on design information reuse has been focused on search by matching keyword and file name or search by specific indexes (e.g., part number, relationship among parts, etc.). However, these methods indicate various drawbacks [3]. First, product model information is often incomplete or is not defined detailed enough. Second, it is often not true knowing proper keywords (e.g., project name or part name) before an actual search. Third, search by product relationships may generate too detailed search results or too broad results. Additionally, the previous works use 3D models that are generated by specific CAD systems and it is often difficult for general users who do not know how to use the specific CAD systems. This paper presents an integration of multi-modal data to allow assembly design sharing and reuse while utilizing intuitive and user-friendly 2D image (that is independent to specific CAD tools) and assembly information. For the multi-modal integration between visual properties of images and assembly information, we propose to use a graph as a uniform framework. An image is segmented by a labeling method, and the segmented regions are matched with corresponding part information. The visual properties of region and associated design information are represented in an Attributed Relational Graph (ARG). When a designer wants to reuse certain assembly design, an image of the design (ad-hoc image) is provided to the system as a query, the system conducts a similarity test between the ad-hoc image and images in database. Once a similar image is determined by the similarity test, the design information that is represented in the ARG of the similar image is utilized for assembly design information reuse. The main advantages of this reuse system are following: 1) the system is not dependent to specific CAD systems, because it utilizes multimedia images that can be obtained easily from peripheral devices; 2) the system has shown outstanding search performance, because ARG represents various information of segmented regions and their relationships; 3) Since multi-modal data is represented by using graph, seamless integration is performed for the efficient design information sharing.

2 Related Work Related to product design information sharing, [4][5][6] conduct search by using harmonics-based functions consisted by 3D shapes and signals. They decompose 3D model using spherical function or Fourier function that represents 3D signals. The harmonics-based approaches are efficient computationally; however, these methods cannot differentiate different shapes and show low searching performance. [7][8][9] use 3D object recognition techniques for design reuse and aspect graphs[7], spin images[8], and geometric hashing[9] are applied respectively. These methods require significantly large data storage and high computational cost. Existing research related to image segmentation can be classified into area-based, histogram-based, and boundary-based segmentation. As an area-based segmentation

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method, Blobworld[10] applies smoothing technique to an image and maximizes the difference of pixel values by translating the image to L*a*b color system for image segmentation. This method shows limitations when the difference of pixel values is very small. Normalized-cut[11] uses eigenvalues and eigenvectors, and segment pixel repetitively into two groups. The segmentation results are typically generating very small areas and this method is not adequate for product image segmentation that this paper is targeting. [12] presented method that uses a histogram obtained from an input image and conduct color quantization for the image by using two gray values that have the highest frequency. [13] translates an image into HSV color system and applies smoothing process for image similarity tests. Histogram-based method is often difficult to determine thresholds when the distribution of a histogram is complex. Some researchers presented boundary-based segmentation methods [14, 15]. The method of Jiamp et al. [14] is based on preprocessing that separate the color of an image into each channel (e.g., Y. U. V) and it often take long time.

3 Integration Ad-Hoc Images and Assembly Design Information In this paper, for the assembly information reuse by multi-modal data integration, there are three parts; image segmentation, multi-modal data integration and image retrieval. After an image is segmented by edge-based labeling method, it is represented by Attributed-Relational Graph which represents visual properties of image regions as nodes and relations such as angles and distance as links. ARG is further extended by adding product design information. A query image is also represented a query ARG to be computed similarities against ARG in a database. To reuse design information a user browses the product design information from the retrieved images. 3.1 Part Segmentation Conventional image segmentation approaches group pixels if they are similar in terms of colors, motions, and brightness. In this paper, we perform image segmentation to distinguish parts in an assembly. We applied edge-based labeling which does not require preprocessing as much as the conventional approaches do. Edge-based labeling recognizes parts after extracting edges from an image [16]. Over segmented regions are integrated to be reformed as a part which should be matched to user’s intuition. As shown in Figure 1, AND operation is applied on a given image and an edge image from step 1 to enhance the edges. In the second step, edge-based labeling is applied to segment parts. Edges are pixels whose values are much different from adjacent pixels. In this paper, we employ Sobel mask[17] which is known robust for noses. We modify Sobel mask to reduce time complexity producing binary images while edges are extracted. In other words, after derivations are applied on each pixel, it is distinguished into two which is set as a significant point in the distribution of groups by a threshold value derivation values. Edges are enhanced by AND operation between a given image and

α

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the edge-extracted image. Edge enhanced images must be binarized to distinguish a product and background in an image to be applied by labeling. We applied one cluster method which divides pixels into two clusters according to one average value. Since the image we are targeting show large differences between a region of an assembly and background, one cluster method shows simple and less time complexity than kmeans[18]. First Step

Gray Image

Images

AND Operation

Edge Extraction B/W B/WImage Image

Second Step

Gray Image

Labeling

Segmented Image

Fig. 1. Part Segmentation

Labeling methods recognize regions by tagging same numbers on pixels within an edge. Therefore, pixels in one part have same numbers and each part has a different number. In this paper, we exclude parts whose size is smaller than 1% of an image considering they are not meaningful parts which designers want to reuse. Labeled parts are integrated to be a significant part in terms of slope of a line from a left hand side point to a right hand side point as shown in formula (1). The slope means the ratio of a part. Therefore, two parts in similar slopes tend to be one part which maybe over segmented. We ignore a part if ∆x =0 since it is not a useful part.

Slope(oi ) =

Δy yright − yleft = Δx xright − xleft

(1)

When the slopes of two segmented regions oi, oj hold one condition as follows, they are merged as one part. Condition 1: -1<Slope(oi) and Slope (oj) on X × X is given, where < ⋅,⋅ > denotes the inner product and φ maps the input space X to another high dimensional feature space F. With suitably chosen φ, the given nonlinearly separable samples S may be linearly separated in F, as shown in Fig. 5. Many hyperplanes can achieve the above separation purpose but the SVM used in this paper is to find the one that maximizes the margin (the minimal distance from the hyperplane to each points). The soft-margin SVM, which includes slack variables ξ i ≥ 0 , is proposed to solve non-separable problems. The slack variables

ξ i = max( 0, γ − y i (< w, xi > +b)) , shown in Fig. 6, measure the amount by which the training set fails to have margin γ, and take into account any misclassification of the training data. Consequently, the training process tolerates some points misclassified

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and is suitable in most classification cases. The kernel function of exponential radial basis function (ERBF) that is defined as K ( x, x ) = exp(− | x − x | / 2σ 2 ) where parameter σ2 is the variance of the Gaussian function is used in this paper. The parameter σ2 is set to be 60.

margin γ

hyperplane H (x )

ξi

Fig. 6. The margin and the slack variable for a classification problem

5 Experimental Results This paper selects 320 different sentences from the Aurora database [11] for evaluating the VAD performances. In which 160 sentences are used for SVM training and the remaining 160 sentences are applied for VAD testing. Furthermore, three types of noise, i.e., train station, street, and car, with 5 different SNRs are added to the test speech signal to simulate noisy environments. The original speech signals in Aurora database were sampled at 8000 Hz with 16-bit resolution. Each sound is divided into frames with 256 samples (32ms) frame length and 128 samples (50%) overlap between adjacent frames. The software simulations were performed using Matlab® 7.0 on a Pentium® IV 2.0, Windows® XP PC. In this paper, the probabilities of detection Pd and false-alarm Pf for a number of noisy speech signals are utilized to evaluate the VAD performances. To obtain Pd and Pf, the active and inactive regions of the clean speech signals are first marked manually. Pd is calculated as the percentage of test cases when the hand-marked speech regions are correctly detected by the VAD algorithm while Pf is the percentage of test cases when hand-marked noise regions are erroneously identified as speech. Using three types of noise with different SNRs, the Pd and the Pf of the proposed VAD algorithm are compared with those of the VAD Option 1 and Option 2 of AMR codec [1]. These experimental results are summarized in Table 1.

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Table 1. Pd’s(%) and Pf’s(%) of the proposed VAD, AMR Option 1 and Option 2 VAD under various noisy environments

Noise SNR Clear 20dB 15dB 10dB 5dB 0dB Noise SNR Clear 20dB 15dB 10dB 5dB 0dB Noise SNR Clear 20dB 15dB 10dB 5dB 0dB

Train station Pd Pf 99.61 98.43 98.13 97.78 97.23 96.12

5.86 6.17 7.81 8.48 9.48 9.87

Proposed VAD Street Pd Pf -99.15 98.97 98.14 97.38 94.32

-8.24 8.48 8.94 9.09 9.36

AMR VAD Option 1 Train station Street Pd Pf Pd Pf 99.44 99.30 98.66 97.84 96.13 93.72

11.42 16.82 25.24 32.14 45.34 57.47

-98.18 98.28 96.84 95.82 94.42

-26.44 31.76 33.25 40.87 49.15

AMR VAD Option 2 Train station Street Pd Pf Pd Pf 99.76 88.37 84.37 90.93 96.79 96.39

11.32 19.69 20.02 22.56 30.45 39.57

-78.74 82.78 88.73 94.96 98.46

-19.36 18.72 19.18 27.27 40.37

Car Pd

Pf

-98.83 98.24 97.54 95.62 90.71

-7.83 8.51 8.82 9.31 9.73

Car Pd

Pf

-98.46 97.45 96.20 94.78 94.08

-20.01 21.32 39.87 54.53 54.32

Car Pd

Pf

-83.96 86.42 93.58 95.69 98.09

-22.18 23.29 25.18 30.35 41.42

From Table 1, one observes that although the probabilities of detection Pd of AMR VAD Option 1 and Option 2 are fine, their probabilities of false-alarm Pf are somewhat unsatisfied. This means that most noisy sounds have been classified as speech frames using AMR VAD Option 1 and Option 2. The proposed VAD scheme provides a good alternative to these standardized algorithms. In addition to the well average probabilities of detection, the proposed VAD scheme has lower probabilities of false-alarm Pf than all the mentioned standardized algorithms over a variety of noise environments.

6 Conclusion An improved VAD algorithm based on the wavelets and SVMs is proposed in this paper. By the use of wavelet transform, the signal level at each sub-band can be calculated. In addition, the background noise can be estimated in each sub-band via

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the wavelet de-noising method with SURE thresholding. The wavelet filter bank is also derived to detect correlated complex signals like music. Then one applies SVM with ERBF kernel function to train an optimized non-linear VAD decision rule involving the sub-band power, noise level, pitch period, tone flag, and complex signals warning flag of input speech signals. It is shown in this paper that the trained SVM offers accurate VAD under noisy environments. Various experimental results reveal that the proposed VAD algorithm performs better than those of VAD Option 1 of AMR and is comparable with the AMR VAD option 2. Moreover, the proposed VAD algorithm can operate reliably in real noisy environments.

References 1. 3GPP TS 26.094 V6.1.0, Voice Activity Detector (VAD) for Adaptive Multi-Rate speech codec (2006) 2. Ramírez, J., Segura, J.C., Benítez, C., de la Torre, Á., Rubio, A.J.: A New KullbackLeibler VAD for Speech Recognition in Noise. IEEE Signal Processing letters 11(2), 266– 269 (2004) 3. Garner, N.R., Barrett, P.A., Howard, D.M., Tyrrell, A.M.: Robust noise detection for speech detection and enhancement. Electron. Lett. 33(4), 270–271 (1997) 4. Vapnik, V.N.: Statistical Learning Theory. Wiley, Chichester (1998) 5. Lin, C.-C., Chen, S.-H., Truong, T.K., Chang, Y.: Audio Classification and Categorization Based on Wavelets and Support Vector Machine. IEEE Trans. on Speech and Audio Processing 13(5), 644–651 (2005) 6. Donoho, D.L., Johnstone, I.M.: Adapting to unknown smoothness via wavelet shrinkage. Journal of the American Statistical Association 90, 1200–1224 (1995) 7. Stein, C.: Estimation of the mean of a multivariate normal distribution. Annals of Statistics 9(6), 1135–1151 (1981) 8. Mallat, S.: Multifrequency channel decomposition of images and wavelet model. IEEE Trans. Acoustic, Speech and Signal Processing 68, 2091–2110 (1980) 9. Chen, S.-H., Wang, J.-F.: Noise-robust pitch detection method using wavelet transform with aliasing compensation. IEE Proc. Vision, Image and Signal Processing 149(6), 327– 334 (2002) 10. Hu, Y., Loizou, P.C.: Speech enhancement based on wavelet thresholding the multitaper spectrum. IEEE Trans. on Speech and Audio Processing 12(1), 59–67 (2004) 11. Aurora 2 Database (2000) http://www.elda.org/article52.html

The PICA Framework for Performance Analysis of Pattern Recognition Systems and Its Application in Broadcast News Segmentation Xiangdong Wang1,2 , Meiyin Li1,2 , Shouxun Lin1 , Yueliang Qian1 , and Qun Liu1 1

2

Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China Graduate University of Chinese Academy of Sciences, Beijing 100085, China {xdwang,limeiyin,sxlin,ylqian,liuqun}@ict.ac.cn

Abstract. In this paper, the performance influencing class analysis (PICA) framework is proposed for performance analysis of pattern recognition systems dealing with data with great variety and diversity. Through the PICA procedure, the population of data is divided into subsets on which the system achieves different performances by means of statistical methods. On basis of the division, performance assessment and analysis are conducted to estimate the system performance on the whole data population. The PICA framework can predict true performance in real application and facilitate comparison of different systems without the same test set. The PICA framework is applied to the analysis of a broadcast news segmentation system. The procedure is presented and experimental results were given, which verified the effectiveness of PICA. Keywords: performance analysis, PICA, PIFA, pattern recognition, speech recognition, broadcast news segmentation.

1

Introduction

In the field of pattern recognition, the variance and diversity of input data poses great challenge to performance assessment, since a system can achieve quite different performances on different test data. This makes it difficult to assess the overall performance of a system considering all possible test data and to compare the performances between different systems. As a resolution, the evaluation scheme is popular adopted, in which test database is collected with variability in some basic data properties (e. g. speaker gender for speech data) and all systems are tested with the same data. Though this makes comparison possible, the performances obtained are still highly dependant on the test data. In most evaluations [1,2,3] and other researchers work, the test data are collected randomly or arbitrarily despite the coverage of some basic data properties, so they are not representative of all data and the performances are not representative, either. In this paper, we present the Performance Influencing Class Analysis (PICA) framework of performance assessment for applied pattern recognition systems. It H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 925–934, 2007. c Springer-Verlag Berlin Heidelberg 2007 

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aims to estimate the overall performance on the set of all possible input data (the population) for a given task, making the performance assessment more reliable and performance comparison between different systems feasible. The main idea of PICA is to divide the data population into some subsets, and estimate the performance on the data population with performances on the subsets and the proportions of each subset in the data population. The rest of the paper is organized as follows. In Section 2, the basic logic and procedure of PICA are presented. In Section 3, we describe the application of PICA to the performance assessment of a broadcast news segmentation system. Experimental results and analysis for the application in broadcast news segmentation are given in Section 4. Finally, conclusions are drawn in Section 5.

2 2.1

The Framework of PICA The Basic Logic of PICA

To further explore the PICA framework, some terms are proposed as follows. Population: the set of all possible input data for a specific task, denoted by Ω. Each element of the population is a basic unit of data, which can be decided according to the features of data and the task performed. For example, for a speech recognition task, the basic unit of data may be decided as one utterance. Data property: the feature or characteristic of each basic unit of data, e. g. the gender of speaker, or the signal-noise ratio for the speech recognition task. For a basic unit of data d, its data properties are denoted as d.Pi , where i = 1, 2, . . . Levels of a data property: values or classes of a data property. In PICA, only discrete levels are used, so levels of data properties with continuous values are decided by dividing the value domain into intervals. We defined that for basic unit of data d, d.Pi refers to the level of the data property instead of the value. Performance metric: a value as the measurement of performance. For example, word error rate for the continuous speech recognition task. PIF and PIC. The main idea of PICA is to divide the population into subsets satisfying that performances are significantly different on different subsets and close on data in the same one. These subsets are referred to as performance influencing classes (PICs). To achieve the division, the method of ANOVA (analysis of variance) [4,5] is introduced from statistics, which is a powerful method of hypothesis test. For data with n data properties, when not considering interaction between data properties, the statistical model of ANOVA is written as Fl1 l2 ...ln m = μ +

n 

τli + εl1 l2 ...ln m

(1)

i=1

where li (i = 1, . . . , n) stands for a level of the ith data property, Fl1 l2 ...ln m denotes the performance metric value on the mth data with specific data property levels, τli denotes the effect of the li level on the performance, and ε denotes

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experimental error. The purpose of ANOVA is to test statistically that for a data property with K levels, whether the following hypothesis is accepted or rejected. τ1 = τ2 = . . . = τK = 0

(2)

Definition 1. If for a data property, the hypothesis in (2) is rejected through ANOVA, the data property is called a performance influencing factor (PIF). Definition 2. For a PIF Pi , if for each two levels l1 , l2 of it, τl1 = τl2 is rejected through ANOVA, then Pi is called a level-complete PIF. Only PIFs are considered when dividing the population into PICs, since other data properties bring no significant difference in performance. Though not all PIFs are level-complete ones, in practice, most PIFs can be modified to be levelcomplete PIFs by adjusting the definition of levels. Theorem 1. Let Pi be a level-complete PIF, whose levels are l1 , l2 , . . . , lK , and set Aj is defined as Aj = {d|d.Pi = lj , d ∈ Ω}, j = 1, 2, . . . , K, then Si = {Aj , j = 1, 2, . . . , K} is a partition of Ω. Proof. Si is a partition of Ω because Aj , j = 1, 2, . . . , K, satisfying Aj1 ∩ Aj2 = Φ, j1 = j2 , and

K 

Aj = Ω

(3)

j=1

  Definition 3. The partition Si = {Aj , j = 1, 2, . . . , K} in Theorem 1 is called the performance influencing partition of Ω for Pi , and Aj ∈ Si , j = 1, 2, . . . , K is called a performance influencing class (PIC) for Pi . Definition 4. Let P1 , P2 , . . . , Pn be level-complete PIFs, whose performance influencing partitions are S1 , S2 , . . . , Sn , then the product of the partitions S = S1 · S2 ·. . .·Sn is called the performance influencing partition of Ω for P1 , P2 , . . . , Pn , and each B ∈ S is called a performance influencing class for P1 , P2 , . . . , Pn . It can be seen from the definitions that when many data properties are considered, the performances on different PICS are quite likely to be different due to different levels of PIFs. And when enough data properties are considered and not too few data are used, performances on subsets in the same PIC are likely to be similar for few factors may influence the performance in a PIC. Estimation of Performance on the Population. Once the PICs are determined, performance metric value on each PIC can be obtained by testing the system using corresponding data. These performances as a whole can give more information than simply test the system using a randomly selected test set. But sometimes, only one metric value is needed to represent the overall performance on population or to compare with other systems. In the following theorem, it is proved that for metrics such as precision or error rate, the metric value on the population equals to a weighted sum of metric values on all PICs.

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Theorem 2. Assume that for a data set D, a performance metric is defined as R = f (D)/q(D)

(4)

where f and q are functions of D, satisfying that for two data sets D1 , D2 , f (D1 ∪ D2 ) = f (D1 ) + f (D2 ), q(D1 ∪ D2 ) = q(D1 ) + q(D2 ), if D1 ∩ D2 = Φ (5) Then for a partition S = {A1 , A2 , . . . , An } of the data population Ω, letting Ri be the metric for the subset Ai , the following holds true. R=

n 

ci Ri , where ci = q(Di )/q(Ω), i = 1, 2, . . . , n

(6)

i=1

Proof. According to (4), for each subset, we nhave Ri = f (Di )/q(Di ), and acn cording to (5), f (Ω) = f (D ), q(Ω) = = q(Ω), we have i i=1 i=1 q(Di ). Let n n Q n R = f (Ω)/Q = [ f (D )]/Q = [ R q(D )]/Q = i i i=1 i=1 i i=1 [q(Di )/Q]Ri = n c R   i=1 i i In fact, in most metrics used in the pattern recognition area, the function q(D) in the above theorem usually stand for the amount of data, such as the number of basic units of data or the whole duration of speech. So the proportion q(Di )/Q stands for the proportion of amount of Di in the population. Design of Test Data. For performance metrics that do not satisfy (4) or (5), there is a more direct way for estimating overall performance on the population. That is, to design and collect a test set in which the proportion of each PIC is equal to that in the population. For cases that levels of all PIFs can be controlled when collecting data, this can be easily done. However, for most cases, the data are just collected with little control, so a selection approach is proposed, as described in the following. Let Ω be the data population for a specified task. Suppose that there are K PICs. If there are n sets of data already collected, denoted by D1 , D2 , . . . , Dn . The data amount of Di is Ni , the proportion of the j th PIC in Di is aij , and the proportion of the j th PIC in the population is bj . So the problem can be described as forming a test data set D of data amount N by selecting sets from D1 , D2 , . . . , Dn , satisfying that the proportion of PICs are most close between D and Ω. The Euclid distance is used as the measurement of similarity between the proportions of PICs in D and Ω, so this can be transformed into the problem of finding X0 , X1 , . . . , Xn that minimizes K n  1  aij Ni Xi − bj )2 )]1/2 d(D, Ω) = [ ( N j=1

under the restriction of n 

(7)

i=0

Ni Xi = N, Xi ∈ {0, 1}, i = 1, . . . , n

(8)

i=0

This is a problem of integer programming and can be solved using classical algorithms such as the branch and bound method [10].

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The Procedure of PICA

The whole procedure of PICA is shown in Figure 1.

Fig. 1. The procedure of PICA

First, the data population should be determined according to the task, which includes deciding the basic unit of data and the coverage range of the population. Once the population is fixed, data properties under examination should be decided because it is impossible to study all of them. Then the framework of PIFA [8] is incorporated into PICA to select PIFs from the data properties. The main idea of PIFA is to design the experiments using orthogonal design and test for significant differences of performance between levels using ANOVA. After the PIFs are chosen, their levels may be slightly adjusted to become level-complete PIFs. Then, PICs for each PIF and all PIFs are derived according to Defination 3 and 4. The product of multiple partitions is computed as [9] S = S1 · S2 · . . . · Sn = {

n  i=1

Ai |

n 

Ai = Φ, Ai ∈ Si }

(9)

i=1

n n To decide whether i=1 Ai = Φ, all i=1 Ai are maintained as potential PICs, and after the proportions of all potential PICs are estimated, the sets with proportion less than a threshold will be eliminated as approximate null set. As for estimating the proportions, information from other researchers may be useful. If such information is not available, the sampling method can be used to draw a sample from the population and compute the proportions in the sample set as substitution. Sampling theory supports that if the sampling method is appropriate, the sample set may be good miniature of the population [6,7]. When PICs are determined and their proportions are know, an overall performance metric is calculated as estimation of performance on the population. There are two ways for such assessment—-estimating the metric on the population using (6) or designing a test set by solving (7) and (8).

3

Application of PICA to Broadcast News Segmentation

To verify the effectiveness of the PICA, we applied it to the performance assessment of a broadcast news segmentation system. The aim of broadcast news segmentation is to segment the audio stream into homogeneous regions (referred to

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as segments) according to speaker identity, environmental condition and channel condition. It can also be seen as detection of change points which are the positions in the audio where change occurs in speaker identity or recording conditions. Considering the cost of experiment, the system task is narrowed to segmentation of broadcast news from one radio channel (China National Radio). Segmentation result of the system is compared to the reference to yield performance metrics of rejection rate (RJ) and false alarm rate (FA), as defined in the following, where Nmiss , Nf a , and Nref denote the number of missed, false alarm and reference change points. RJ = Nmiss /Nref

(10)

F A = Nf a /(Duration of the audio stream)

(11)

The PICA framework is applied respectively to RJ and FA. Since the procedures are quite similar, only details for the FA metrics are described in this paper. Determination of Population. First, decision should be made about the basic unit of data. In our work, when investigating false alarm rate, each segment (speech between two change points) is considered as a basic unit of data, since data properties such as recording condition usually keep the same within one segment and varies much between different ones. Then, population is defined as the set of all segments in broadcast news from China National Radio. Selection of PIFs. Once the basic unit of data is chosen, data properties of a unit are also determined. Since there could be innumerous data properties, only those that may influence system performance and can be measured in practice are involved in the selection of PIFs. In our work, data properties about the speaker, recording condition and channel condition are chosen, and their levels are decided, as listed and explained in table 1. When selecting PIFs from these data properties, the PIFA (performance influencing factor analysis) framework [8] is adopted. In our work, because the data properties such as speaker gender are only related to speech segments, a hierarchical approach is utilized: The two levels of the data property ”content” is analyzed first using a 1-way ANOVA, and the orthogonal design (the orthogonal table L8 (27 ) is adopted) is used for the other data properties. Then a data set (referred to as the PIFA set ) of 6 hours is collected and used as input of the segmentation system. With the result and reference, statistical data of performance are generated for each group according the method presented in [8], the main idea of which is to divide the group into subgroups and consider metric on each subgroup as one observation. The results of ANOVA shows that content, speech background and speech scene influence system performance significantly (Pr < 0.05). So three PIFs are selected, which is content, background, and speech scene. Derivation of Potential PICs. When PIFs are determined, PICs can be derived according to (9). As mentioned in section 2.2, potential PICs are first derived and tested later for whether it can be eliminated as a null set. For our work, the potential PICs are listed in table 2.

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Table 1. Data properties and their levels Data property Level Speech Content Non-Speech Male Speaker gender Female Yes Speaker accent No Planned Speech mode Spontaneous Yes Background No Studio Speech scene Live

Explanation Speech by human Music, noise, etc. Male speaker Female speaker With dialectal accent Without dialectal accent Reading planned text Speaking spontaneously Speech with music or noise Speech in silent environment Speech in studio Speech in open environment

Table 2. Potential PICs and their proportions 1 2 3 4 5

Content Speech Speech Speech Speech Non-speech

Background No Yes No Yes —

Speech scene Studio Studio Live Live —

Proportion 0.566 0.028 0.247 0.083 0.060

Estimation of Potential PIC Proportions. Since no information of the potential PICs is available from other researchers, the approach of sampling is adopted, as explained in section 2.2. In our work, the sampling frame is defined as all broadcast news from China National Radio in 2005, and sampling unit is defined as one section. A procedure similar to stratified sampling [6,7] is performed: the sampling frame is divided into 12 strata according to the month (from Jan. to Dec.) and broadcast news of 2 hour is draw from each stratum using simple random sampling method. Notice that what is needed for these data is the information of the duration of the PICs, so full speech data is unnecessary. So with the sample data of 24 hours, the proportion of a PIC is calculated as the quotient of the duration of all segments in the PIC and the total duration of the sample set. The result is shown in table 2. Since the proportion of the 2nd potential PIC is less that 0.05 which is the threshold in our work, so it is eliminated. Therefore, there are only 4 PICs and the proportions are re-estimated, as shown in part of table 3. Performance Assessment. Once the PICs are fixed, the system is tested with a test set of 2 hours in which total duration of each PIC is about 30 minutes and the FA metric is calculated for each PIC. The results are shown in Table 3. From the definition of FA, it can be seen that (6) is suitable. So FA metric on the population is estimated using (6), and the result is 3.213.

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Content Background Speech scene Proportion FA Speech No Studio 0.592 2.348 Speech No Live 0.258 4.313 Speech Yes Live 0.087 4.944 Non-speech — — 0.063 4.427 Estimated FA on population = Proportion(i)*FA(i) = 3.213

The method of test data designing is also used. A data set of 30 hours are divided into 90 clips each with the duration of 10 minutes, from which 12 clips are selected according to (7) and (8) resulting in a test set of 2 hours. The proportions of PICs in the selected data set are shown in Table 4. The system is tested with this set, and the FA metric obtained is 3.115, which is close to the estimation in Table 3. Table 4. Details of the selected test set PIC Proportion in population Proportion in the selected set d(D, Ω) FA

4

1 0.592 0.586

2 3 0.258 0.088 0.251 0.081 0.0014 3.115

4 0.063 0.060

Experiments and Analysis

Test of PICs. It is expected that performance be similar when on data within the same PIC, and be significantly different on data from different PICs. So for each PIC, we collected 10 data sets of 20 minutes, and for comparison, ten random sets are also selected randomly without any consideration of PIC. The FA metrics were calculated on all 5 data sets, as shown in Figure 2(a). The result indicates that performance is consistent in one PIC and varies much for different ones. And for random sets with the same size, performance also varies much, which implies that testing the system using one or few random sets is unreliable. Test of Performance Assessment. The main advantage of PICA is that it can achieve performance approximate that on the population. So we test the system using 5 difference test sets: the test set designed and the sample set described in Section 3, and 3 random selected test sets. Sizes and FAs for those test sets are listed in Table 5, which shows that performance on the test set designed is most similar to that on the sample set of larger size, while metrics on sample sets varies considerably for different sets. It is also favorable that the FA value estimated in Table 3 is quite close to the metric on the sample set, which means the estimation is reliable, too.

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Fig. 2. Experiments results: (a) Result of test of PICs; (b) Result of test of performance assessment Table 5. Result of experiment on different test sets Size FA

Designed set 2 hrs 3.115

Sample set 24 hrs 3.223

Random set1 2 hrs 2.933

Random set2 2 hrs 3.318

Random set3 2 hrs 3.581

Figure 2(b) shows the result of another experiment: the system was tested using 10 test sets, each is of 2 hours. For each test set Di , the PIC proportion distance between Di and Ω is calculated as the Euclid distance, and distance of FA is calculated as FA distance for Di = |(FA on Di ) − (FA on Ω)|

(12)

The figure indicates that the less the distance Di , the closer the performance on Di to the performance estimated for the population, implying that when the distance is small enough, the performance on the designed test set is close to that on population.

5

Conclusions

In this paper, the PICA (performance influencing class analysis) framework is presented. Under the framework, performance on the population of all possible data is estimated to analyze the system. By means of the analysis, difference in performance caused by different test data can be avoided, performance in real application can be predicted, and comparison between different systems tested with different data can be easily realized. Also in this paper, the application of the PICA framework to the analysis of a broadcast news segmentation system was described. The whole procedure was presented and experimental results were given, which verified the effectiveness of PICA. Actually, the PICA framework can be applied to any pattern recognition task with complex input data. So in the future, we are planning to apply PICA in other task of speech recognition and wider fields.

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References 1. Vandecatseye, A., et al.: The COST278 pan-European Broadcast News Database. In: Procs. LREC 2004, Lisbon, pp. 873–876 (2004) 2. Paul, D.B., Baker, J.M.: The Design for the Wall Street Journal-based CSR Corpus. In: Proceedings of Second International Conference on Spoken Language Processing, pp. 899–902 (1992) 3. Pearce, D., Hirsch, H.-G.: The Aurora Experimental Framework for the Performance Evaluation of Speech Recognition Systems under Noisy Conditions. In: Proceedings of 6th International Conference on Spoken Language Processing, pp. 29–32 (2000) 4. http://www.statsoft.com/textbook/stanman.html 5. Shen, Q.: SAS Statistical Analysis, pp. 84–107. Higher Education Press, Beijing (2005) 6. Renssen, R.: A Course in Sampling Theory. http://www.cs.vu.nl/∼ stochgrp/aionetwerk/course.doc 7. Friel, C.M.: Sampling Theory. http://www.shsu.edu/∼ icc cmf/cj 787/research11.doc 8. Wang, X., Xie, F., et al.: DOE and ANOVA based Performance Influencing Factor Analysis for Evaluation of Speech Recognition Systems. In: ISCSLP, Proceedings (companion volume), pp. 431–442 (2006) 9. Jiao, Z.-y., Hu, Y.-p.: Quotient Set and Fundamental Operation of Quotient Set. Journal of Xi’an University of Science and Technology 24(3), 372–375 (2004) 10. Kaufmann, A.: Pure and Mixed Integer Programming (1997)

The Theory of Maximal Social Welfare Feasible Coalition Laor Boongasame1, Veera Boonjing2 , and Ho-fung Leung3 1

Department of Computer Engineering, King Mongkut University of Technology Thonburi, Bangkok, Thailand 2 Department of Mathematics and Computer Science, King Mongkut’s Institue of Technology Ladkrabang, Bangkok, Thailand 3 Department of Computer Science and Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong, P.R. China [email protected],[email protected],[email protected]

Abstract. This paper proposes a new theory for forming a maximumvalue-cooperation coalition known as the Maximal Social Welfare Feasible Coalition. This theory can give such solution because it does not assume that each player requesting to join a coalition knows information of other players. However, all players’ private information requesting to join the coalition is known by an honest coordinator. This allows the coordinator to select a coalition structure with maximal value of cooperation among successful players so as they get at least at their required minimum values. Not only this maximal value is shown to be equal to or larger than the value of a core coalition but also the value allocation is shown to be Pareto optimal. Keywords: cooperative game theory, coalition formation, Pareto optimality, the core, group buying.

1

Introduction

Cooperative game theory, with solution concepts such as the core [2], the Shapley value [4] , the bargain set [1], the private core [6], and the fine core [5], is one important branch of n-person game theory which deals with situations where the players in a coalition can make binding agreements. The primary aim of the theories is to investigate the conditions under which players in a coalition receive sufficiently large payoffs that they will not break away from the coalition. This means that they seek for a coalition maximizing payoffs of successful players. Therefore, they might not give a maximum-value-cooperation coalition. This paper proposes a new theory of Maximal Social Welfare Feasible Coalition for forming a maximum-value-cooperation coalition. The theory assumes that each player in a coalition does not know the private information of other H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 935–942, 2007. c Springer-Verlag Berlin Heidelberg 2007 

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players. The assumption agrees with the situation of forming a coalition in the real world environment, i.e., each player in the coalition has some private information and is not assumed to exchange his private information with other players. However, all players’ private information requesting to join the coalition is known by an honest coordinator. This allows the coordinator to select a coalition structure with maximal value of cooperation among successful players so as they get at least at their required minimum values. The value of this coalition is known to be equal to or larger than the core coalition formed using the core theory. The rest of paper is organised in four sections. Section 2 describes the core theory. Section 3 elaborates the maximal social welfare feasible coalition theory. Section 4 evaluates the theory. Lastly, section 5 concludes the paper.

2

The Core Theory

The core theory is one of the most popular solution concepts. The coalition structure, called C, can be found as follows. Let N = {1, 2, ..., n} be a set of players, C ⊆ N be a subset of players in N , T v(|C|) be the value of cooperation among players in a coalition C, T v(|C|) be the value of each player in a coalition |C| C which is an ascending function of the number of players in the coalition  C, mvi be the required minimum value of player i, and T s(|C|) = T v(|C|) − i∈C mvi be the surplus of cooperation among players in a coalition C. Then, let  β = {C ⊆ N : T v(|C|) ≥ i∈C mvi },   χ = {C ∈ β : T s(|C|) ≥ T s(|C |), ∀C ∈ β},    = {C ∈ χ : |C| ≥ |C , ∀C ∈ χ} Example 1. The purchasing office of a university obtains a discount on items purchased for all departments in the university. Consequently, when any departments in the university order the same item, the purchasing office combines these orders to purchase the item with a larger discount. Suppose some departments in the university want to purchase computers of a particular model. The price schedule for the particular model of computer is shown in Table 1. For instance, if three or four units are purchased, the unit price is $900 each. The departments’ reservation prices, that is, the maximum price which a department is willing to pay for a unit of the particular model of computer, are shown in Table 2. For instance, the Economics Faculty wants to purchase a unit of the computer at $921 or less. Let U = {u1 , u2 , ..., uk } be the set of departments in the university. A coalition C is a subset of the departments C ⊆ U which can join together to purchase computer of a particular model with a larger discount. Rk is the reservation price of department uk . P (|1|) is the price that one needs to pay when he buys only one unit of the particular model of computer and P (|C|) is the unit price of the particular model of computer when the computers are sold to the departments in coalition C. Let RDi = P (|1|) − Ri be the required minimum discount of

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Table 1. The price schedule for a particular model of computer Number of units sold Unit price ($) 1 940 2 920 3-4 900 5-6 890 7-8 880 9-10 870

Table 2. Departments’ reservation prices of this example Department Economics Faculty (Econ) Library (Lib) Computer Center (Com) Business Faculty (Bus) Engineering Faculty (Eng)

Rk $921 $920 $899 $879 $870

RDk $19 $20 $41 $61 $70

Table 3. The total discount and the total required minimum discount of any subsets in these departments in this example Coalition {Econ,Lib,Com,Bus,Eng} {Econ,Lib,Com,Bus} {Econ,Lib,Com} {Econ,Lib} {Econ}

T D(|C|) (940 − 890) × 5 = 250 (940 − 900) × 4 = 160 (940 − 900) × 3 = 120 (940 − 920) × 2 = 40 (940 − 940) × 1 = 0



 RDi T D(|C|) − i∈C RDi 211 250-211=39 141 160-141=19 80 120-80=40 39 40-39=1 19 0-19=-19

i∈C

department i, and T D(|C|) = (P (|1|) − P (|C|)) × |C| be the total discount of coalition C. We define β, χ and  of this example based on the core theory.  β = {C ⊆ U : T D(|C|) ≥ i∈C RDi },     χ = {C ∈ β : T D(|C|) − i∈C RDi ≥ T D(|C |) − i∈C  RDi , ∀C ∈ β}, and    = {C ∈ χ : |C| ≥ |C |, ∀C ∈ χ} The coalition C is found as follows. First, we find the set β which is {{Econ, Lib}, {Econ, Lib, Com}, {Econ, Lib, Com, Bus}, {Econ, Lib, Com, Bus, Eng}}. Then, we find the set χ which is {{Econ, Lib, Com}}. The total discount and the total required minimum discount of any subsets in these departments are shown in Table 3. Finally, we find the set  which is {{Econ, Lib, Com}}. In summary, the number of departments in the coalition C is 3, and the purchasing office saves $120 = ($940 - $900) ×3 in total for the university.

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The Maximal Social Welfare Feasible Coalition Theory Basic Terms

Let N = {1, 2, ..., n} be a set of players, and C ⊆ N be a subset of players in N . Definition 1: The value of each player in a coalition C is an ascending func1 |) 2 |) tion of the number of players in the coalition C. That is, T v(|C ≥ T v(|C if |C1 | |C2 |

1 |) 2 |) |C1 | > |C2 |, and T v(|C = T v(|C if |C1 | = |C2 | where T v(|C|) be the value of |C1 | |C2 | cooperation among players in a coalition C.

Definition 2: Let T v(|C|) be the value of cooperation among players in a coalition  C. An Allocation of Value in Coalition C is defined as (vi )i∈C such that i∈C vi ≤ T v(|C|), where vi is the allocated value of player i. Definition 3: Let vi be the allocated value of player i. (vi )i∈C is a Feasible Allocation if ∀i ∈ C(vi ≥ mvi ), where mvi ≥ 0 is called the required minimum value of player i. Definition 4: Let T v(|C|) be a value of cooperation among players in a coalition C, mvi be the required minimum value of player i. The surplus of cooperation among players in a coalition C is defined as T s(|C|) = T v(|C|) − i∈C mvi . 3.2

The Maximal Social Welfare Feasible Coalition Theory

In conventional coalitional game theory, the core [2] is one of the most important solution concepts. This theory selects a coalition that gives the maximal surplus of cooperation among players in a coalition C to give incentive for players not to leave the coalition if each player in a coalition completely knows all other players’ information. However, the assumption is seldom practical in forming a coalition in the real word and the solution may decrease the value of cooperation among players in the coalition. We define that a coalition is a maximal social welfare feasible coalition if (1) the value of cooperation among players in the coalition is more than or equal to the sum of the required minimum values of players in the coalition, (2) the coalition is the largest among such coalitions, and (3) the surplus of cooperation among players in the coalition is the highest among all these largest coalitions. We do not make the assumption that each player has complete information of all other players. Instead, we assume that each player’s private information is not known to other players. Since all players’ private information in the coalition is known by an honest coordinator and each player does not know other players’ information, a coalition structure that give maximal value of cooperation among players is selected by the honest coordinator. Without knowing other players’ private information, each player is satisfied with a payoff that is higher than his minimum required value. The maximal social welfare feasible coalition is thus one in which there are maximum number of players receiving a satisfactory payoff in a Pareto optimal manner. An outcome

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of players in a coalition is Pareto optimal if there are no choices of outcome which can make at least one player in the coalition better off, without making any other player worse off [3]. Definition 5: Let mv ⊆ i be the required minimum value of player i, ρ = {Cmax  N : T v(|Cmax |) ≥ i∈Cmax mvi }, λ = {Cmax ∈ ρ : |Cmax | ≥ |Cmax |, ∀Cmax ∈   ρ}, and γ = {Cmax ∈ λ : T s(|Cmax |) ≥ T s(|Cmax |), ∀Cmax ∈ λ}. Then, Cmax is a maximal social welfare feasible coalition if and only if Cmax ∈ γ. We show existence of a Pareto optimal feasible payoff allocation of a coalition in this theory. Theorem 1. If γ = ∅ and Cmax ∈ γ, then there exists a Pareto optimal feasible allocation (vi )i∈Cmax of coalition Cmax . Proof. Consider the feasible allocation X = (vi )i∈Cmax = (mv1 , mv2 , ..., mvn−1 , n−1  T v(|Cmax |) − i=1 mvi ). Therefore, X satisfies i∈Cmax vi = T v(|Cmax |) and is consequently Pareto optimal. To prove that X satisfies ∀i ∈ Cmax , vi ≥ mvi , n−1 we only need to prove that T v(|C |) − max i=1 mvi ≥ mvn , or T v(|Cmax |) ≥ n mv . Since C ∈ γ, therefore C ∈ ρ and trivially T v(|Cmax |) ≥ i max max i=1 n mv . i i=1 Theorem 1 shows that the allocation of value in the coalition is a Pareto optimal. Then, we show a concept of a maximal social welfare feasible coalition in selecting members into a coalition. Theorem 2. If D ⊆ N , and D ∈ / ρ, then either there is no Cmax ⊆ D such that Cmax ∈ γ, or there exists Cmax ⊆ D\{Phighest } such that Cmax ∈ γ where Phighest = argmaxi∈D mvi . Proof. Let mvhighest ≥ mvhighest−1 ≥ ... ≥ mvlowest−1 ≥ mvlowest be the required minimum values of players Phighest , Phighest−1 , ..., Plowest−1 , Plowest in D,   respectively. Since D ∈ / ρ, therefore, (T v(|D|)− i∈D mvi ) < 0, or i∈D ( T v(|D|) |D| − mvi ) < 0. The term ( T v(|D|) − mvhighest ) on the left hand side of the inequal|D|

ity is the smallest and must be negative. Denote D(−1) = D\{Phighest }, then we (−1)   T v(|D\{Pj }|) |) − mvi ) ≥ ) − mvi , ∀j ∈ find that i∈D(−1) ( T v(|D i∈D\{Pj } ( |D|−1 |D(−1) |  (−1) has the highest T v(|D|) − i∈D mvi among all subsets D; that is, the set D of D of size |D| − 1. Therefore, D(−1) ∈ λ if and only if D(−1) ∈ γ. Next, if  T v(|D (−1) |) − mvi ) is less than zero and D(−1) = ∅, then we repeat this i∈D(−1) ( |D(−1) |

process until we find the smallest k such that the subset D(−k) ⊆ D, of size |D| − k,  (−k) satisfies (T v(|D |) − i∈D(−k) mvi ) ≥ 0; or D(−k) = ∅. Note that for each    k = 1, 2, ..., k, the set D(−k ) has the highest T v(|D|) − i∈D mvi among all sub





sets of D of size |D| − k . Therefore D(−k ) ∈ λ if and only if D(−k ) ∈ γ. If we find the smallest k such that the nonempty subset D−k ⊆ D that satisfies  (−k) (T v(|D |) − i∈D(−k) mvi ) ≥ 0, D(−k) ∈ ρ, then D(−k) ∈ λ because D(−k)

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 is among the largest coalitions that satisfies (T v(|D(−k) |) − i∈D(−k) mvi ) ≥ 0. Consequently, D(−k) ∈ γ, because D(−k) ∈ λ if and only if D(−k) ∈ γ. The theorem is proved with Cmax = D(−k) ∈ γ and Cmax ⊆ D\{Phighest }. (−k) If we do not find the smallest ⊆D  k such that the nonempty subset D (−k) that satisfies (T v(|D |) − i∈D(−k) mvi ) ≥ 0, then there is no Cmax ⊆ D   such that Cmax ∈ γ, because if there is a Cmax ⊆ D such that Cmax ∈ γ,   where |Cmax | = |D| − l = 0, then Cmax is a coalition of size |D| − l = 0 that satisfies (T v(|D(−l) |) − i∈D(−l) mvi ) ≥ 0. However, we know that D(−l) is the  coalition that does not satisfy (T v(|D(−l) |) − i∈D(−l) mvi ) ≥ 0 . Thus they are contradictions. Theorem 3. Given a set N of players, then either N ∈ γ; or if N ∈ / γ then there exists Cmax ⊂ N and Cmax ∈ γ only if Cmax ⊆ N \{Phighest }, where Phighest = argmaxi∈N mvi . Proof. We note that either N ∈ ρ or N ∈ / ρ. If N ∈ ρ, then N ∈ λ = {N } and hence N ∈ γ. Otherwise, if N ∈ / ρ, then by Theorem 2, either there is no Cmax ⊆ N such that Cmax ∈ γ, or there exists Cmax ⊆ N \{Phighest } such that Cmax ∈ γ where Phighest = argmaxi∈N mvi . Theorem 3 shows that if a set of players is not a member of γ, the set of players can become a member of γ by using a concept as follows. The player with the highest require minimum value is removed from the set of players one by one until the surplus of cooperation among players in the coalition is non-negative, or the set of players is empty. Then, if the set of players is not empty, the set of players is a member of γ. Otherwise, the set of players can not become a member of γ. 3.3

Example1 Revisited

Consider the scenario in Example 1 again. We define ρ, λ and γ of this example based on the maximal social welfare feasible coalition theory.  ρ = {Cmax ⊆ U : T D(|Cmax |) ≥ i∈Cmax RDi },   λ = {Cmax ∈ ρ : |Cmax | ≥ |Cmax |,∀Cmax ∈ ρ}, and   γ = {Cmax ∈ λ : T D(|Cmax |) − i∈Cmax RDi ≥ T D(|Cmax |) − i∈C  RDi , 

∀Cmax ∈ λ}

max

The coalition Cmax is found as follows. First, we find the set ρ which is {{Econ, Lib}, {Econ, Lib, Com}, {Econ, Lib, Com, Bus}, {Econ, Lib, Com, Bus, Eng}}. Then, we find the set λ which is {{Econ, Lib, Com, Bus, Eng}}. Finally, we find the set γ which is {{Econ, Lib, Com, Bus, Eng}}. In summary, the number of departments in the coalition Cmax is 5, and the purchasing office saves $250 = ($940 - $890) ×5 in total for the university. The total discount and the number of departments of the coalition in the maximal social welfare feasible coalition theory are larger than or equal to those in the core theory.

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Evaluation

The primary aim of this theory is to generate the maximal value of cooperation among players in a coalition. This theory guarantees that a value of cooperation among player in a coalition in the maximal social welfare feasible theory is larger than or equal to that in the core theory.  Proposition 1. Let β = ρ = {C ⊆ N : T v(|C|) ≥ i∈C mvi },   χ = {C ∈ β : T s(|C|) ≥ T s(|C |), ∀C ∈ β},    = {C ∈ χ : |C| ≥ |C |, ∀C ∈ χ},   λ = {Cmax ∈ ρ : |Cmax | ≥ |Cmax |, ∀Cmax ∈ ρ},   γ = {Cmax ∈ λ : T s(|Cmax |) ≥ T s(|Cmax |), ∀Cmax ∈ λ} C ∈  be a coalition that is formed by using concepts of the core theory, and Cmax ∈ γ be a coalition that is formed by using concepts of the maximal social welfare feasible theory. We have |Cmax | ≥ |C|. Proof (Number of players in a coalition). Let Cmax ∈ γ and C ∈ . Therefore Cmax ∈ λ and C ∈ χ. Since λ contains only all the largest coalitions in ρ, and elements of χ are all chosen from ρ as well, we have |Cmax | ≥ |C|. Corollary 1. Let C ∈  be a coalition that is formed by using concepts of the core theory, and Cmax ∈ γ be a coalition that is formed by using concepts of the max |) ≥ T v(|C|) maximal social welfare feasible theory. We have T v(|C |Cmax | |C| . Proof. Since |Cmax | ≥ |C| by Preposition 1, the Corollary follows trivially from the fact that T v(|•|) is an ascending function. |•| Proposition 2. Let C ∈  be a coalition that is formed by using concepts of the core theory, and Cmax ∈ γ be a coalition that is formed by using concepts of the maximal social welfare feasible theory. We have T v(|Cmax |) ≥ T v(|C|). Proof (Value of cooperation among players in a coalition). Since |Cmax | ≥ |C| by T v(|Cmax |) max |) Preposition 1, and by Corollary 1, T v(|C ≥ T v(|C|) × |Cmax | |C| , therefore, |Cmax | |Cmax | ≥

T v(|C|) |C|

× |C| or T v(|Cmax |) ≥ T v(|C|).

Proposition 2 shows that there is a larger value of cooperation for all players in the coalition to share.

5

Conclusion

In this paper, we propose a new theory for forming a coalition known as the maximal social welfare feasible coalitions. The theory assumes that each player in a coalition does not know other players’ information. It gives each player a payoff at least his minimum required value. Thus, it achieves the maximal value of cooperation among players in the coalition by forming a coalition that gives the number of players as large as possible (when the value of each player in the

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coalition is an ascending function of the number of players). We show that the value of cooperation among players in a coalition in this theory is equal to or larger than that in the core theory and its payoff allocation of the coalition is Pareto optimal.

References 1. Aumann, R.J., Maschler, M.: The bargaining set for cooperate games. In: Advances in Game Theory (Annals of Mathematics Studies 52), pp. 443–477. Princeton University Press, Princeton (1964) 2. Gillies, D.B.: Solutions to general non-zero-sum games. In: Contributions to the Theory of Games 5 (Annals of Mathematics Studies 40), pp. 47–85. Princeton University Press, Princeton (1959) 3. Straffin, P. D.: Game Theory and Strategy. The Mathematical Association of America (1993) 4. Shapley, L.S.: A value of n-person games. In: Contributions to the Theory of Games 2 (Annals of Mathematics Studies 28), vol. 2, pp. 307–317. Princeton University Press, Princeton (1953) 5. Wilson, R.: Information, efficiency and the core of an economy. Econometrica 46, 807–816 (1978) 6. Yannelis, N.C.: The core of an economy with differential information. Economic Theory 1, 183–198 (1991)

Recommender Agent Based on Social Network Heng-Li Yang and Hsiao-Fang Yang Department of MIS, National Cheng-Chi University, 64, Sec. 2, Chihnan Rd., Mucha Dist, Taipei 116, Taiwan {yanh, 94356507}@nccu.edu.tw

Abstract. Conventional collaborative recommendation approaches neglect weak relationships even when they provide important information. This study applies the concepts of chance discovery and small worlds to recommendation systems. The trust (direct or indirect) relationships and product relationships among customers are to find candidates for collaboration. The purchasing quantities and feedback of customers are considered. The whole similarities are calculated based on the model, brand and type of purchased product. Keywords: recommender system, social network, chance discovery, trust.

1 Introduction and Related Work Internet technology is changing day by day, and has becomes an important media channel of enterprises in the twenty-first century. By transcending the limits of time and space, electronic commerce (EC) has changed business marketing and people life-style. However, high growth rates are difficult to obtain in highly competitive and turbulent environments. Maintaining the loyalty of current customers and further attracting new customers are challenges for business. To attract customers to purchase products, recommendation systems are often embedded into EC web sites [14]. Common approaches include content-based approach [2,6] and collaborative filtering [1,4,13,15,16]. The content-based approach analyzes the content of item description documents, compares customer profiles, then recommends highly-related items to the customer. InforFiner [5] and NewsWeeder [6] are some examples of content-based systems. This approach has some limitations, e.g., the difficulties of analyzing multimedia documents, generating serendipitous finding, and filtering items based on criteria other than those disclosed [16]. Collaborative filtering is an alternative approach, which recommends items that other similar users have liked. It calculates the similarity of users rather than the similarity of items. Ringo [16] is an collaborative filtering application for recommending music. To judge similarity, some clusters of customers with the same characteristics (e.g., shared experiences, location, education or interests have to be found. However, this study argues that the clustering approach has some blind spots in. First, a customer might not trust the recommendations of customers in a cluster assigned by the system. Second, two customers in different clusters could still reference each other recommending A based on B’s preferences. This is the kernel spirit of chance H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 943–952, 2007. © Springer-Verlag Berlin Heidelberg 2007

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discovery: chances occur while mining low relationships but important information [9]. This study applies the idea of chance discovery to the recommendation approach. However, the problem is locating these weak but important relationships. As shown in Fig. 1, relationships among customers of a commercial web site can be categorized into two groups. The first class of relationship, namely product, occurs because customers purchased the same product(s), e.g., the same model, brand or type. The second class of relationship is trust. Some previous studies have recommended trust-based recommendation systems [3,7,8,10,11,12]. For example, Pitsilis and Marshall [11,12] recommended establishing a direct trust link between two people with common experiences (i.e., purchased the same items), and deriving indirect trusts through a chain. Battiston et al. [3] suggested that when purchasing an item, consumers could query their neighborhood for recommendations. O'Donovan and Smyth [8] proposed a system that automatically infers trust values from ratings between users and movie producer. Massa et al. [7] suggested that consumer A could assert a trust statement toward B if A consistently found the product reviews and ratings of B valuable. Integrating the concepts of the above works, this study proposes checking the list of trustful customers, which are explicitly established by same customers. The listing of trustful customers might already exist before becoming the customers of the web. Alternatively, customers could provide names by searching product reviews or the forum discussions at this web site. People may consult the suggestions from others that they directly or indirectly trust when buying something.

Fig. 1. Two kinds of networks among members: dotted line is product connection (purchase the same product), solid line is trust connection, A→B implies A trusts B

The number of relationships that should be consulted could be determined by the small world theory of developed by Watts [17]. The small world theory claims that the distance between any two people is no more than six degrees. People can be clustered into groups based on characteristics. A person may belong to several clusters simultaneously at the same time. Because of such overlap, a target person can be found by consulting only six persons. In the proposed model, the indirect trust listing could be constructed from the direct trust listing: A trusts C because A trusts B, and B trusts C. However, for efficiency, this study only considers three layers of trust.

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The rest of this study is organized as follows. Section 2 explains the main idea of the proposed approach. Section 3 describes a scenario to explain how such an approach would help customers. Section 4 draws conclusions.

2 The Proposed Approach The web site is assumed to maintain basic profiles of its members. The past year of detailed transaction data should be kept. A proper product type hierarchy should be maintained. These data enable the system to determine the product model, brand and type bought by each member. The granularity of type classification, fine or coarse, depends on management decisions. For example, the chain of ancestors of model “Nokia model N90” could be “Nokia model N-series”, “Nokia 3G cellular phone”, “3G cellular phone”, “cellular phone”, “telephone”, “communication product”, “electronic product”. The website should also maintain the evaluation scores of bought products for each customer. If a customer bought a product for one month, then the website would e-mail him (her) to ask for his (her) evaluation score (“1” to “7”). The website might give some bonus to encourage the replies. If the customer did not reply within some specified period, then a default score “4” would be assigned. Additionally, the trustfulness listing of each member is obtained. It could be established at the time that a person became the member of the website, and could be updated anytime later by the member. The website has a forum for customer discussions and product reviews. The persons on the trustful list are also members of the website. They may be friends before in real world or have become acquainted in the virtual world. Alternatively, customers could pick up the member names after reading their product reviews. A member could only provide five names (IDs) for all products, or could give five names for each product type. However, even if the website already had the name listing, the system would ask when a member requesting a recommendation to confirm his trust rating. The proposed approach has two phases. Based on the two relationships mentioned above, the first phase searches all candidate persons with similar purchasing experience. The second phase calculates the similarities of each candidate, and recommends the candidate with the most similar experience. 2.1 Candidate Search Phase As shown in Fig. 2, the first phase would attempt to find candidates from trust and product relationships. Users are then requested to clarify the recommendation task: specifying product model series (e.g., “Nokia N-series cellular phone”, product brand (e.g., “Nokia 3G cellular phone”) or product type (i.e., “3G cellular phone” or “cellular phone”). As shown in Fig. 3, at most three layers of trust are consulted. In the first derivative (i=1), the direct trustful list (five persons at most), is applied. The transitive trusts are derived from the direct trusts: the second derivative (i=2) consists of 25 persons at most, while the third derivative (i=3) consists of 125 persons at most. The expansions stop once any candidates are found. As shown in Fig. 4, when consulting with the trusted persons, the system checks whether these trusted persons have purchased any related product model (e.g., any “Nokia”, “3G cellular phone” or “cellular phone” model) based on the product type hierarchy.

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Procedure Search_Candidate Step1: Initialize i=1 // i denotes the expanding times of trustful relationship Step2: Initialize TL0 and Candidate to be {} // TLi denotes a trust list; TL0, the initial set, is empty; Candidate store all candidates who purchased the specified product Step3: Set r as some specified product model series, brand, or type specified by the user or automatically recommended by the system. Step4: Ask the user to confirm or provide the trustful list TL1 Step5: Do while (i w2> w3. The max function would return the maximal values of all values of SM, SB, or ST.

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M

SM ( x, u ) = ∑ FM [ x, m] − FM [u , m] m =1

(1)

B

SB ( x, u ) = ∑ FB[ x, b] − FB[u , b]

(2)

b =1 T

ST ( x, u ) = ∑ FT [ x, t ] − FT [u , t ]

(3)

t =1

S ( x, u ) = w1×

SM ( x, u ) SB( x, u ) ST ( x, u ) + w2 × + w3 × max( SM ) max(SB ) max( ST )

(4)

3 A Scenario A person, James, is currently unsure about what notebook he should purchase for his work. He has not used any notebook before. Additionally, he is not familiar with the technology market. He is worried about the notebook price, performance and future possible repair support. If he is a member of the website, then he can use it to help him choose a suitable notebook. The user could first browse the product items and read the descriptions on the website. Suppose that he has good previous impressions of IBM products and has become interested in the Z-series after reading the product descriptions. The IBM notebooks are available in several series, such as, X, T, R and Z. The Z-series has a number of models, such as Z60t and Z60m. The user makes a recommendation request. The website first asks him to provide a list of names of trusted users, at most five members. If he cannot provide such a listing, then the website can only recommend the best Z-series model as evaluated by all of the members who had purchased before. Suppose that he provides the names of three members. In the candidate searching phase, the website checks whether these members have purchased any Z-series model. If no directly trusted member has such purchase records, then the system searches the indirect trustful member listings in at most two iterations. If neither direct nor indirect members have such experiences, then the website relaxes the constraints to set the search target to the higher level of product items (e.g., any series of IBM notebook). If the system can find any suitable candidate after relaxing constraints, then it gives the user a message such as “No members whom you trust directly or indirectly have purchased any IBM Z-series model notebook, but some have purchased IBM R-series models. Would you agree to relax your original constraints?” If more than one candidate exists, then the system calculates the similarity, and recommends the purchase experiences of the candidate (say Tom), who was most similar to James from past experience. For example, the system would give the user a message such as “Based on the previous records, the system has found that among those your directly (or indirectly) trusted persons, as the whole, Tom, had purchase behaviors most similar to you. Among those IBM Z-series models, which Tom

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purchased, he gave Z60m the highest feedback evaluation. Therefore, we suggest you consider Z60m”. If James were a new member without any purchase records, then the system could not calculate the similarity. In that case, the system would suggest him the best Z-series model, which was evaluated by all of the members whom James trusted (of course, the directly trusted would be given first priority). Figure 5 shows the GUI of a proposed recommendation system. On the left side, a user can browse product descriptions, input/modify trust lists and request recommendation via type hierarchy.

Fig. 5. The proposed recommendation system

4 Conclusions Integrating the ideas of the literature, this study applies the ideas of chance discovery and small world to recommendation systems. The proposed approach has the following characteristics of our approach. (1)The explicit trust relationships are respected. The indirect trusts are derived beginning from the direct trust listing. The theory of chance discovery means that indirect trust relationships might imply low relationships, but provide important [9]. Watts [17] reported that in a world consisting of hundreds of thousands of individuals, every actor could be connected to every other actor within an average of less than six steps. This study uses three layers of trustful lists. Customers should feel comfortable because the consulted persons are within their trust networks, had all purchased the requested product, had the most similar purchasing experiences. Therefore, the recommender would pay attentions to

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psychological status as well as the actual behaviors of the consumers. Suppose that a consumer John chooses a particular product brand because it has been purchased by Mary whom he trusts. However, upon consumption, he discovers that this brand does not match his taste and gives low evaluation feedback. In this case, the similarity score between John and Mary is lowered. John would be less likely to follow the recommendations of Mary the next time he goes shopping, although Mary remains in his list of trusted members. (2) In case the customer does not know any other members, he could pick up the professionals as the trusted persons after querying some of the product reviews and judging who are “best” buyers. Notably, trustful persons must first be selected to enable the customers to refer to their experiences. (3) The concept of product type hierarchy is used to allow the recommendation to climb up or drill down to search the "same" product purchases. The system relaxes the user's specification and provides more intelligent suggestions. (4) The similarities between customers are based on overall pictures of their purchase behaviors: purchasing quantities, feedback of customers, the whole chain of product model, brand, and type. Therefore, the similarity would be integrated and holistic. Future work will be to implement the proposed approach, and perform experiments to evaluate its performance.

References 1. Balabanovic, M., Shoham, Y.: Fab: Content-Based, Collaborative Recommendation. Communications of the ACM. 40(3), 66–72 (1997) 2. Basu, C., Hirsh, H., Cohen, W.: Recommendation as Classification: Using Social and Content-Based Information in Recommendation. In: Proceedings of AAAI Symposium on Machine Learning in Information Access (1998) 3. Battiston, B., Walter, F.E., Schweitzer, F.: Impact of Trust on the Performance of a Recommendation System in a Social Network. In: W Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS’06). Hakodate, Japan (2006) 4. Goldberg, D., Nichols, D., Oki, B., Terry, D.: Using Collaborative Filtering to Weave an Information Tapestry. Communications of the ACM. 35(12), 61–70 (1992) 5. Krulwich, B., Burkey, C.: Learning User Information Interests through Extraction of Semantically Significant Phrases. In: Proceedings of the AAAI Spring Symposium on Machine Learning in Information Access (1996) 6. Lang, K.: Newsweeder: Learning to Filter Nnetnews. In: Proceedings of the 12th International Conference on Machine Learning, pp. 331–339. Morgan Kaufmann, San Francisco (1995) 7. Massa, P., Bhattacharjee, B.: Using Trust in Recommender Systems: An Experimental Analysis. In: Jensen, C., Poslad, S., Dimitrakos, T. (eds.) iTrust 2004. LNCS, vol. 2995, pp. 221–235. Springer, Heidelberg (2004) 8. Montaner, M., Lopez, B., de la Rosa, J.L.: Developing Trust in Recommender Agents. In: Proceedings of the First International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS’02). Palazzo Re Enzo Italy, pp. 304–305 (2002) 9. Ohsawa, Y., McBurney, P.: Chance Discovery, Advanced Information Processing. Springer, Heidelberg (2003)

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10. Pitsilis, G., Marshall, L.: Trust as a Key to Improving Recommendation System. In: Herrmann, P., Issarny, V., Shiu, S.C.K. (eds.) iTrust 2005. LNCS, vol. 3477, pp. 210–223. Springer, Heidelberg (2005) 11. Pitsilis, G., Marshall, L.: A Proposal for Trust-enabled P2P Recommendation Systems. Technical Report Series (CS-TR-910). University of Newcastle upon Tyne (2005) 12. O’Donovan, J., Smyth, B.: Trust in Recommender Systems. In: Proceedings of the 10th International Conference on Intelligent User Interfaces, pp. 167–174 (2005) 13. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In: Proceedings of the ACM Conference on Computer Supported Cooperative Work, pp. 175–186. ACM Press, New York (1994) 14. Resnick, P., Varian, H.: Recommender Systems. Communications of the ACM 40(3), 56–58 (1997) 15. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based Collaborative Filtering Recommendation Algorithms. In: Proceedings International WWW Conference, pp. 285– 295 (2001) 16. Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating ’Word of Mouth’. In: Proceedings of the Conference on Human Factors in Computing Systems (CHI95), pp. 210–217 (1995) 17. Watts, D.J.: Six Degrees: The Science of a Connected Age. W.W. Norton & Company, New York (2003)

A New Pooled Buying Method Based on Risk Management Tokuro Matsuo Department of Informatics, Faculty of Engineering, Yamagata University, 4-3-16, Jonan, Yonezawa, Yamagata, 992-8510, Japan [email protected] http://veritas.yz.yamagata-u.ac.jp/

Abstract. In this paper, we handle a negotiation method in which a main negotiation consists of multiple sub-negotiations. In items allocation for commerce, there are some risks on the trade because the market balance is determined by supply and demand. The result of main negotiation is also determined by the order of sub-negotiations and agents’ behaviors since agents’ budgets have limitations on the actual commercial trading. However, it is difficult to decide the order of negotiations, such as simultaneous decisions and rotations. In this paper, we give the trading model in such cases, that is, agents purchase items by pooled buying. In actual trading as pooled buying, items are sold by the volume discount. Concretely, we discuss joint-stock company and private limited partnership on the web. In the negotiation phase, an agent proposes pooled buying based on the number of items and their prices considering their budgets. The degree of risks is calculated. All agents can see the risks with each item. Agents cooperate to the proposing agent based on the degree of risks. In this paper, we give two scenarios for trading. One is to avoid free riders who get surplus without risks. Another one is to promote agents’ participation to increase social surplus. For risk aversion and promoting cooperation, we employ the side-payment policy, that is, cooperative agents’ risks are preserved to a minimum. Further, we give some discussions where agents must pay the negotiation costs and charge for storage.

1 Introduction In recent years, one of important research issues in agent-mediated negotiations is to support multiple users when users use the system such as e-commerce [1][4][5][6]. There are some researches concerned with negotiations and decision making for agents including multiple utility function-based negotiations, realistic decision support systems and several others. To solve the problems in many situations, most of researches should be integrated to support us as intelligent systems. In the most of agent researches, many B2C/C2C-based negotiations and trading methods are proposed as commercial activities like an agent-mediated auctions [2][7]. In this paper, we handle an integrated both B2B and B2C trading case where agents purchase items from sellers as pooled buying to sell the end buyers. H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 953–962, 2007. c Springer-Verlag Berlin Heidelberg 2007 

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Fig. 1. Overview of the marketplace

Generally, in electronic commerce, agents who negotiate with other agents, have a certain limitation concerned with their budgets and they cannot spend money over their budgets. Using this realistic condition, we consider that one negotiation consists of multiple sub-negotiations such as pooled buying. Figure 1 shows an example in which multiple traders select items with cooperatively. Agents ante up to pay to purchasing at low price. All agents cannot determine whether they stock in the item or not without other agents’ behaviors. For example, there are 3 items and each item’s cost is {$5, $3, $4} and the market price of each item is {$6, $4.8, $4.8}. When an agent’s budget is 7 dollars and he/she gets the first item for five dollars, he/she cannot stock in the other items even though second and third items’ surpluses are larger than the first item’s surplus. Agent’s utility is just $1. On the other hand, if agent purchase items 2 and 3 with other agent’s joint payment $1, the agent’s utility is $1.4 and another agent’s utility is $0.2 even though we simply divide the surplus. Let me give more complex situations. In actual commercial activities, agents are sometimes cooperate with each other and make a collusion to purchase items at a low price. Moreover, when agents purchase items for pooled buying, a seller sometimes discounts the items’ prices if a lot of items are traded. In these situations, an agent may not increase his/her total utilities even though he/she gets a lot of utilities in sub-negotiations. Concretely, we discuss the situation where multiple sole proprietorships exist in the marketplace. Although each agent’s transaction volume is not heavy, agents can buy-in the items at a low price when they cooperate with each other. If all agents know players’ preference distributions, the cooperation performs easily. However, it is difficult for agents to know other agents’ preferences, values, intentions and strategies. Further, pooled buyers have some risks in which they stock in the items since the items might not be purchased by end-buyers. Cooperative agents might pin the blame for the dismal outcome on the proposing agent in actual tradings. To solve the problems, we give a new negotiation process and property of our proposed issues. In our proposed pooled buying, first, an agent proposes buying-in of items to all agents who participate in the association. All agents can know the discount rates

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with number of items. Agents who want to participate in the buying group declare to the proposing agent. Then, agents negotiate about allocations and costs. Agent ai gathers money from agents who participate in the group. After that, items are allocated to the agents. Finally, each agent deals in the item of self-discipline. In this paper, we give two scenarios for trading. One is to avoid free riders who gets surplus without risks. Another one is to promote agents’ participation to increase social surplus. The rest of this paper consists of the following six parts. In Section 2, we show preliminaries on several terms and concepts of tradings. In Section 3, we give a simple trading model. In Section 4, we consider an example based on the trading model. In Section 5, we discuss and give a trading rule to modify the dry hole of tradings. Further, we give an experiment to compare between some cases and show related work. Finally, we present our concluding remarks and future work.

2 Preliminaries In this section, we give some definition of model used in our proposed mechanism. There are agents who have multiple preferences based on their trading history. Here, we give the model for the mechanism. 2.1 Model Agent ai is ith agent in a set of agents {a1 , ..., ai , ..., an } who participate in a trade. Seller bj is jth seller in a set of sellers {1, ..., j, ..., m} who sells items to an agent. Item j is an item sold by the seller bj . Ch is an agent group formed as coalition. If agents {a1 , a2 , a3 } participate in the group, Ch is C{a1 ,a2 ,a3 } . – πij is an expected price of item j for agent i when the agent deals in item j. – Πij is a cost price in which an agent stocks up items from sellers. – pij is an actual price sold in the market.

– – – –

Assumption 1. All sellers can deal in the sets of items at price on volume discount. Assumption 2. Each agent has enough money to purchase much number of items at one time. 2.2 Overview We consider a model about broking business and intermediate business on both B2B and B2C. Figure 2 shows the overview of the trading model of intermediate traders. The intermediate agent stocks in the item from sellers such as producers on the B2B. The items are sold to buyers on the price added some yields. However, in some cases, the agent might not have any ability of buying in the items because he/she is nothing but a small trader. If traders can cooperate with each other, they can get some opportunities for enlargement of the trading. In such case, multiple agents ante up to pay for stocking in the items form sellers. Then, how the items are allocated ? We consider a simple case of the allocation by agent i and i . The set of allocations is X = {(xi , xi )|xi + xi = 1, xi ≥ 0, xi ≥ 0}. xi is the allocation to agent i and xi is the allocation to agent i .

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Fig. 2. Overview of trading

Fig. 3. Volume discount

2.3 Volume Discount Volume discount is one of trading scheme used in some commercial situations. Company called as ”Co-op” employs volume discount trading based on number of items. Generally, when items increase sold in a trading, item’s price decreases. For example, in volume discount, item’s price is decided based on the number of items as shown in the Figure 3. If the buyer purchases each item individually, each item is sold for $7. When a buyer purchases 5 items simultaneously, the item is dealt in for $5. Trading in volume discount can provide items at a lower price compared with individual purchasing. Buyer’s utility is calculated as 35 − 25 = 10.

3 Trading Model In this section, we give a trading model for intermediaters.

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3.1 Risk Management In actual trading, items are not dealt in to the buyers on the expected price. Each item has a risk on the cost. Figure 4 shows an example of the graph of a risk function. The left figure in Figure 4 shows an example of reasonable price for general buyers. The right figure in Figure 4 shows an example of relationship of price and number of items purchased by buyers. When expected price πij is a lower price than item’s price pij , items may end up on the shelf. If pij > πij , much number of items are sold comparing with above case. The inside area of the curve is the number of potential buyers who want to purchase the items. Thus, if item’s price drops-off as the right figure in Figure 4, many items are purchased by buyers. When sellers deals in the items on the volume discount price, the stable solution is on the cooperations. However, it is difficult to know how agents should cooperate effectively. Simply, we assume the following situation about the reasonable price and relationships between number of items and its price.

Fig. 4. Trading risks

3.2 Trading Scheme The difference between general cooperation purchasing and the trading on this paper is the order of making a coalitions. In general volume discount, it is difficult for each agent to negotiate with each other. Agents must anticipate how many agents join in the group. If no agents participate in the cooperation group, the agent who declares an intention to purchase must purchase the items at an expensive rate. On the other hand, the cooperation of this paper gives the opportunity of negotiation with each agent. Based on the result of negotiations, agents can determine whether items are stocked in or not. Then, What is the appropriate negotiation for this situation ? First, we consider the simple negotiation model except for the order of negotiations. We assume that agents make the traders association to cooperate when they stock in items. Protocol (1) Agent ai proposes buying-in of items bj to all agents who participate in the association. All agents can know the discount rates with number of items. (2) Agents who want to participate in the buying group declare to agent ai . (3) Agents negotiate about allocations and costs.

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(4) Agent ai gathers money from agents who participate in the group. (5) Items are allocated to the agents. (6) Finally, each agent deals in the item of self-discipline. Demand of items determines the risk if there are some items as shown in the area in Figure 5. Even though the distribution of expected price is different, the risk is shown as essentially same as the area. In microscopic point of view, the agent in which a lot of items are allocated may suffer latent losses. Thus, even though some agents participate in the buying group, the agent ai who propose making coalition should be secure the remained items if no agent want to stock in a lot of items.

Fig. 5. Trading risks

4 Examples First, we give a simple example of determination of price and allocations. Agent 1 proposes buying-in the item, which is sold for the price as same as Figure 3. In this case, we assume p in the Figure 3 is 1. Agent 1’s budget is 7.5. Agent 2 declares that he/she can cooperate with agent 1’s project. Agent 2’s budge is 3. Here, if agent 2 does not participate in the group, agent 1 purchases 8 items for 7.2. Since agent 2 joins in the group (C{1,2} ), they can buy-in the items at the lowest price. Agent 2 can purchase 3 items for 2.4. Each items price for agent 1 is 0.8. Table 1 shows the total price of each case where agent 2 does not join in and he/she joins in. Agent 1’s risk reduce as 7.2 − 6.4 = 0.8. Here, side-payment from agent 1 to agent 2 is calculated. It is determined in the value 0 to 0.8 because agent 1 can buy-in the item at low price for 0.8 comparing with individual purchasing. Then, we give another example of generalized formations. There are n agents in the marketplace and a proposing agent is a1 . There is a seller who deals in the item for buyers’ pooled buying. We assume, in this case, that only one item is sold for the Table 1. Examples

Agent 1 Agent 2

Number of items Agent 2 does not cooperate Agent 2 cooperates 8 0.9 * 8 = 7.2 0.8 * 8 = 6.4 3 0.8 * 3 = 2.4

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1 2 k k 1 price π. The seller gives an item’s price level as {Πij , Πij , ..., Πij ..., Πij }. Πij is the 1 cheapest price in the table like 3. Π11 shows that item 1 is sold to the agent a1 at the lowest price. The following shows the some situations on the cooperations. k – If Π11 < p11 , agent a1 needs not searching for cooperative agents. However, agent 1 a1 can get more utilities if he/she can cooperate with agents holding Π11 < p11 . 1 In this case, agent a1 ’s expected utility is p11 − Π11 without side-payments. Sidek 1 payments should be defined between 0 to (Π11 − Π11 ) ∗ l (l is number of items whom cooperative agents can stock in). k – If p11 < Π11 , agent a1 should make coalitions with other agents because expected utility is minus.

Then, we consider there are multiple items in the market-place. We assume there are 2 items {b1 , b2 } and 3 agents {a1 , a2 , a3 }. Each item’s price table is 2 levels, that is, for 1 2 example, item b1 ’s price is {Πi1 , Πi1 }. Each agent has his/her budget. In this case, how agents cooperate with each other. If is difficult to determine allocations because each agent has a certain strategy to increase his/her utilities. If agent a1 ’s expected utility for 1 item b1 is p11 − Π11 > 0 when he/she has enough amount of budgets, the agent may not propose cooperations because he/she can get the maximized utility. On the other hand, we consider agent a1 ’s budget has a limitation and he/she can not stock in the item at 2 the lowest price. If agent a1 ’s expected utilities for item b1 and b2 are p11 − Π11 > Πi1 , agents can propose and cooperate easily. Here, we consider the case, that is, a relationship of expected price and cost price for item b1 is πi1 > Πi1 . In this case, some agents may make a loss when each agent deals in the items. Agents who have a lot of items have a higher chance of making a loss even through the rate of selling items assumed on the uniformed distributions. There are less proposing agents like this situation. Most of agents might consider getting a lot of utilities with a low risk.

5 Discussion 5.1 Safty or Efficiency In our protocol, there are 2 scenarios/options set up by a system manager, that is, the mechanism can avoid free riders who gets surplus without risks or can promote agents’ participation to increase social surplus. If the manager employ our mechanism for secured e-commerce system, the former is selected. Otherwise, if the manager uses our mechanism to be an efficient marketplace, the latter is employed. Particularly, in the economical point of view, the latter is selected to increase traders’ utilities.

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Total Utilities

Individual Purchasing Mechanism 1 Mechanism 2 $1737 $1260 $2905

Avoiding Free Rider. In our mechanism, proposing agents must buy-in some number of items in which they selected. Moreover, if the proposing agents’ payment amounts are decrease due to the participation of cooperative agents, they pay the sided-payment to the cooperator. In this situation, if all agents are rational, no agents propose their intentions to select and purchase items. They can increase their utilities to be a cooperative agent rather than to be a proposing agent. In this case, the allocation might never determine for a long, long time because free-rider agents can come off more gainers than proposing agents. Some agents may not propose the pooled buying. Instead, they participate in the cooperation to get the high utilities. Thus, we give one rule for trading to avoid free riders. Agents who participate in the marketplace must be proposing agents. If the agents propose purchasing items, they have rights to be cooperative agents. Further, agents propose simultaneously. When the negotiation should be conducted simultaneously in the mechanism, agents should give proposals of pooled buying to get item j, which is max(πij − Πij ) if there are not any risks. If agents are rational, they search for the allocations for pooled buying in which agents’ utilities are maximum. Efficiency. On another hand, if the system manager wants to hold the efficiency of trading. The simultaneous proposings from agents are removed. If agent can forecant that the items price goes down by participating other cooperative agents, proposing agents can propose the purchasing easily. Cooperative agent participate group buyings as funds permit sequentially. In this mechanism, the social surplus for agents increases. 5.2 Experiment To show the difference between general volume discount, the mechanism for avoiding free riders, and the mechanism for efficiency, we conducted an experiment by a simulation. We prepares the three items and five agents. Each item has a price with number of items. In this experiment, we set that there are three sorts of price with the number of items. Each price is set up based on the uniform distribution from $1 to $100. First, the lowest price is defined. The middle rank of price is defined from the lowest price to $100. Finally, the highest price is defined from middle price to $100. Each agent’s budget also set up based on the uniform distribution from $200 to $1, 000. Market price of each item is defined as 2(the highest price) - the lowest price. We conducted 100 trial simulations. As the result, the averages of agents surplus show in Table 2 Mechanism 1 is the protocol of avoiding free riders and mechanism 2 is the protocol of increasing efficiency. 5.3 Related Work In this section, we present an overview of the work of others related to our study.

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eMediator [8] is an electronic commerce server and consists mainly of eAuctionHouse and eCommitter. eAuctionHouse is a configurable auction place that supports many auction types, while eCommitter is a leveled commitment contract optimizer that can solve the Nash equilibrium thresholds. The above systems do not handle volume discount. GroupBuyAuction [9] is an agent-based electronic market on which agents automatically negotiate with each other on behalf of their users. In particular, in the GroupBuyAuction, buyer agents can form coalitions to buy goods at a volume discount price. Li and Sycara considered an e-market where each buyer places a bid on a combination of items with a reservation cost, and sellers offer price discounts for each item based on volumes [4]. By artificially dividing the the reservation cost of each buyer among the items, optimal coalitions with respect to each item are constructed. These coalitions satisfy the complementarity of the items by reservation cost transfers, and induce the optimal solution. The BiddingClub [3] is proposed by Layton-Brown, Shoham and Tennenholtz. In the BiddingClub, agents conduct a pre-auction. After the pre-auction, monetary transfers take place. The G − Commerce [1] is an agent-mediated electronic commerce system which seller agents and buyer agents negotiate with each other. In the model, seller agents cooperatively negotiate in order to sell goods in stock. Buyer agents cooperatively form coalitions in order to buy goods based on discount prices. Seller agents’ negotiations are completed by using an exchanging mechanism for selling goods. Buyer’s multi-attribute preferences are not referred to in these researches. Matsuo proposed the effective cooperation methods based on group buying [6]. Buyers cannot always purchase goods at a lower price, because buyers’ sub-groups are distributed. Matsuo proposed three methods for group integration. First, buyers trades on simple group buying. Second, all buyers are integrated. Third, some buyers are integrated.

6 Conclusion In this paper, we proposed negotiation mechanism where a certain negotiation consists of multiple sub-negotiations. In items allocation for commerce, there are some risks on the trade because the market balance is determined by supply and demand. The result of main negotiation is also determined by the order of sub-negotiations and agents’ behaviors since agents’ budgets have limitations on the real commercial trading. We also discussed joint-stock company and private limited partnership on the web. In the negotiation phase, an agent proposes pooled buying based on the number of items and their prices considering their budgets. If we employ the model based on same preference distributions among all agents, the allocation might never determine because free-rider agents can come off more gainers than proposing agents. On the other hand, we give some property under the realistic situations. In discussion, we give 2 types of protocols. One is to avoid free riders who gets surplus without risks. Another one is to promote agents’ participation to increase social surplus. In experiments, avoiding free riders, social surplus decreases a lot of degrees.

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Our future work includes how side-payments are calculated and determined. In real commercial activities, side-payments are one of most important issues to give incentives to cooperations among agents. Further, we apply our mechanism to the real electronic commerce marketplace.

References 1. Ito, T., Hattori, H., Shintani, T.: A cooperative exchanging mechanism among seller agents for group-based sales. The International Journal of Electronic Commerce Research and Applications (ECRA) 1(2) (2002) 2. Ito, T., Yokoo, M., Matsubara, S.: A combinatorial auction protocol among versatile experts and amateurs. In: Proc. of the 3rd International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS04), pp. 481–488 (2004) 3. Layton-Brown, K., Shoham, Y., Tennenholtz, M.: Bidding clubs: Institutionalized collusion in auction. In: the proceeding of the ACM Conference on Electronic Commerce(EC’00), pp. 253–259. ACM Press, New York (2000) 4. Li, C., Sycara, K.: Algorithms for combinational coalition formation and payoff division in an electronic marketplace. In: the proceedings of International Joint Conference on Autonomous Agents and Multi-agent Systems, pp. 120–127 (2002) 5. Matsuo, T., Ito, T.: A decision support system for group buying based on buyers’ preferences in electronic commerce. In: the proceedings of the Eleventh World Wide Web International Conference (WWW-2002), pp. 84–89 (2002) 6. Matsuo, T., Ito, T., Shintani, T.: A buyers integration support system in group buying. In: the proceedings of EEE International Conference on Electronic Commerce Technology (2004) 7. Parkes, D.C, Ungar, L.H: An ascending-price generalized vickrey auction. In: The SITE Workshop on The Economics of the Internet (2002) 8. Sandholm, T.: emediator: A next generation electronic commerce server. In: Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI99), pp. 923–924. AAAI Press, Stanford (1999) 9. Yamamoto, J., Sycara, K.: A stable and efficient buyer coalition formation 40 scheme for emarketplaces. In: the proceedings of International Joint Conference on Autonomous Agents and Multi-agent Systems(AAMAS-2001) (2001)

The Representation of e-Contracts as Default Theories* Georgios K. Giannikis and Aspassia Daskalopulu Department of Computer and Communications Engineering, University of Thessaly, Gklavani 37, 38221 Volos, Greece {ggiannik,aspassia}@inf.uth.gr

Abstract. It is widely acknowledged that a temporal representation of econtracts is essential in order to support e-contract execution and performance monitoring. One possibility that has been explored by many researchers is to represent e-contracts in Event Calculus. Although such representations are intuitive and facilitate temporal reasoning about actions/events and their factual and normative effects, they fall short in situations where domain knowledge cannot be assumed to be complete. Moreover, it is not clear how dynamic normative conflict resolution can be achieved, without resorting to unintuitive representations for conflict resolution strategies. In order to maintain the benefits of an underlying Event Calculus representation, and incorporate assumption-based reasoning and dynamic conflict management capability, we propose a representation of e-contracts as Default Theories, which are constructed by translating Event Calculus representations dynamically. Finally, we discuss how the resulting Default Theory representation enables a software agent to address various reasoning problems. Keywords: Multi-agent systems, E-commerce, Reasoning with incomplete knowledge, Event Calculus, Circumscription, Default Logic, Default reasoning.

1 Introduction This paper reports on work conducted within a broader project that is concerned with the development of open electronic markets. It is envisaged that such e-markets will be offering a variety of services to software agents, to seek potential partners, negotiate and establish agreements and subsequently monitor whether the business exchanges that they perform comply with the established agreements. During the conduct of a business transaction regulated by an e-contract, an agent will need to establish at a given time point, among other things: (i)

*

What state the business exchange is in, that is, given a history of events, what factual information is established and what norms are active for each party. This calls for some kind of temporal reasoning and reasoning about actions and their effects. Moreover, if the history of events is incomplete, or if the agent

This work was supported by the European Commission and the Greek Secretariat for Research and Technology (PENED 2003 – 03ΕΔ466).

H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 963–973, 2007. © Springer-Verlag Berlin Heidelberg 2007

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possesses incomplete domain knowledge, such reasoning will need to employ assumptions; should more information become available later, rendering some of these assumptions false, any conclusions drawn will need to be retracted. Hence, some kind of temporal default reasoning is required. (ii) Whether normative conflicts arise for the agent, that is, whether it finds itself in a situation where it bears norms that it cannot fulfill simultaneously. In such cases, the agent needs some way to resolve such conflicts: perhaps resolution may be achieved through planning, where the agent can identify a course of action that makes it possible to fulfill all the norms it bears; alternatively resolution may amount to choice, where there is really no plan that could satisfy all the active norms. Then, the agent must accept that some violation is inevitable and the question for it is which norm to sacrifice. This kind of reasoning calls for some way to detect and manage normative conflicts. In order to support temporal reasoning and reasoning about actions and their effects, many researchers (for example, [1, 2, 3, 4] among others) have adopted Event Calculus (EC) [5] for contract representation. However, EC representations cannot support assumption-based reasoning. In order to enhance the EC representation of an e-contract and support default reasoning one might use the Closed World Assumption (CWA) [6] or McCarthy’s Circumscription [7], both of which seem problematic for our purposes. In the first case, under the CWA, an atomic formula is assumed false, unless it is known to be true. The agent using a (possibly incomplete) EC contract representation essentially admits into its knowledge base negative literals that correspond to assumptions it makes under CWA, about the falsity of certain atomic formulae. In many realistic scenarios, however, the agent will need to make assumptions about the truth of certain atomic formulae. In the second case, we might use Circumscription, a generalization of the CWA. Here, we use special predicates to denote abnormal (unexpected) events and effects of actions, and our inference strategy attempts to minimize abnormality. The agent essentially admits into its knowledge base explicit information about abnormality and the conclusions derived are those contained in the minimal models of the augmented knowledge base. Yolum and Singh [8] work in this direction. However, this approach presents some problems for realistic scenarios: First, it requires that we define abnormal events, effects of actions and the like, explicitly, and, also, that we explicitly distinguish each abnormal from other individuals. Second, in order to decide which individuals to characterize as abnormal, we are required to anticipate the conclusions that we want to be able to derive. There are two other approaches to support default reasoning with e-contracts that do not, however, employ some underlying temporal logic. Grosof’s SweetDeal [9] represents contract rules via Logic Programs. Governatori et al. [10] use Nute’s Defeasible Logic [11] in their DR-Contract architecture. Both of these address conflict detection and management, by ascribing priorities to contract norms. However, the ascription of priorities is static, and hence, the agent’s conflict handling mechanism cannot evolve and adapt as circumstances change. Finally, a third approach to e-contract representation is Paschke’s ContractLog [12], developed for Service Level Agreements, which represents contracts via event-condition-action rules. ContractLog also adopts Nute’s Defeasible Logic and conflict management is possible, again through the explicit, static, ascription of priorities to norms.

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In order to maintain the benefits afforded by Event Calculus and address its limitations, we propose a representation of e-contracts in Reiter’s Default Logic (DfL) [13], which may be constructed from an EC representation.

2 e-Contracts in Event Calculus Consider a 3-party business transaction that takes place in an electronic marketplace populated by software agents. A retailer agent (RA) communicates with a wholesaler agent (WA) and establishes an agreement for purchasing a certain product. Consequently, WA communicates with a carrier agent (CA) and establishes another agreement for the timely and safe delivery of goods from WA to RA. The first agreement (between RA and WA) is to be conducted on the following terms: WA should see to it that the goods be delivered to RA within 10 days from the date RA’s order happens. RA, in turn, should see to it that payment be made within 21 days from the date it receives the goods. The agreement specifies sanctions in case the two agents do not comply with their obligations, but we do not need to refer to them explicitly here. In the same spirit, the second agreement (between WA and CA) defines obligations, deadlines and possible sanctions/reparations in case of violations. Following [14], we may take an informal, process view of the business transaction that is regulated by the two agreements. Each state offers a (possibly partial) description of the factual and normative propositions that hold true in it. A transition between states corresponds to an event that takes place, i.e., an action that one of the parties performs or omits to perform. Initially, at time point T0, the transaction is in state S0 where the two agreements have been established and no events have occurred yet. If RA places an order at some time after T0, the transaction will move to a state S1, where WA is obliged towards RA to deliver goods within 10 days. Also, CA’s obligation towards WA, to deliver goods to RA on WA’s behalf within 10 days, is active. If CA delivers within the specified time bounds, then the business exchange will move to a state S2, where CA’s obligation (and WA’s obligation towards the RA for delivery, which is related to it) is successfully discharged, and RA’s obligation towards WA to pay becomes active (as does WA’s obligation to pay CA). If, when the transaction is at state S1, CA does not deliver on time, then the transaction will move to some state S3, where WA must compensate RA as specified by their agreement (and CA must compensate WA as specified by their agreement). In the same manner we may discuss other states of the business exchange. To establish the state of the business exchange, given the actions that parties perform or omit to perform, we may employ a representation of the two agreements in Event Calculus [5]. The basic elements of the language are time points, fluents and actions or events. Fluents are factual and normative propositions whose truth-value alters over time, as a result of the occurrence of an action or an event. For our example we adapt the simple EC formalism presented in [15]. In its original form, the formalism does not distinguish between events that are brought about through agents’ actions, and events that are brought about independently of the agents. We preserve the distinction and use the term ‘action’ to refer to the former, and ‘event’ to refer to the latter. We use terms, such as Order(agent1, agent2), for fluents that become true as a result of specific actions (here ordering AOrder(agent1, agent2)). We use terms of the form Op(agent1, agent2, action, time) for fluents that describe normative propositions and their

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intended reading is “agent1 is in legal relation Op towards agent2 to perform action by time”. The legal relation Op may be obligation, prohibition or permission; although these notions are typically formalized in some system of Deontic Logic, we merely use them as descriptive names for fluents, and do not adopt any specific Deontic Logic axiomatization. As [16, 2] note, the effects of an action apply only when the action is considered valid, and this, in turn depends on whether its agent has the legal and practical ability to perform it. An agent’s legal and practical ability with respect to certain actions may be time-dependent, so we use the fluents IPower(agent, action) and PAbility(agent, action) respectively, and the fluent Valid(agent, action) to denote that an action performed by an agent is valid. We employ the six basic predicates of [15]; of those, Initiates and Terminates are used along with Happens in the specific description of a particular contract, to represent causal relations between fluents and actions/events. The other three are defined in domain-independent manner. We have modified the original definition of the HoldsAt predicate to take into account action validity, and have extended the Happens predicate to include the agent of an action as an argument (for events, though, we use the original form of Happens). Table 1. Basic Event Calculus Predicates Initiates(action, fluent, time) / Terminates(action, fluent, time) Initiates(event, fluent, time) / Terminates(event, fluent, time) HoldsAt(fluent, time) Happens(agent, action, time) Happens(event, time) Clipped(time1, fluent, time2) / Declipped(time1, fluent, time2)

fluent starts/stops to hold after action occurs at time. fluent starts/stops to hold after event occurs at time. fluent holds at time. agent performs (instantaneous) action at time. event occurs (instantaneously) at time. fluent is terminated/activated between time1 and time2.

Some of the domain-independent definitions are shown below: Clipped(time1, fluent, time2)← (Happens(agent, action, time) ∧ time1≤ time 0}. Axk+1:k+n k

3.2

(11)

Frontier Based Exploration

In its usual sense, frontier based exploration involves robots moving to a frontier in order to gain some additional information about the environment, where a frontier consists of those areas of the search space about which some information is known and which are adjacent to unknown space. In this work a frontier, Fk ,

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is defined as the set of points from which the target is able to, in the next time step, reach beyond the boundaries of the current search space: xt

k Fk = {xtk |Ak+1:k+1 ⊆ Xkt }.

(12)

˜ tk ∈ Fk , if the value of the posterior distribution is For any frontier node, x greater than zero, ˜ s1:k ) > 0 p(˜ xtk |s ˜ z1:k , x (13) then application of the prediction equation (7) will always result an invalid PDF, as described by (9), implying Pr(xtk+1 ∈ Xkt ) > 0. In such cases, a new search space fXkt should be defined, based on the present search space Xkt and the target’s FRS evaluated at those frontier nodes described by (13), ˜ t+ ∈F x

k k Xkt ≡ Xkt ∪ Ak+1:k+1

f

(14)

˜ s1:k ) > 0}. Before it can be used for RBE, the where xt+ ∈ {xt |p(xt |s z˜1:k , x posterior distribution must be redefined in order to fit this new search space. However this is made straightforward by observing that at time k, Pr(xtk ∈ Xkt ) = 0. Therefore put  ˜ s1:k ) if xtk ∈ Xkt z1:k , x p(xtk |s ˜ t s s ˜ 1:k ) = z1:k , x (15) p(xk | ˜ 0 otherwise for xtk ∈ fXkt . Using this newly defined search space and posterior distribution function the prediction stage may now be performed without loss of information using  t s s ˜ 1:k ) = ˜ s1:k )dxtk . p(xk+1 | z˜1:k , x p(xtk+1 |xtk )p(xtk |s z˜1:k , x (16) fX t k

t The update stage should then be applied using Xk+1 = fXkt . It should be noted that this approach may be applied independently of the method chosen for implementing RBE. Furthermore, as there is no loss of information, the technique requires no change to the established objective function for coordinated SAT.

4

Numerical Examples

This section presents three examples in order to demonstrate the use of FRS frontier based exploration for autonomous SAT. The first example considers only a single target and a single sensor whilst the second and third consider two lost targets and the use of three UAVs. The targets in each of the examples share a common model. They move in the horizontal plane according to t

t

t

t

t

t

t

t

j xk+1 = xkj + Δt · vkj cos γkj j yk+1 = ykj + Δt · vkj sin γkj

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t

where vkj and γkj are, respectively, the speed and direction of the target’s motion due to external disturbances such as wind and current, subject to Gaussian noise. A time increment, Δt = 100s, was used in each example. The sensors also move in the horizontal plane, and share the following model i xsk+1 = xski + Δt · vksi cos(θksi + γksi )

si yk+1 = yksi + Δt · vksi sin(θksi + γksi ) si θk+1 = θksi + γksi

(18)

where vksi is the speed of the sensor platform and γksi is the angle through which the platform turns. Table 1 shows the sensor platform and target control limits used during each of the simulations. Table 1. Vehicle Model Control Limits Maximum Speed [m/s] Minimum Speed [m/s] Maximum Turn Rate [deg/s]

Sensor Platform Target 30 20 10 0 3 60

The probability of detection was given by a zero mean Gaussian distribution with constant covariance. The observation likelihood was given by a zero mean Gaussian distribution with covariance proportional to the distance between the sensor platform and the target. A five step lookahead with receding horizon was used for control optimization. In each of the simulations the sensors are indicated with a cyan circular marker, the targets with a yellow triangular marker. The search space is shown as a colored surface, representing the posterior distribution, against a grey background. 4.1

Single UAV, Single Target

In this example a single UAV searches for and tracks a single lost target over a 20 km × 20 km search space. Figure 1 shows a comparison between static and dynamic search spaces. The top row shows the result of using a static search space. The second row shows the result of using a dynamic search space. When the target was within 1 km of the UAV it was detected by the sensor. The true distance between the sensor and target for each case can be seen in Fig. 1(d). This example clearly highlights the limitations of SAT over static search spaces: once the target drifted beyond the boundary of the search space, Fig. 1(c), the UAV was incapable of maintaining an appropriate PDF of the target, leading to mission failure. The use of a dynamic search space allowed the UAV to keep the detected target within 1 km and therefore within its sensor range for the remainder of the 3 hour mission.

Dynamic Search Spaces

(a) Initial Conditions, k = 1

(d) Distance, Target

Sensor

(b) Static Space, k = 72

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(c) Static Space, k = 110

to (e) Dynamic Space, k = 72 (f) Dynamic Space, k = 110

Fig. 1. Comparison of Static and Dynamic Search Spaces

4.2

Coordinated Search and Tracking - Multiple Targets

In this example three UAVs cooperatively search for and track two lost targets, see Fig. 2. The exploration method was again implemented, with each of the UAVs decentrally determining the required changes in the search space. Both targets were located by k = 30, that is, within 50 minutes. Again the technique allowed the sensors to continuously track the targets over a period of more than 4 hours, even after the targets drifted beyond the initial search boundary. This example shows the ease with which the proposed method can be scaled to include multiple targets and multiple sensor platforms. It should also be noted that the exploration method operates regardless of whether the targets have been detected or not, see Fig. 2(b). This advantage is exploited in the final example. 4.3

Coordinated Search and Tracking - Reduced Initial Space

In this example the initial target locations and PDFs from the previous example were used, however the initial search space was reduced such that its boundary only barely includes the extremities of the PDFs. Figure 3 shows that regardless of this the UAVs were still able to successfully locate and track the two lost targets. Furthermore, a comparison of Figs. 3(b) and 2(b) reveals that as a result of the smaller initial search space, the time taken to locate both of the targets is less than in the previous example (both targets were located by k = 25, under 42 minutes, when the reduced search space was used).

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(a) Searching, k = 1

(b) Target found, k = 25

(c) Tracking, k = 150

Fig. 2. Dynamic Search Spaces for Coordinated Search and Tracking

(a) Searching, k = 1

(b) Targets found, k = 25

(c) Tracking, k = 150

Fig. 3. Search and Tracking Using Reduced Initial Search Space

This example highlights the advantage of the proposed technique, in that a complete representation of the PDFs can be maintained for all time steps, so long as the initial search boundaries completely enclose the initial distributions. This greatly reduces the burden on human mission planners, who when using static search spaces must consider not only the initial PDFs, but also the possible duration of the mission. Also, the reduction in time required to locate the missing targets may be a life saving factor in real marine search and rescue scenarios.

5

Conclusion

A general theoretical approach was presented which enables sensor exploration during recursive Bayesian SAT. This approach may be applied decentrally and independently of the method of Bayesian estimation. It was demonstrated through a number of examples that a sensor with the ability to explore during SAT has an improved chance of succeeding in its mission compared with a sensor with no exploration capabilities, especially over extended time periods. Another advantage of the technique is that the sensor’s ability to explore reduces the burden on mission planners when specifying the initial boundaries of the search space, as the presented technique maintains a complete representation of the target’s PDF for all time steps, provided the prior distribution is complete. Future work focussing

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on search space reduction for areas of low probability density, will aim to reduce the computational burden associated with the increased search space sizes.

Acknowledgements This work is partly supported by the US Air Force Office of Scientific Research (AFOSR) and the ARC Centre of Excellence programme, funded by the Australian Research Council (ARC) and the New South Wales State Government.

References 1. Wong, E.-M., et al.: Multi-vehicle Bayesian search for multiple lost targets. In: Proc. IEEE Int. Conf. Robot. Autom (ICRA 2005) pp. 3180–3185 (2005) 2. Le Cadre, J.-P.: Search and Screening: General Principle with Historical Applications. Alexandria, VA: MORS Heritage Series (1999) 3. Stone, L.D.: Search theory: a mathematical theory for finding lost objects. Mathematics Magazine 50(5), 248–256 (1977) 4. Stone, L.D.: What’s happened in search theory since the 1975 Lanchester prize? Operations Research 37(3), 501–506 (1989) 5. Stone, L.D.: Theory of Optimal Search, Arlington, VA: Operations Research Society of America (ORSA) Books (1989) 6. Bourgault, F., et al.: Process Model, constraints and the coordinated search strategy. In: Proc. IEEE Int. Conf. Robot. Autom (ICRA 2004), vol. 5, pp. 5256–5261 (2004) 7. Bourgault, F., et al.: Coordinated decentralized search for a loast target in a Bayesian world. In: Proc. IEEE/RSJ Int. Conf. Intel. Robot. Sys. pp. 48–53 (2003) 8. Butenko, S., et al.: Recent Developments in Cooperative Control and Optimization. Kluwer Academic Publishers, Boston (2005) 9. Furukawa, T., et al.: Recursive Bayesian search-and-tracking using coordinated UAVs for lost targets. In: Proc. IEEE Int. Conf. Robot. Autom., pp. 2521–2526 (2006) 10. Yamauchi, B.: Frontier-based exploration using multiple robots. In: Proc. 2nd Int. Conf. Autonomous Agents, pp. 47–53 (1998) 11. Choset, H., Nagatani, K.: Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization. IEEE Trans. Robot. Autom. 17(2), 125–137 (2001) 12. Howard, A., et al.: Multirobot simultaneous localization and mapping using manifold representations. Proc. IEEE 94(7), 1360–1369 (2000) 13. Burgard, W., et al.: Coordinated multi-robot exploration. IEEE Trans. Robot. 21(3), 376–386 (2005) 14. Arulampalam, M.S., et al.: A tutorial on particle filters for on-line non-liner/nonGaussian Bayesian tracking. IEEE Trans. Sig. Proc. 50(2), 174–188 (2002)

Composite Endoscope Images from Massive Inner Intestine Photos Eunjung Kim1 , Kwan-Hee Yoo2 , Je-Hoon Lee1 , Yong-Dae Kim1 , and Younggap You1 1

2

Dept. of Computer and Communication Eng., Chungbuk Nat’l Univ, San 12, Gaeshin-dong, Cheongju, Chugnbuk, Korea [email protected] Dept. of Computer Education, Chungbuk Nat’l University, San 12, Gaeshin-dong, Cheongju, Chugnbuk, Korea

Abstract. This paper presented an image reconstruction method for a capsule endoscope. The proposed method constructs a 3–D model of the intestine using massive images obtained from the capsule endoscope. It merges all images and yields a complete 3-D model of the intestine. This 3-D model is reformed as a 2-D plane image showing the inner side of the entire intestine. The proposed image composition has been evaluated using the OpenGL 3-D simulator. The composite image provides an easyto-understand view for examining the intestine. In addition, it provides fast track-and-check diagnosis using the 3-D model implementation. Keywords: Capsule endoscope, image composition, 3-D modeling, bioinformatics.

1

Introduction

There are two types of endoscopes to examine the GI (gastrointestinal) track; a wired endoscope and a capsule endoscope. The wired endoscope can easily diagnose the GI track using the real image and catch the biopsy sample. It causes discomfort to the patient when a physician pushes the cable into the patient body. Further, it is difficult to reach the small intestine using the wired endoscope. However, the capsule endoscope can directly examine the entire gastrointestinal tract, including the small intestine. And the capsule endoscope is easier to swallow [1–2]. It observes the inside of the small intestine and transfers the pictures obtained from the capsule [3–6]. There has been a substantial amount of technical improvements to make the capsule endoscope complete. The captured images may suffer substantial nonlinear spatial distortion, which obstructs accurate medical examination. The calibration will help to restore the original image. There are many researchers devoted to improving image quality [7–12]. S. Pongnumkul et al. proposed an image reconfiguration method to change the video stream captured by the endoscope to a 2-D image, to reduce the time required for diagnosis [10]. H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 1042–1051, 2007. c Springer-Verlag Berlin Heidelberg 2007 

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The main goal of a capsule endoscope is to form accurate diagnosis in a short time. The capsule endoscope transfers the image data to the diagnose system at a rate of two frames per second. The capsule transmits about 50,000 images for each patient. This causes some problems for fast diagnosis. The images are reviewed by video at speeds of about 20 [frame/second]. The time spent watching the video is over 42 minutes. It takes a long time for the physician to diagnose a patient, because only a fraction of this video is needed. This paper proposes an image reconstruction method. The intestine model is similar to that of the patient model. This 3-D model is reformed as a 2-D plane image. We generate a dissection image of an intestine from the image sequence, where the dissection image looks like an image obtained by cutting the intestine lengthwise and opening its inside. The physician can diagnose this image at a glance and thereby saves time. This paper is organized as follows. Section 2 describes the modeling environment. Section 3 describes the transformation method from a photo image into a planar image. Section 4 introduces 3-D image reconstruction from a planar image. Section 5 presents the experimental results. Section 6 concludes this paper.

2

Modeling Environment

This section explains the three dimensional modeling of the intestine based on the pictures from the capsule camera. The position and attitude data are attached to the images captured by the endoscope, which will help formulate precise diagnosis. A cylindrical intestine model utilizes the information on the camera location and attitude for each intestine image from the capsule. The small intestine of a human being is about 6 meters in length, including duodenum, jejunum, and ileum as shown in Fig. 1. The average diameter is about 20 mm for adults. The small intestine is constantly in motion, for digestion and absorption.

Fig. 1. The capsule endoscope and the intestine

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Fig. 2. Cylindrical model of an intestine

Fig. 3. The parameters for cylindrical modeling

The three dimensional model of the intestine image leads to a composite image representing massive capsule photos. An intestine is assumed to be a series of piecewise cylindrical pieces as shown in Fig. 2, with each cylindrical piece of differing radius and height. The ith piece has a radius of ri and a height of hi . The process of three dimensional modeling begins with photo images from the capsule endoscope. Each image carries the position and attitude information of a capsule endoscope in the human body. Each image experiences a different camera setting of image center position, direction vector, fovy, near and far point, and aspect ratio, as shown in Fig. 3. The coordinate of the capsule endoscope is (x, y, z) in the Cartesian coordinate system. The center position represents the coordinate of the capsule camera lens. The rotation angle (α, β, γ) changes with capsule movement. We use the position and orientation tracking method proposed by Wang and Meng [13]. The radius and total height of the 3D model cylinder are assumed 30.0 mm and 200.0 mm, respectively. The fovy is assumed 100◦ . The near and far points are 4.5 mm and 25 mm, respectively. The camera focuses on objects between 4.5 mm and 25 mm. The aspect ratio is the ratio of the height and the width of an image. This model assumes an aspect ratio of 1. The default direction vector is defined (0.0, 0.0, -1.0).

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Transformation from a Photo Images into a Planar Images

The photos from a capsule camera are the raw data for constructing a three dimensional cylindrical model, which is then transformed into a two dimensional planar composite image. The image is equivalent to the inner wall of the cylinder model inside the view frustum of the capsule camera. The camera’s view frustum can be obtained from the fovy, aspect ratio, near and far planes. The view frustum with their values is shown in Fig. 4a. Here xmin , ymin , and -near values are coordinates of a lower left corner point and also xmax , ymax and -far values are coordinates of a higher right corner point. Let Int(V,C) be the intersection area of the original view frustum and the given cylinder. In order to compute Int(V,C) efficiently, we use the transform matrix, Mpers→cv like Eq. 1, by which the original view frustum is transformed into a canonical view volume as shown in Fig. 4b. Clearly, the problem to compute Int(V,C) is equivalent to the clipping problem of the transformed line segments with respect to the canonical view volume, where the used line segments are obtained from multiplying Mpers→cv to end points of each line segment modeling the given cylinder. By applying the 3D clipping algorithm proposed by Cyrus and Back [14], we can identify a portion of the transformed line segments inside the canonical view volume. ⎛ 2(−near) ⎞⎛ ⎞ xmax +xmin 0 0 x xmax −xmin xmax −xmin ⎜ ⎟⎜ ⎟ 2(−near) ymax +ymin 0 0 ⎜ ⎟ ⎜y⎟ ymax −ymin ymax −ymin Mpers→cv = ⎜ (1) ⎝z⎠ f ar+near 2f ar+near ⎟ ⎝ ⎠ 0 0 − f ar−near − f ar−near 1 0 0 1 0 The identified line segments are projected orthogonally on the normalized view rectangle as shown in Fig. 4b, which is a rectangle on the near plane of the canonical view volume. With the orthogonally projected line segments, we can determine the corresponding positions of the captured image, through the mapping function between the normalized view rectangle and the captured image rectangle. Clearly, we can apply the methods so that the mapped texture, M Ti , on Int(Vi ,C) is computed efficiently. Finally, we have to decide where the mapped texture is mapped into the portions of the composite image. Let S = (S1 , S2 , · · · , Sk ), Si =(CLi , CHi ) be the set of line segments of Int(V, C) on the cylinder surface. CLi and CHi are the (x, y, z) coordinate values of the low point and the high point, respectively. The reconstruction of the image employs a set of foregoing line segments S. It is used to find a place corresponding to an intersected line segment Si =(CLi , CHi ) as Eq. 2. The foregoing transformation method solves this problem, by transforming the cylindrical coordinate into a texture coordinate. k CLiy k CHiy ), CHi = ( , ) (2) CLi = ( , n h n h Here, k is the sequence of the line segment that belongs to the intersection area of the original cylinder and the view frustum. A captured image is obtained

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Fig. 4. Projection normalization

by projecting the photo corresponding to the normalized view angle and the texture coordinate. The pinnacle points p=(nucx, nucy) of each line segment corresponds to the point of (CWi × nucx, CHi × nucy) for the two dimensional image size of (CWi , CHi ). Here, nucx and nucy are the x and y coordinates in the canonical view volume, respectively. Figure 4 shows the correspondence between them. Figure 4b shows the line segments within the normalized view rectangle. Figure 4c shows the line segments mapped on the captured image. (CWi , CHi ) is the captured image from the ith photo.

4

3-D Image Reconstruction from a Planar Image

A composite image is formed by mapping captured images on a two-dimensional plane. A two-dimensional planar image is obtained from the massive intestine photos, from a capsule endoscope employing the foregoing transformation scheme. It is possible to perform a texture mapping of the photos from the capsule camera on the two dimensional planar image. The intersected areas are filled with the best quality pixels or interpolated data. The piecewise cylindrical model described in the previous section becomes the basis of the three dimensional modeling of the intestine. The model of the intestine leads to the reconstruction of a two dimensional composite image. Figure 5a shows the cylindrical model of the intestine. The first step of the transformation is the conversion of cylindrical images into planar images. The origin of a Cartesian coordinate is the center of the cylinder, where its radius is r. The top and bottom of the cylinder are h/2, -h/2, respectively. The high and low points denote the points on the top and the bottom circumferences of the cylinder, respectively. The Eq. 3 of (Hxi , Hyi ,Hzi ,) and (Lxi , Lyi ,Lzi ,) define traces of high and low points of the ith cylinder, respectively. The angle value, Θi of the equation represents the angle between the vector from the center to the high

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Fig. 5. Reconstruction method: (a) the 3-D cylinder model; (b) the normalized texture coordinate; (c) the composite image

or low point and the reference vector when the circumference is divided into n pieces. Θi = i × ΔΘ, ΔΘ = 2π n Hxi = 2πr × cos(Θi ), Hyi = h2 , Hzi = 2πr × sin(Θi ) (3) Lxi = 2πr × cos(Θi ), Lyi = − h2 , Lzi = 2πr × sin(Θi ) (where, i = 0, 1, . . . , n) An image on the inner cylinder wall is transformed into normalized coordinate values of the two dimensional planar form through a mapping defined by Eq 3. The second step is the mapping of the three dimensional cylindrical image into the two dimensional image as shown in Fig. 5b. The coordinates of points on the high and low points of the two dimensional image are represented as THi and TLi , respectively. The points Hi and Li on the three dimensional cylinder are mapped on THi and TLi , respectively. THui and THvi represent the x and y coordinate values of a point (Hxi , Hyi , Hzi ) on high points after normalization and mapping on the two dimensional image, respectively. TLui and TLvi represent the x and y coordinates of a point (Lxi , Lyi , Lzi ) on low points after normalization and mapping on the two dimensional image, respectively. The coordinate values of THui and TLui are obtained from Eq 4. THui = TLui =

i n , THvi = 1 i n , TLvi = 0

(4)

The third step is to perform texture mapping on the normalized coordinate values of Fig. 5b, yielding the result shown in Fig. 5c. The size of the output image is obtained by multiplying the width, CW and height, CH values on the normalized x and y values of the two dimensional image, respectively, as shown in Fig. 5c.

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Simulation Results

The proposed scheme was implemented using the OpenGL three dimensional simulator of MS Visual Studio. Figure 6 shows the transformation process of generating a composite image. Figure 6a shows the mapping of a series of two dimensional consecutive photos on a three dimensional cylinder. The parallel dotted lines represent the intersected area of a view frustum and the cylinder. This area contains all the image data captured by the capsule camera. Figure 6b shows the two dimensional image from the capsule camera. Figure 6c describes the capsule information on the center position, direction vector, fovy, near and far point and aspect ratio. Figure 6d shows the partial composite image projected on the intersection areas of the cylinder and the view frustum. Figure 7a shows the eye point of (4.00, 22.20, -1.00), and the center position of (0.30, -0.20, 0.00). Figure 7b shows the eye point of (4.60, 26.10, -3.10), and the center position of (-0.30, -5.60, 0.00). Figure 7c shows the eye point of (30.10, 10.30, 6.90), and the center position of (-0.00, -1.30, 0.80). The resultant composite image obtained by overlapping Figures 7a, 7b and 7c is shown in Fig. 7d. The photos are reconstructed as a two–dimensional planar image. Figure 8 shows the photo taken inside the intestine after the three dimensional cylindrical modeling. The doctors can examine modeling photos by moving camera positions down to the outside areas of Fig. 9. This single composite image

Fig. 6. The 3-D model for a small intestine and reconstructed 2-D image: (a) 3-D Cylinder Modeling, (b) Captured 2-D Image, (c) Capsule Endoscope Position and Camera Parameter, (d) Composite Image

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Fig. 7. Process of a composite image construction

Fig. 8. The picture captured at the front of a small intestine

Fig. 9. The picture captured at the end of a small intestine

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can save a substantial amount of diagnosis time to find the location of a lesion inside the intestine.

6

Conclusion

This paper presents an image reconstructing method for a capsule endoscope. The capsule endoscope transmits about 50,000 images. A physician spends almost 2 hours to examine a patient’s intestine because he looks at only one small fragment at a time. The proposed method creates a three-dimensional model of a small intestine that includes all images obtained from the capsule endoscope. We assume that the shape of a small intestine consists of a series of consecutive cylinders. The photos obtained from the capsule endoscope contain the image information of the intersection between the cylinder model and the view frustum. The clipping algorithm is used to get the image information from the intersection. We use the perspective transformation method to reconstruct the planar image. It generates a three dimensional model and a planar image. The proposed image composition is evaluated using the OpenGL three dimensional simulator. This simulation result shows that a composite endoscope image is obtained from the massive inner intestine photos. The physician can diagnose this two dimensional image at a glance. They can freely track and check the location using the implemented three-dimensional model if they find an abnormal section in a planar image. This result allows a physician to diagnose a patient over a short time period. In future work, we will make an improved model to overlap each cylinder model of a small intestine. Acknowledgments. This work was supported by the Regional Research Centers Program of the Ministry of Education & Human Resources Development in Korea. And Dr. J. H. Lee participated in this work is supported by the Second Phase of the Brain Korea 21 Project at Chungbuk National University.

References 1. Iddan, G., Meron, G., Glukhovsky, A., Swain, P.: Wireless capsule endoscopy. Nature 405, 417–420 (2000) 2. Ginsberg, G.G., Barkun, A.N., Bosco, J.J., Isenberg, G.A., Nguyen, C.C., Petersen, B.T., Silverman, W.B., Slivka, A., Taitelbaum, G.: Wireless capsule endoscopy. Gastrointestinal Endoscopy 56(5), 621–624 (2002) 3. Tang, T.B., Johannessen, E.A., Wang, L., Astaras, A., Ahmadian, M., Murray, A.F., Cooper, J.M., Beaumont, S.P., Flynn, B.W., Cumming, D.R.S.: Toward a miniature wireless integrated multisensor microsystem for industrial and biomedical application. IEEE Sensors Journal 2(6), 628–635 (2002) 4. Lin, M.C., Dung, L R, Weng, P.K.: An ultra–low–power image compressor for capsule endoscope. BioMedical Engineering Online , 1–8 (2006) 5. Park, H.J., Park, J.C., Lee, J.H., Moon, Y.K., Song, B.S., Won, C.H., Choi, H.C., Lee, J.T., Cho, J.H.: New method of moving control for wireless endoscopic capsule using electrical stimulus. In: Proc. of Int’l Technical Conference on Circuits, Systems, Computers and Communications, pp.7E1L–1–1–7E1L–1–4 (2004)

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6. Shahidi, R., Bax, M.R., Maurer, C.R., Johnson, J.A., Wilkinson, E.P., Wang, B., West, J.B., Citardi, M.J., Manwaring, K.H., Khadem, R.: Implementation, calibration and accuracy testing of an image–enhanced endoscopy system. IEEE Trans. on Medical Imaging 21(12), 1524–1535 (2002) 7. Hu, C., Meng, M., Liu, P.X., Wang, X.: Image distortion correction for wireless capsule endoscope. In: Proc. IEEE Int’l Conference on Robotics and Automation, pp. 4718–4723 (2004) 8. Asari, K.V., Kumar, S., Radhakrishnan, D.: A new approach for nonlinear distortion correction in endoscopic images based on least squares estimation. IEEE Trans. On Medical Imaging 18(4), 345–354 (1999) 9. Kim, B., Lee, S., Park, J.H., Park, J.O.: Design and fabrication of a locomotive mechanism for capsule–type endoscopes using shape memory alloys (SMAs). IEEE Trans. on Mechatronics 10(1), 77–86 (2005) 10. Pongnumkul, S., Sagawa, R., Echigo, T., Yagi, Y.: Deformable registration for generating dissection image of an intestine from annular image sequence. In: Liu, Y., Jiang, T., Zhang, C. (eds.) CVBIA 2005. LNCS, vol. 3765, pp. 271–280. Springer, Heidelberg (2005) 11. Smith, W.E., Vakil, N., Maislin, S.A.: Correction of distortion in endoscope image. IEEE Trans. on Medical Imaging 11(1), 117–122 (1992) 12. Mranda-Luna, R., Blondel, W.C.P.M., Daul, C., Hermandez-Mier, Y., Posada, P., Wolf, D.: A simplified method of endoscopic image distortion correction based on grey level registration. In: Proc. 2004 Int’l Conf. on Image Processing, pp. 3383– 3386 (2004) 13. Wang, X., Meng, M.Q.H.: Study of a position and orientation tracking method for wireless capsule endoscope. Int’l Journal of Information acquisition 2(2), 113–121 (2005) 14. Cyrus, M., Back, J.: Generalized two–and three–dimensional clipping. Computer and Graphics 3, 23–28 (1978)

Using Trust in Collaborative Filtering Recommendation Chein-Shung Hwang and Yu-Pin Chen Department of Information Management, Chinese Culture University, 55, Hwa-Kang Road, Yang-Ming-Shan, Taipei, Taiwan, R.O.C. [email protected], [email protected]

Abstract. Collaborative filtering (CF) technique has been widely used in recommending items of interest to users based on social relationships. The notion of trust is emerging as an important facet of relationships in social networks. In this paper, we present an improved mechanism to the standard CF techniques by incorporating trust into CF recommendation process. We derive the trust score directly from the user rating data and exploit the trust propagation in the trust web. The overall performance of our trust-based recommender system is presented and favorably compared to other approaches. Keywords: Trust, Collaborative Filtering, Recommender System.

1

Introduction

The ever-increasing popularity of the Internet has led to an explosive growth of the sheer volume of data. Recommender systems are emergent to solve the information overload problem by suggesting users items that they might like or find interested. Collaborative filtering (CF) [1][2][3] is one of the most successful and widely used recommender systems. The main idea behind CF model is to automate the process of ’word-of-mouth’ by which people recommend items to one another. For each user, CF model uses historical information to identify a neighborhood of people who have shown similar behavior in the past and then predicts the interest of new items by analyzing the neighborhood. The formation of neighborhood requires the computation and comparison between current user and every other user based on their ratings data. However, the number of ratings already collected is very small compared to the number of ratings needed to provide a prediction. As a result, CF model often has difficulty in finding a sufficient number of similar neighbors for a user and providing an effective recommendation. Recently, several researches have suggested that the incorporation of a notion of trust into the standard CF model can effectively solve the sparsity problem and thus provide better recommendations. A user can build his personalized web of trust by specifying those friends or users he trusts. The trust web can be constructed through the explicit trust ratings provided by users. For example, Massa et al. [4] build a trust model directly from users’ direct feedbacks. H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 1052–1060, 2007. c Springer-Verlag Berlin Heidelberg 2007 

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This trust model is incorporated into the recommendation process for recommending various items (such as books, movie, music, software etc.) to on-line users. Users can express their personal web of trust by identifying those reviewers whose reviews and ratings are consistently found to be valuable. Massa et al. argue that it is possible to predict trust in unknown users by propagating trust even there were no direct connection between them. They also show, in their subsequent experiment [5], that the incorporation of trust metric and similarity metric can increase the coverage of recommender systems while maintaining the recommendation accuracy. Due to the limitation on trust value representation, in their experiments, the webs of trust are built on binary relationships among users and the propagating trusts are computed simply based on the distances between them. Avesain et al. [6] apply the trust model into the ski mountaineering domain. They present a community-based website in which users can share their opinions about the snow conditions of different ski routes and also express their trust on others’ opinions. The trust score of a user depends on the trust statements of other users on him/her and their trust scores. However, the trust model requires the direct feedback of users and the effectiveness of the trust model on the skiing community has not been validated. Golbeck et al. [7] describe an E mail filtering system based on trust ratings. The predicted trust of a user is given by a weighted average of her neighbors’ trust ratings. They have shown that the weighted average metric can provide better results than other metrics. However they still need the explicit trust ratings from users and do not use any mail ratings information. The explicit user participation for providing his trustworthiness to one another suffers from some limitations such as additional user effort and cold start that new users have to build up their trust webs before the filtering is effective. Alternatively, the trust web can be implicitly and directly derived from the item ratings data. Pitsilis et al. [8] view trust as a form of opinions which are always subjective and uncertain. Every opinion is expressed as a three-dimensional metric comprising belief, disbelief and uncertainty. The uncertainty is modeled from prediction error and the levels of belief and disbelief are derived based on correlation between every pair of users. The system presents a comparative performance to Beta distribution approach. However, no comparison to the traditional CF has been performed and reported. Donovan et al. [9] claim that the reliability of a user profile to deliver accurate recommendation in the past is an important factor for influencing recommendation and prediction. A user is viewed as more trustworthy if he has made more accurate predictions in the past than other users. The trust metrics are calculated at both the Item and Profile levels. Essentially these metrics summarize the relative number of correct recommendations that a given user has made, according to a predefined error bound. They have shown that the incorporation of trust metric into a standard CF has a positive impact on the prediction quality. However, this system only uses a global trust metric and provides neither any personalization nor trust propagation.

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In general, while local trust metrics can be more precise and personalized than global trust metrics, they are also computationally more expensive. In this paper, we present an improved mechanism to the existing trust-based CF techniques. In particular, we will discuss how the local trust metrics can be incorporated into CF process and be efficiently propagated in the trust web. Formally, we aim to tackle the following problems: 1. How to directly derive trust ratings score from item ratings data? 2. How to define the global trust metric and the local trust metric? 3. How to propagate the trust score in the trust web?

2

System Architecture

The main goal of our study is to design an effective recommender system by integrating trust metric into the traditional CF process. The proposed system consists of three modules: Trust Computation (TC) module, Similarity Computation (SC) module, and Rating Prediction (RP) module as shown in Fig. 1.

Fig. 1. System Architecture

The overall system can be viewed as a blackbox which takes as input the rating matrix and produces, as output, a prediction matrix. The ratings matrix R contains the rating scores ri,k standing for the rating of user ui for item ik , which can be either a numerical scale (representing his opinion) or ⊥(representing no rating). The TC module derives the trust score directly from rating data and computes the propagated trust. The SC module computes the correlation coefficient between each pair of users. The RP module integrates the trust matrix and the similarity matrix to produce predictions for unseen items. 2.1

Trust Computation Module

The TC module involves a lot of works as shown in Fig. 2. Trust derivation module takes as input the rating matrix and computes the direct trust score of each pair of users. For every user, two trust metrics are computed. Global trust metric measures every user’s global trust score reflecting the trustworthiness of all other users with the target user. The global trust score of a user is the same for every user. Local trust metric computes a user’s trustworthiness with

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respect to another user. Local trust metric takes as input the direct trust relationships resulting from the Trust derivation module and exploits the indirect trust relationships through trust propagation and path composition.

Fig. 2. Trust Computation Module

Trust Deviation. We believe that prediction accuracy of a user in the past is an important factor for measuring the trustworthiness of him. Therefore if a user delivers high accurate recommendations to another user in the past, then he is trustworthy and should obtain a high trust score from that user. Our system uses a simple version of Resnick’s prediction formula [10] to compute the predicted rating. The predicted rating of item i for user ua by another user ub is given as follow: pba,i = r a + (rb,i − r b ) , (1) where r a and r b refer to the mean ratings of ua and ub , respectively, and rb,i is the rating of item i given by ub . The trust score of ua with respect to ub is then derived by averaging the prediction error of co-rated items between them.    |pba,i − ra,i | 1 ta→b = , (2) 1− n(Ia ∩ Ib ) m i∈(Ia ∩Ib )

where Ia and Ib refer to the set of rated items of ua and ub , respectively, and m is the size of the rating range. It should be noted that the computation of the trust score is performed based on the co-rated items. Global Trust Metric. A user’s global trust with respective to another user combines the local trust with recommendations received from other users. We define the global trust score of a user ua as the average of the local trust scores given by neighbors who are directly connected to ua in the trust web. gta =

1 n(N B(ua ))

 j∈N B(ua )

where N B(ua ) is the neighborhood of ua .

tj→a ,

(3)

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Trust Propagation Metric. Due to the large number of items existing in a recommender system, the ratings matrix is very sparse. The sparsity of rating matrix often makes two users have no co-rated items, which results in no direct trust relationships between them. The problem can be handled by means of trust propagation to infer the indirect relationships. Trust propagation implies that, in the trust web, there exists a trust path between a source user us and a target user ut . Suppose that there is an intermediate user um in the trust path connected us and ut . The inferred trust score of ut given by us through um is computed by the weighted average of the two direct relationships of us → um and um → ut [11]. ts→ t = ts→m ⊕ tm→t = m

n(Is ∩ Im )ts→m + n(Im ∩ It )tm→t , n(Is ∩ Im ) + n(Im ∩ It )

(4)

The rational behind this computation is that if two users have more co-rated items then their direct relationship should be more reliable and desiring more weight. The propagation operator can be repetitively applied for computing the indirect trust relationship between any two users in the trust web. Path Composition. It is possible that there are multiple paths between two users in the trust web. Each path contributes its own inferred trust score. The inferred trust score in each path is independent of each other. We need to decide how to combine these trust scores to give a single composite measure. In our current study, we simply compute the average of all the inferred trust scores contributed by each of the alternative paths. 2.2

Similarity Computation Module

The SC module is one of the standard steps in the standard CF algorithms. SC module computes the similarity between users. Recent studies [12][13] have shown a strong and significant correlation between trust and similarity. The more similar the two users are the higher trust they have. We take the ratings matrix as an input and produce a similarity matrix containing the similarity value of any user against every other user. We calculate the similarity as Pearson correlation coefficient [14].  i∈(Ia ∩Ib ) (ra,i − r a ) (rb,i − r b ) sima,b =  . (5) 2 2 (r − r ) (r − r ) a,i a b,i b i∈(Ia ∩Ib ) i∈(Ia ∩Ib ) 2.3

Rating Prediction Module

The RP module is the final step in the standard CF algorithms. A common used algorithm is Resnick’s standard prediction formula [10]. The predicted rating of item i for a user ua is the weighted sum of the ratings given by users in his neighborhood.  k∈N B(ua ) (rk,i − r k ) wa,k  pa,i = r a + , (6) k∈N B(ua ) wa,k

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where, wa,k represents the weight of user ua assigned to his neighbor uk . wa,k can be taken either from the similarity score sima,k , the local trust score ta→k or the global trust score gtk .

3 3.1

Experimental Evaluation Data Sets

We use the movielens dataset collected by the GroupLens Research at the University of Minnesota. It contains 100,000 ratings from 943 users for 1628 movies. Each user has rated at least 20 movies, and each movie has been rated at least once. The original data set was converted into a new user-movie matrix R that had 943 rows (i.e. 943 users) and 1682 columns (i.e. 1682 movies). We employ the 5-fold cross-validation approach. First, we randomly divide the dataset into five groups. Then we run five rounds of tests, each time choosing one group of data as test data and the other four groups as training data. The training set is used to generate the recommendation model. For each user in the test data, we employ the AllButOne protocol in which one item is selected at a time as the predicated item; all other ratings are used as input to the system. Our recommender system is then evaluated by comparing the predicted ratings with the actual ratings of the selected items. 3.2

Evaluation Metrics

To measure the accuracy of the recommendations we computed the standard Mean Absolute Error (MAE) between ratings and predictions in the test data. MAE is a measure of the deviation of recommendations from their actual ratings. Specifically, given the set of actual/predicted pairs (ra,i ,pa,i ) for all the movies rated by user ua , the MAE for user ua is computed as:  i∈R(ua ) |ra,i − pa,i | , (7) M AEa = n(R(ua )) where R(ua ) represents the set of items that are rated by ua . The overall MAE is computed by averaging these individual MAEs over all users in the test data. Another important measure for discriminating between different recommendation approaches is coverage. Coverage is a measure of percentage that a recommender system can provide predictions. A prediction is impossible to be computed only when very people rated the movie or the active user has no correlation with other users. So a movie is predictable even only two users have rated it. Our pilot study reveals a near perfect coverage (around 99% in all experiments). To make a reasonable comparison,we examine the change of coverage with respect to different sparsity of user rating data.

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Performance Results

Table 1 shows the prediction accuracy of different trust settings in our recommender system in contrast to those produced by standard CF technique. In all experiments, we compare the recommendation quality of different trust settings in our recommender system with those produced by standard CF technique. We compare the global trust model to local trust model with different maximum propagation distance, precisely, 1, 2, 3, and 4. We define as global-CF the method that employs the global trust metric in the CF process. Local-CF-n represents the method in which the local trust metric with maximum propagation distance n is used. The MAE is expressed with respect to different neighborhood sizes. Table 1. MAE of trust-based CFs vs.standard CF recommendation Size of NB Standard CF Global-CF Local-CF-1 Local-CF-2 Local-CF-3 Local-CF-4 10 0.893 0.879 0.870 0.864 0.861 0.862 20 0.861 0.845 0.831 0.822 0.816 0.816 30 0.832 0.829 0.802 0.794 0.790 0.790 50 0.801 0.805 0.776 0.769 0.767 0.768 70 0.763 0.784 0.752 0.744 0.742 0.742 90 0.763 0.782 0.753 0.746 0.744 0.744 120 0.764 0.785 0.755 0.749 0.745 0.745 150 0.767 0.786 0.757 0.752 0.750 0.750

In all approaches, the prediction accuracy improves as the number of neighbors increases but they reach the maximum performance at around 70 neighbors and any further increment makes no better or even worse results. However, the trust enhanced approaches result in an overall improvement in accuracy. Specifically, the local trust CFs perform the best under all cases and the global trust CF is slight better than the standard CF in a small neighborhood, but performs worse with increasing number of neighbors. The performance of the local trust CF varies with the propagation distances and reaches the best when the distance is 3. As discussed earlier, sparsity of ratings is one of the common problems that collaborative recommender system may encounter. The sparsity problem is one major reason causing poor prediction quality. In this experiment, we examine the effectiveness of trust in solving the sparsity problem. To evaluate the coverage of different approaches, we relax the size of neighborhood to all users, and perform an experiment with different sparsity levels. Table 2 shows the result of coverage for different CF approaches. As expected, when the sparsity level increases, the coverage drops gradually. The global-CF has the highest coverage as all users are involved for recommendations. Local-CF-1 gains a slight improvement in coverage compared with the standard CF. The coverage increases when the maximum propagation distance increases. Local-CF-2 provides a large improvement over Local-CF-1, but the improvement starts to converge to 0 with increasing propagation distance.

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Table 2. Coverage of different CF approaches Sparsity (%) Standard CF Global-CF Local-CF-1 Local-CF-2 Local-CF-3 Local-CF-4 96.0 0.942 0.998 0.961 0.991 0.993 0.993 96.5 0.934 0.996 0.956 0.990 0.992 0.993 97.0 0.932 0.994 0.951 0.990 0.992 0.992 97.5 0.922 0.991 0.948 0.988 0.990 0.990 98.0 0.913 0.989 0.933 0.982 0.984 0.985 98.5 0.804 0.984 0.846 0.953 0.958 0.959 99.0 0.597 0.977 0.627 0.925 0.928 0.929 99.2 0.437 0.962 0.474 0.875 0.881 0.883 99.5 0.277 0.968 0.372 0.785 0.789 0.791

4

Discussion and Future Work

In this paper we have presented a trust-based CF recommender system which incorporates the trust notion into the standard CF process. We derive the trust score directly from the ratings data based on users’ prediction accuracy in the past. We investigate the effects of both the local trust metric and the global trust metric in the standard CF recommendation. The global metric has shown to have an advantage over other approaches in prediction coverage. The local metrics provide more accurate recommendations than those provided by standard CF technique. Experimental results verify that the incorporation of trust into CF process can indeed improve the prediction accuracy while maintain satisfactory prediction coverage. We have described the proposed trust-based CF approach in the context of the movie domain. A further application to a range of other domains would be investigated. In fact, we would suggest that any social community network could benefit from the web of trust, assuming that the ratings data are available.

References 1. Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating ’Word of Mouth’. In: Proceedings of Human Factors in Computing Systems, pp. 10–217 (1995) 2. Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms For Collaborative Filtering. In: Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998) 3. Herlocker, J., Konstan, J.A., Terveen, L., Riedl, J.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems 22, 5–53 (2004) 4. Massa, P., Bhattacharjee, B.: Using Trust in Recommender Systems: An Experimental Analysis. In: Proceedings of the 2nd International Conference on Trust Management, Oxford, England, pp. 221–235 (2004) 5. Massa, P., Avesani, P.: Trust-Aware Collaborative Filtering for Recommender Systems. In: Proceedings of the International Conference on Cooperative Information Systems (CoopIS), Agia Napa, Cyprus, pp. 492–508 (2004)

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6. Avesani, P., Massa, P., Tiella, R.: Moleskiing: A Trust-Aware Decentralized Recommender System. In: Proceedings of the First Workshop on Friend of a Friend Social Networking and the Semantic Web, Galway, Ireland (2004) 7. Golbeck, J., Hendler, J.: Reputation Network Analysis for Email Filtering. In: Proceedings of the First Conference on Email and Anti-Spam, Mountain View, California (2004) 8. Pitsilis, G., Marshall, L.: A Model of Trust Derivation from Evidence for Use in. Recommendation Systems. Technical Report, University of Newcastle Upon-Type (2004) 9. O’Donovan, J., Smyth, B.: Trust in recommender systems. In: Proceedings of the 10th international conference on Intelligent user interfaces, pp. 167–174 (2005) 10. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: An Open Architecture for Collaborative Filtering of Netnews. In: Proceedings of ACM CSCW’94 Conference on Computer-Supported Cooperative Work, Sharing Information and Creating Meaning, pp. 175–186. ACM Press, New York (1994) 11. Papagelis, M., Plexousakis, D., Kutsuras, T.: Alleviating the sparsity problem of collaborative filtering using trust inferences. In: Herrmann, P., Issarny, V., Shiu, S.C.K. (eds.) iTrust 2005. LNCS, vol. 3477, pp. 224–239. Springer, Heidelberg (2005) 12. Ziegler, C., Georg, L.: Analyzing Correlation Between Trust and User similarity in Online Communities. In: Proceedings of Second International Conference on Trust Management, pp. 251–265 (2004) 13. Abdul-Rahman, A., Hailes, S.: Support Trust in Virtual Communities. In: Proceedings of the 33rd Hawaii International on System science. Maui, Hawaii, USA, pp. 1769–1777 (2000) 14. Pearson, K.: Mathematical contribution to the theory of evolution: VII, on the correlation of characters not quantitatively measurable. Phil. Trans. R. Soc. Lond. A 195, 1–47 (1900)

AdaptRank: A Hybrid Method for Improving Recommendation Recall Maciej Kiewra1 and Ngoc Thanh Nguyen2 1

Fujitsu Spain Services, C/ Camino Cerro de los Gamos, 128224 Pozuelo de Alarcon, Madrid, Spain [email protected] 2 Institute of Information Science & Engineering, Wroclaw University of Technology, Poland [email protected]

Abstract. A hybrid recommendation method is presented in this paper. Its main goal is to improve recommendation recall maintaining high recommendation precision and adaptive ability. The formal model is used to define the method and to analyze how the measures known from traditional Information Retrieval may be adapted to recommendation. The presented theorems show that the method is able to adapt to changing user’s needs and achieve the maximal effectiveness if the component methods work properly.

1 Introduction Hundreds of recommender systems have appeared during more than ten years of investigations related to recommendation in hypertext environment [6], [9], [14]. Their main goal is to select from a set of items, a subset of them that are relevant for the user needs. The selection process is usually iterative and it is based on observing user behavior and characteristics of visited items. Researchers noticed relatively early that combining two or more methods of recommendation had a lot of benefits [2], [11]. There are two main types of component methods that can be used to obtain a new hybrid method: content-based filtering and collaborative filtering. The former assumes that the recommended items should be “somehow similar” to the items that have been seen or rated as valuable by the current user. The latter recommends such items that were interesting for the users whose behavior or features are similar to the current user’s counterparts. Many different algorithms of component methods integration have been presented including interesting surveys [1], [4]. All of them can be divided in seven groups (following the classification presented in [4]). First of them known as weighted integration assumes that component methods assign the number that reflects estimated relevance to each item. The hybrid method combines these numbers by means of weighted sum. The items that possess the highest value of the total number are recommended to the user. The second approach labeled as switching uses only one component method in each iteration, replacing it by the other if the user has not found interesting initial recommendation. The third type of hybrid methods, known as H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 1061–1071, 2007. © Springer-Verlag Berlin Heidelberg 2007

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mixed, presents m the best items of each component method simultaneously. The remaining four types mentioned in [4] (feature combination, cascade approach, feature augmentation and meta-level integration) do not integrate component methods but rather create a single method using different source of data. In our opinion, only weighted integration can increase recommendation precision significantly, because neither switching nor mixed approach can avoid recommending irrelevant items if one of the component methods is not able to find any relevant item (e.g. due to wellknown sparsity problem in case of collaborative filtering or over-specialization in case of content based filtering [1]). The main contribution of this paper consists in defining an original formal model and a method for hybrid recommendation. The novelty of this method is based on using modified weighted integration not only to enhance recommendation precision, but also to improve diversity of recommended items (diversity of recommendation is measured by means of recall). Since information retrieval research has revealed that increase in recall usually causes decrease in precision and vice versa (this phenomena is known as the recall-precision curve [13]), we will try to demonstrate that our method permits the maximal precision and recall to be achieved. This paper presents in short the results worked out by the authors in [7]. In this non-published thesis the recommendation model has been defined in detail and the AdaptRank method has been analysed and verified by experiments.

2 The Recommendation Model The model, presented in this paper, permits recommendation in a hypertext system to be formalized. The hypertext system (e.g. a web site) is a system that manages and provides access to a set of hypertext documents D = {d1, d2,…, dn}. Definition 1. A hypertext document d ∈ D is a quadruple d = (ud, Fd, Ld, vd), where: ud is the unique document’s identifier; Fd is the set of statistical features of the document d; Ld is the set of hyperlinks located inside the document d that point to the documents from the set D and v d = (v1d , v 2d ,..., vndw ) is the descriptor vector. Each coordinate vdj ∈ [0,1] for j = 1, 2,…, nw is a number that determines importance of the jth descriptor in the document d. The detailed algorithm for calculation of the vd vector depends on a recommendation method. Statistical features of documents may vary for each hypertext system. The most popular magnitudes used in web environments are document visit frequency and opening rate (the ratio of the sessions’ number in which the document d was visited as the first to the total number of sessions in which the document d was visited) [8]. Once the user establishes connection with the hypertext system, he or she sends requests in order to obtain documents. Each request is related to exactly one document from the set D. A session is a sequence of requests sent during one connection. Each sent request together with the response will be denominated a step of the session (see Fig. 1). As it can be appreciated in Fig. 1, the system sends as the response to the single request not only the requested document, but also the set of hyperlinks to recommended documents that can (but does not have to) be requested in the next step.

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If the document recommended in the ith step is not requested in the next step, it will be considered as ignored in the ith step. The session ends when the user closes the connection with the hypertext system.

Fig. 1. A single step of the current session

There are two types of sessions in the recommendation model: current sessions and historical sessions. The current session is a session that has not been closed yet (the recommender system is performing recommendation for this session). Definition 2. Let sb = ( sb(1) , sb( 2) ,..., sb( n sb ) ) be the current session, where nsb is the session’s length and sb(i) = for i = 1,…, nsb, is the state of the session sb in the ith step, where: • R(i) is the set of documents recommended in the ith step (R(i) ⊆ D), • z(i) = (z1(i), z2(i),…,zn(i)) is the vector of documents visited till the ith step of the current session. Each coordinate z(i)j ∈ [0, 1] for j = 1,…,n determines how long ago the document d(j) has been visited in the session sb (0 - it has not been visited, 1- it is visited just now). The detailed algorithm of coordinate’s calculation varies for the particular method. • g(i) = (g1(i), g2(i),…,gn (i)) is the vector of ignored documents. The coordinate g(i)j ∈ [0, 1] for j = 1,…,n determines how long ago the document d(j) has been ignored in the session sb (0 - it has not been ignored, 1- it has been ignored in the previous step). The detailed algorithm of coordinate’s calculation varies for the particular method. nsb

Let R = ∪ R ( i ) be the set of all documents recommended during the session. Once the i =1

session is finished it is converted in a historical session. Definition 3. Let s = (s1,s2,…,sn) be a historical session vector, where sj = 1 when the document dj has been visited in the session; sj = 0 – otherwise Let S be the set of all historical sessions available in the system. Data related to a single historical session are very limited comparing with the current session, because even a medium hypertext system can possess millions of sessions. We have decided to restrict the historical data in order to avoid possible performance problems. The component of the hypertext system that is responsible for recommendation will be denominated recommender system.

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Definition 4. Let RS = be a recommender system where D is the set of available documents, S is the set of historical sessions and f is the recommendation function that in every step of the current session sb assigns for each document from the set D a real number that represents relevance of the document in the current step of the session sb. For example, f(d,sb(4)) = 0.9 means that in the 4th step of the current session sb relevance to the user’s needs is equal 0.9. The recommendation function is the most important element of every recommender system. It is similar to the retrieval status value that estimates degree of document relevance to the given query and it is widely used in information retrieval [3]. In case of the recommendation function the query is replaced with the current session. Although recommendation and search processes are similar (in both cases the relevant documents are presented) the recommendation process does not require the implicit user intervention and is more iterative. Obviously, the user’s needs can change during the same session (e.g. due to the recommendation content), therefore document relevance should be estimate in every session step separately. Let DR(i) be the subset of the set D that contains documents considered by the system as relevant for the user in the ith step of the current session and the rest of the documents (D\DR(i)) as irrelevant ones. Precision and recall – two measures known from Information Retrieval [13] can be successfully adapted to evaluate the quality of recommender systems. Due to possible changes in user’s needs, recommendation precision in the current session is slightly more complex than search precision in the given query. Definition 5. Recommendation precision of the system RS in the session sb is the ratio of the number of relevant documents recommended by the system RS during the session sb to the total number of documents recommended by the system RS during the session sb: ns b

prec( RS , sb ) =

∑ card ( R i =1

nsb

(i)

∩ DR( i ) )

∑ card ( R

(i )

.

(1)

)

i =1

The difference between recommendation recall definition and its counterpart from searching is more significant, because recommender systems tend to recommend only a few documents in each step, therefore the standard recall definition (the ratio of the number of relevant documents retrieved by the system to all relevant documents available) would promote the long sessions (sessions with many steps). Definition 6. Recommendation recall of the system RS in the session sb is the ratio of the number of different relevant documents recommended by the system RS during the session sb to the total number of different relevant documents that would have been recommended by the system RS during the session sb assuming that up to m documents could be recommended in a single step: ⎛ n sb card ⎜⎜ ∪ DR(i ) ∩ R (i ) ⎜ i =1 ⎝ rec( RS , sb ) = nr

(

⎞

)⎟⎟⎟ ⎠

(2)

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where nr can be obtained from the following formula:

(

( ))

n sb ⎛ i −1 ⎛ ⎞⎞ nr = min m, card DR(1) + ∑ min⎜⎜ m, card ⎜ DR(i ) \ ∪ R ( j ) ⎟ ⎟⎟ ⎜ ⎟⎟ ⎜ i=2 j =1 ⎝ ⎠⎠ ⎝

(3)

As it can be deduced from Definition 6, nr expresses the number of relevant documents that would have been recommended during the session sb. The left part of the formula (3) – min(m, card(D(1)R)) corresponds to the first step. Obviously, there is no document recommended in the session sb before the first step therefore the number of relevant documents that can be recommended to the user in the first step according to the conditions from Definition 6 is equal to m, except for the situation in which m > card(D(1)R) (the set D would not contain m relevant documents). The number of relevant documents that can be recommended in the next steps is calculated iteratively in a similar way. Obviously, only the number of not recommended relevant documents is compared with m. Recall and precision can be expressed as effectiveness. This measure is also widely used in Information Retrieval and its calculation formula differs according to the authors [5], [10], [13]. We have chosen the definition proposed in [15]: E ( RS , sb ) =

prec( RS , sb ⋅ rec( RS , sb ) .

(4)

This definition is simple, intuitive and it promotes recommender systems that are able to keep balance between high recall and high precision. It is important to underline that it is relatively easy to obtain high recall by recommending different documents neglecting their relevance and as a consequence recommendation precision. Similarly, if a recommender system proposed the same very relevant documents in each step of the current session, the recommendation precision would be very high but the recall would decrease drastically for medium and long sessions. The effectiveness from the formula (4) will be relatively low in both cases.

3 AdaptRank – A Hybrid Recommendation Method The model defined in the previous section will be used to present a hybrid recommendation method denominated AdaptRank that integrates two method of recommendation. The first is based on collaborative filtering (recommended documents are selected comparing the current session sb with the set of historical sessions - S) and the second on content based filtering (the documents similar to the visited documents are obtained using the vd vector). The detailed definitions of the both methods are beyond the scope of this paper (their examples may be find in [7] or [11]). Let fu be a recommendation function that estimates document relevance using a collaborative filtering method (usage component) and let fc be a recommendation function that estimates document relevance using content based filtering method (content component). Additionally, let q be a quality function that for each document from the set D assigns the real number from [0, 1] that describes quality of every document. The value of the q function may vary for each implementation. It can depend on the statistical features of documents (the set Fd from Definition 1) [8]. Similarly, the

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value of the q function may be determined by hyperlink structure (see the set Ld from Definition 1). Obviously, the value of the q should not depend on user interactions because it does not measure the relevance of documents to particular user’s needs. It rather expresses contextless value of documents. Its main goal is to promote the documents that are objectively valuable and it should have more impact at the beginning of the current session when the user’s needs have not been transmitted to the system yet. Definition 2 has left to a particular method the way in which the coordinates of the z and g vectors are obtained. The (5) formula presents the value of the z vector coordination in the first step of the AdaptRank method:

⎧0 for d (1) ≠ d j ⎪ =⎨ (5) (1) ⎪⎩1 for d = d j The values of the z vector in the next steps can be calculated recursively from the following formula: ⎧ρ ⋅ z (i ) for d (1) ≠ d j j (i +1) ⎪ s zj . =⎨ (6) (1) for d = d j ⎪⎩1 z (j1)

where d(i) is the document visited in the ith step of the current session and ρs∈(0, 1) is a parameter that determines how fast the fact of the document’s visit will be “forget” by recommender system (the visited documents are almost treated as no visited ones when their zj(i) values are close to 0). As it can be noticed the value of the coordinate that corresponds to the document visited in a given step decreases geometrically in next steps. It was shown in [7] that the increase of the ρs value may improve recommendation recall and decrease recommendation precision. The g vector is obtained in the following way: g (j1) = 0 ; ⎧ γ−α ⎪ (i +1) ⎪ =⎨ 1 gj ⎪ (i ) ⎪⎩γ ⋅ g j

for

d (i ) ≠ d j ∧ g (ji ) = 1

for d j ∈ R (i ) ∧ d (i +1) ≠ d j . otherwise

(7)

where d(i) is the document visited in the ith step of the current session, α ∈(0, 1) and γ∈(α, 1). The α parameter will be used in the f function definition (see below) to regulate the influence of the values of the function f in previous steps on the current one. Although the formulas 5, 6 and 7 are similar (the γ parameter is the counterpart of the ρs) there are three differences that should be explained. The explanation of the first difference is obvious the g(1) vector has 0 values for all its coordinates because before the first step any document cannot be recommended and as a consequence ignored. The second difference is the value of the coordinate that corresponds to the document that was ignored two steps ago: γ -α. If this value was equal to γ, there

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would be cases in which the fact of ignoring the document dj in the ith step would have more impact in the step i+2 that in the step i+1 (see the theorem 5.1.6 from [7]). The third difference - γ∈(α, 1) - is a consequence of the second one (γ>α would drive to γ -αi ⎜ d ∈D \{d } ⎝

j

∧ f c (d max , sb(i ') ) − f c (d j , sb(i ') ) ≥ c

max

)⎞ ⎠

(13)

This theorem illustrates the adaptive character of the method. It shows that the dmax document (the document that possesses the highest value of two component functions since the ith step) will be recommended, even if the component functions’ values for this document have been the smallest before the ith step. In other words, if the component functions are able to identify properly the relevant documents, the document that has been irrelevant to initial user’s needs, but it converts in relevant when the user has changed the interests will be recommended in the limited number of steps. For example, if α=0.5, c=0.4 and i=10, then st≈1.81. The next theorem needs four auxiliary variables to be introduced. Let rcmin, rumin, be the minimum values of the component functions fc and fu obtained by the relevant documents in the current session: rcmin = min { f c (d , sb ) : 0 < i ≤ nsb ∧ d ∈ DR( i ) } . (14)

rumin = min { f u (d , sb ) : 0 < i ≤ nsb ∧ d ∈ DR( i ) } .

Similarly, let ncmax, numax, be the maximal values of the component functions fc and fu obtained by the irrelevant documents in the current session:

{ = max {f (d , s ) : 0 < i ≤ n

} }.

ncmax = max f c (d , sb ) : 0 < i ≤ nsb ∧ d ∈ D \ DR(i ) . numax

u

b

sb

∧ d ∈D\

DR(i )

(15)

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Theorem 2. Effectiveness of the recommender system RS will be maximal in the current session sb – E(RS,sb) = 1 independently from the session’s length, if the user visits one of the m documents recommended in the previous step in each ith step (i = 2,..,nsb) of the current session sb (i. e. d(i) ∈ R(i-1)) and the recommender system RS possesses mr (mr>m) relevant documents that do not change during the session sb (n ) DR(1) = DR( 2 ) = ... = DR and the following conditions hold in the first ⎣mr / m ⎦ steps: sb

nc max 1 − α [ mr / m ] rc min > (1 − ρ s ) ⋅ 1−α , 1−α 2 . n 1 − α [ mr / m ] > 0.5 ⋅α + u max , ru min > (1 − ρ s ) ⋅ 1−α 1−α 2

rc min > 0.5 ⋅ α + ru min

(16)

and in the next steps the following inequalities are fulfilled: rcmin >

0.5 ⋅ α 1 − ρ[sm r / m]

+

ncmax (1 − ρ[smr / m] ) ⋅ (1 − α)

,

rumin >

0.5 ⋅ α 1 − ρ[sm r

/ m]

+

numax (1 − ρ[sm r

/ m]

) ⋅ (1 − α)

(17)

Theorem 2 shows that AdaptRank guarantees the maximal effectiveness, if there is a significant difference between the values of the component functions for relevant and irrelevant documents (i. e. component methods behave properly). For example, the AdaptRank method would obtain the maximal effectiveness with the following parameter: α=0.25, ρs=0.8, mr=20 (there was 20 relevant documents), m=2 (two documents were recommended in each step), ncmax=numax=0.2 (values of the component functions for irrelevant documents did not exceed 0.2), rcmin=rumin=0.4 (values of the component functions for relevant documents did exceed 0.4). The inequalities presented in the theorem 2 guarantee only the maximal precision of the component methods, but not recall. The most important contribution of the AdaptRank method illustrated in the theorem 2 is the achievement of maximal recall maintaining maximal precision.

5 Experiments Effectiveness of the presented method has been also measured and verified by means of AdaptRank Simulator program. Its main purpose is to simulate the AdaptRank method and its component behaviour in hypothetical random current sessions. The all method parameters presented in Table 1 can be configured. Additional variables that can be established on configuration level are presented in Table 2. All performed experiments consist of generating 100 sessions for each iteration defined in Table 3. The principal goal of the experiments is to analyze the method’s effectiveness when the component function value for relevant documents is only slightly greater than for irrelevant ones: rmin=0.49 and rmax=0.5 (please note that Theorem 2 requires that the difference between rmin and rmax is significant). AdaptRank has obtained the highest effectiveness in all cases. The condition rmin>nmax guarantees maximum precision of the component methods, so the better effectiveness of AdaptRank has been achieved by recall increase.

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Parameter

Description

n nr sessionLen rmin nmax

Cardinality of the set D Number of relevant documents The length of the session The value of rcmin and rumin presented in the formula 14 The value of ncmax and numax presented in the formula (15)

Established Value 100 20 Random [5,30] 0.49 0.5

Table 3. Designed experiments and their results

Changing parameter n nr rmin nmax

Minimal Value 20 20 0.5 0

Maximal Value 100 100 1 0.49

Increase per step 1 1 0.01 0.01

Mean AdaptRank Effect. 0.93 0.97 0.97 0.97

Mean Usage Component Effect. 0.85 0.94 0.95 0.95

Mean Content component Effect. 0.82 0.89 0.9 0.89

6 Conclusions A hybrid recommendation method has been presented in this paper. The method has introduced integration mechanisms that permit the method to adapt to user’s needs very quickly and to increase recommendation recall. Although recall improvement can deteriorate recommendation precision, formal analysis and the experiments have demonstrated that AdaptRank can achieve the maximal effectiveness, if the component methods work correctly. Moreover, the parameters of the method permit the system administrators to adjust recommendation to local needs (it is possible to increase recommendation recall at the expense of precision and vice versa).

References 1. Adomavicius, G., Tuzhilin, A.: Recommendation technologies: Survey of current methods and possible extensions. MISRC working paper 0329. University of Minnesota (2003) 2. Balabanovic, M., Shoham, Y.: Fab: content-based, collaborative recommendation. In: Communications of the ACM, vol. 40(3), pp. 66–72. ACM Press, New York (1997) 3. Bookstein, A.: Relevance. Journal of the American Society for Information Science 30(5), 269–273 (1979) 4. Burke, R.: Hybrid Recommender Systems: Survey and Experiments. In: User Modeling and User-Adapted Interaction, vol. 12(4), pp. 331–370. Springer, Netherlands (2002) 5. Heine, M.H.: Distance between sets as an objective measure of retrieval effectiveness. Information Storage and Retrieval 9, 181–198 (1973) 6. Kazienko, P., Kiewra, M.: ROSA - Multi-agent System for Web Services Personalization. In: Menasalvas, E., Segovia, J., Szczepaniak, P.S. (eds.) AWIC 2003. LNCS (LNAI), vol. 2663, pp. 297–306. Springer, Heidelberg (2003)

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7. Kiewra, M.: A Hybrid Method of Document Recommendation in Hypertext Environment. Ph.D. Thesis. Wroclaw University of Technology (Advisor: Nguyen, N.T. in Polish) (2006) 8. Kiewra, M., Nguyen, N.T.: Non-textual Document Ranking Using Crawler Information and Web Usage Mining. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3682, pp. 520–526. Springer, Heidelberg (2005) 9. Lieberman, H.: Letizia: An Agent that Assists Web Browsing. In: Proc. of IJCAI’95, pp. 924–929. Morgan Kaufmann publishers Inc., San Mateo (1995) 10. Meadow, C.T.: Text Information retrieval Systems. Academic Press, Orlando (1992) 11. Mobasher, B., Dai, H., Luo, T., Sun, Y., Zhu, J.: Integrating Web Usage and Content Mining for More Effective Personalization. In: Proc. of 1st International Conference on Electronic Commerce and Web Technologies, pp. 165–176. Springer, London, UK (2000) 12. Montaner, M.: Collaborative Recommender Agents Based on Case-Based Reasoning and Trust. Ph.D. Thesis. Universitat de Girona (2003) 13. van Rijsbergen, C.J.: Information Retrieval. Dept. of Computer Science, University of Glasgow (1979) 14. Schafer, J.B., Konstan, J., Riedl, J.: Recommender Systems in E-Commerce. In: Proc. of ACM Conference on Electronic Commerce, pp. 156–158. ACM Press, New York (1999) 15. Voiskunskii, V.G.: Evaluation of search results: a new approach. Journal of the American Society for Information Science 48(2), 133–142 (1997)

Combinatorial Auction with Minimal Resource Requirements Fu-Shiung Hsieh Department of Computer Science and Information Engineering Chaoyang University of Technology 41349 Taichung County, Taiwan, R.O.C. [email protected]

Abstract. Although combinatorial auction has been studied extensively, it is difficult to apply the existing results to a problem with minimal resource requirements. In this paper, we consider a combinatorial auction problem in which an auctioneer wants to acquire resources from a set of bidders to process the tasks on hand. Each task requires a minimal set of resources for executing the operations. Each bidder owns a set of resources to bid for the tasks. The problem is to determine the resource assignment to minimize the total cost to perform the tasks. The main results include: (1) a problem formulation for combinatorial auction with minimal resource requirements; (2) a solution methodology based on Lagrangian relaxation; (3) an economic interpretation and a proposed structure for implementing our solution algorithms.

1 Introduction Auctions are popular, distributed and autonomy preserving ways of allocating items or tasks among multiple agents to maximize revenue or minimize cost. An excellent survey on combinatorial auctions can be found in [3]. In a combinatorial auction [1], bidders may place bids on combinations of items or tasks. This allows the bidders to express complementarities between items instead of having to speculate into an item's valuation about the impact of possibly getting other, complementary items or tasks. The combinatorial auction problem can be modeled as a set packing problem (SPP), a well-known NP-complete problem [4]-[8]. Many algorithms have been developed for combinatorial auction problems. For example, in [2], the authors proposed a Lagrangian Heuristic for a combinatorial auction problem. Exact algorithms have been developed for the SPP problem, including a branch and bound search [8], iterative deepening A* search [7] and the direct application of available CPLEX IP solver [4]. However, in real world, a task may require a minimum number of resources for processing. In this case, it is difficult to apply the existing combinatorial auction algorithms to allocate the resources. Motivated by the deficiency of the existing methods, we consider a combinatorial auction problem in which an auctioneer wants to acquire resources from a set of bidders to process the tasks on hand. Each task requires a minimal set of resources for executing the operations. Each bidder owns a set of identical resources to bid for the tasks. The problem is to determine the winners to minimize the total cost to perform H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 1072–1077, 2007. © Springer-Verlag Berlin Heidelberg 2007

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the tasks. The remainder of this paper is organized as follows. In Section 2, we present the problem formulation. In Section 3, we propose the solution algorithms. We give an economic interpretation for our solution approach in Section 4. Finally, we address our future research direction for this paper.

2 Combinatorial Auction with Minimal Resource Requirements Suppose an auctioneer has a set J of tasks to be performed. Each task consists of a set of operations which rely on a minimum set of resources for processing. There are a set of potential bidders in the system. Each bidder holds a set of resources that can be used to process the operations of the tasks. Depending on the operations involved, the costs of using different resources for conducting the same operation vary. The problem is to distribute the tasks among a set of bidders such that the total cost is minimized. In this paper, we first formulate the above combinatorial optimization problem as an integer programming problem. We then develop solution algorithms based on Lagrangian relaxation. Let B denote the set of bidder agents in the system. A bidder b ∈ B holds a set Rb of resources. The set of all resources is denoted by R = ∪ Rb . Let A j denote the set of b∈B

operations in task j ∈ J . The set of all operations is denoted by A = ∪ A j , where j∈J

Ai ≠ Ak ∀i ≠ k . Let x ra be the decision variables for allocating resource r ∈ R to process operation a ∈ A . The decisions variables x ra , r ∈ R , a ∈ A must satisfy the

⎧1 if resource r is allocated to operation a following constraints x ra = ⎨ ⎩0 otherwise Σ xra ≤ 1 , x ra ∈ {0,1}.

a∈A

Let wra denote the cost of applying resource r to process task a . For a given resource allocation x ra , r ∈ R , a ∈ A , the total cost is Σ Σ wra xra . To completely process a r∈R a∈A

task, a minimum set of resources is required. Let ω a denote the minimum number of resources required for operation a ∈ A . The following constraints must hold Σ x ra ≥ ω a ∀a ∈ A .

r∈R

The combinatorial auction with minimal resource requirements (CAMRR) optimization problem can be formulated as follows min f ( x) = Σ Σ wra x ra r∈R a∈A

xra

s.t. Σ x ra ≥ ω a ∀a ∈ A r∈R

Σ xra ≤ 1 ∀r ∈ R ,

a∈A

x ra ∈ {0,1} ∀r ∈ R a ∈ A.

(2-1)

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In the CAMRR problem, we observe that the coupling among different operations is caused by the contention for resources through the minimal resource requirement constraints (2-1). Applying Lagrangian relaxation to these coupling constraints, we form a Lagrangian function L(π ) = min{[ Σ Σ wra x ra ] − [ Σ π a ( Σ x ra − ϖ a )]} x

r∈R a∈ A

a∈A

r∈R

= min{[ Σ Σ x ra [ wra − π a ]] + ( Σ π aϖ a )} x

r∈R a∈A

a∈ A

= [ Σ min Σ x ra [ wra − π a ]] + ( Σ π aϖ a ) r∈R xra ,a∈A a∈A

a∈A

= Σ L r (π ) + ( Σ π aϖ a ), where r∈ R

a∈ A

L r (π ) =

min Σ x ra [ wra − π a ]

xra ,a∈A a∈A

s.t. Σ x ra ≤ 1, x ra ∈ {0,1} a∈A

For a given Lagrange multiplier π , the relaxation of constraints (2-1) decomposes the original problem into a number of resource assignment (RA) subproblems. These subproblems can be solved independently. That is, the Lagrangian relaxation results in subproblems with a highly decentralized decision making structure. Interactions among subproblems are reflected through Lanrange multipliers, which are determined by solving the following dual problem. max L(π ) π ≥0

3 Solution Algorithms Our methodology for finding a near optimal solution of the CAMRR problem is developed based on the result of Lagrangian relaxation and decomposition. It consists of three parts: (1) an algorithm for solving subproblems by exploiting their individual structures; (2) a subgradient method for solving the non-differentiable dual problem; (3)a heuristic algorithm for finding a near-optimal, feasible solution based on the solution of the relaxed problem. The structure of our algorithms is depicted in Fig. 1. (1) An algorithm for solving subproblems Given Lagrange multiplier π , the RA subproblem to be solved for resource r is as follows. L r (π ) = min Σ x ra [ wra − π a ]

xra ,a∈A a∈A

s.t. Σ x ra ≤ 1, x ra ∈ {0,1} a∈A

As Σ x ra ≤ 1 and x ra ∈ {0,1} , the above RA subproblem can be solved by finding a∈A

the a with the minimum wra − π a and setting x ra =1and setting x ra′ = 0 for a ′ ≠ a .

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(2) A subgradient method for solving the dual problem Let x l be the optimal solution to the subproblems for given lagrange multipliers π l of iteration l . We define the subgradient of L(π ) as

g al =

∂L(π ) l = −( Σ xra − ϖ a ) , where a ∈ A . r∈R ∂π a π al

The subgradient method proposed by Polak [9] is adopted to update π as follows ⎧⎪π l + α l g al if π al + α l g al ≥ 0; π al +1 = ⎨ a ⎪⎩0 otherwise.

where α l = c

L − L(π ) Σ( g al ) 2

, 0 ≤ c ≤ 2 and L is an estimate of the optimal dual cost. The

a

iteration step terminates if α l is smaller than a threshold. Polyak proved that this method has a linear convergence rate. Iterative application of the algorithms in (1) and (2) may converge to an optimal dual solution ( x * , π * ).

Dual Problem

π

π x1a , a ∈ A

Resource Assignment Subproblem Resource 1

π x 2a , a ∈ A

Resource Assignment subproblem Resource 2

x Ra , a ∈ A Resource Assignment subproblem Resource N

Fig. 1. Structure of solution algorithms

(3) A heuristic algorithm for finding a near-optimal, feasible solution based on the solution of the relaxed problem The solution ( x * , π * ) may result in one type of constraint violation due to relaxation: assignment of resources less than the minimal resource requirements. Our heuristic scheme first checks all the operations that have been allocated with excessive resources. We then move these excessive resources to the operations without sufficient resources to fix the violation. If the violation cannot be completely resolved, all the

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remaining operations without sufficient resources will be allocated the remaining available resources according to their cost. max L (π ) π ≥0

4 Economic Interpretation Decomposition of the original problem into RA subproblems provides us a different viewpoint of the original problem. Decision making of the original problem is composed of those of RA subproblems. RA subproblems can be regarded as the decision making problem to acquire the required resources and benefits from utilizing the acquired resources. The decision processes of each entity are just like the ones made by sellers in a real business environment. That is, a seller is willing to sell a good only when its utility is at least equal to its market price. The buyer will raise the market price in case of resource shortage. Such processes occur in the decision making of the RA subproblems. Lagrange multipliers can often be given the economic interpretation as marginal costs for using the resources when they are used to relax resource constraints. In our relaxation procedure above, the Lagrange multipliers π a is used to relax the minimal resource constraints of an activity a . Lagrange multipliers π a can be interpreted as the marginal benefit of using an additional unit of resources in activity a . If the constraint Σ x ra ≥ ϖ a is violated, that is, Σ x ra < ϖ a . In this case, addr∈R

r∈R

ing an additional unit of resources in activity a reduces the total cost by π a . On the other hand, if the constraint Σ x ra ≥ ϖ a holds. In this case, adding an additional unit r∈R

of resources in activity a increases the total cost by π a . The effectiveness of the solution algorithms can be evaluated based on the duality gap, which is the difference between primal and dual objective values. That is, duality gap is defined by f ( x * ) − L(π * ) .

Example: Consider a problem with J = {1,2} , A = {1,2} , A1 = {1} , A2 = {2} , ϖ1 = 2 , ϖ2 =2 , R = R1 ∪ R2 = {1,2,3,4,5,6} , where R1 = {1,2,3} and R2 = {4,5,6} . Let w11 = 1, w12 = 2, w21 = 2, w22 = 4, w31 = 3, w32 = 6, w41 = 2, w42 = 1, w51 = 4, w52 = 2, w61 = 6, w62 = 3 . For this exam* * * * ple, our algorithms yield an optimal solution in which x11 = 1, x 21 = 1, x42 = 1, x52 = 1. The duality gap for this example is less than 3%.

5 Future Research Direction We formulate an optimization problem for combinatorial auction with minimal resource requirements on the tasks. The minimal resource requirements for tasks impose additional constraints on determination of the winners. By applying Lagrangian relaxation technique, the original optimization can be decomposed into a number of

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resource assignment subproblems. Our methodology consists of three parts: (1) an algorithm for solving subproblems by exploiting their individual structures; (2) a subgradient method for solving the non-differentiable dual problem; (3) a heuristic algorithm for finding a near-optimal, feasible solution. Numerical results indicate that our proposed algorithms yield near optimal solutions for small problems. Our future research direction is to study the optimality of the near optimal solutions obtained from our algorithms for large problems.

Acknowledgement This paper was supported in part by the National Science Council of Taiwan, R.O.C. under Grant NSC95-2416-H-324-009-MY2.

References [1] de Vries, S., Vohra, R.V.: Combinatorial Auctions:A Survey. INFORMS Journal on Computing 3, 284–309 (2003) [2] Guo, Y., Lim, A., Rodrigues, B., Tang, J.: Using a Lagrangian heuristic for a combinatorial auction proble. In: Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence (2005) [3] de Vries, S., Vohra, R.V.: Combinatorial Auctions: A Survey. INFORMS Journal on Computing 15(3), 284–309 (2003) [4] Andersson, A., Tenhunen, M., Ygge, F.: Integer programming for combinatorial auction winner determination. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence, pp. 39–46 (2000) [5] Fujishima, Y., Leyton-Brown, K., Shoham, Y.: Taming the computational complexity of combinatorial auctions:Optimal and approximate approaches. In: Sixteenth International Joint Conference on Artificial Intelligence, pp. 548–553 (1999) [6] Hoos, H.H., Boutilier, C.: Solving combinatorial auctions using stochastic local search. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence, pp. 22–29 (2000) [7] Sandholm, T.: Algorithm for optimal winner determination in combinatorial auctions. Artificial Intelligence 135(1-2), 1–54 (2002) [8] Sandholm, T., Suri, S., Gilpin, A., Levine, D.: CABOB: A fast optimal algorithm for combinatorial auctions. In: IJCAI, pp. 1102–1108 (2001) [9] Polyak, B.T.: Minimization of Unsmooth Functionals. USSR Computational Math. and Math. Physics 9, 14–29 (1969)

Effectiveness of Autonomous Network Monitoring Based on Intelligent-Agent-Mediated Status Information Susumu Konno, Sameer Abar, Yukio Iwaya, and Tetsuo Kinoshita Tohoku University, 2–1–1 Katahira, Aoba-ku, Sendai, Miyagi, 980–8577, Japan [email protected] http://www.ka.riec.tohoku.ac.jp/ka/menber.index.en.html

Abstract. The growing complexity of communication networks and their associated information overhead have made network management considerably difficult. This paper presents a novel Network Management Scheme based on the novel concept of Active Information Resources (AIRs). Many types of information are distributed in the complex network, and they are changed dynamically. Under the AIR scheme, each piece of information in a network is activated as an intelligent agent: an I-AIR. An I-AIR has knowledge and functionality related to its information. The I-AIRs autonomously detect run-time operational obstacles occurring in the network system and specify the failures’ causes to the network administrator with their cooperation. Thereby, some network management tasks are supported. The proposed prototype system (AIRNMS) was implemented. Experimental results indicate that it markedly reduces the network administrator workload, compared to conventional network management methods.

1

Introduction

In recent years, computer communication networks have grown dramatically both in size and complexity. Moreover, they comprise heterogeneous multivendor environments. Traditionally, network management activities have been performed by network managers. However, these activities are becoming more demanding and data-intensive because of the rapid growth of modern networks. For those reasons, automation of network management activities has become necessary. For managing these huge distributed network systems, manual procedures have become tedious. A typical approach to network management is centralized, static, pollingbased management that involves high-capacity computing resources at the centralized platform including commercially available management tools. As managed components become more numerous, the amount of network traffic, which should be managed, have increased accordingly. Consequently, in centralized management systems, the management traffic might eventually oppress the network bandwidth. Even where the management platform uses several distributed H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 1078–1087, 2007. c Springer-Verlag Berlin Heidelberg 2007 

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management stations, the huge bulk of management traffic remains concentrated around those stations [1]. The overwhelming volume and complexity of the information involved in network management imparts a terrible load [2]. Furthermore, in view of the dynamic nature of evolving networks, future network management solutions need to be flexible, adaptable, and intelligent without increasing the burden on network resources. The rapid of network systems has posed the issues of flexibility, scalability, and interoperability for the centralized paradigm. Even though failures in large communication networks are unavoidable, quick detection and identification of the causes of failure can fortify these systems, making them more robust, with more reliable operations, thereby ultimately increasing the level of confidence in the services they provide [3]. Motivated by these considerations, the proposed approach is intended to provide an intelligent, adaptive and autonomous network monitoring support paradigm for communication network systems. A network monitoring support method based on the activated information is proposed in this paper. In this method, the distributed information in a computer network is activated using the concept of Active Information Resource (AIR). In the AIR scheme, each unit of distributed information has knowledge and functionalities related to utilization of the information resource as well as its information. In our experiment network system, each activated information AIR (I–AIR) is developed as an intelligent agent. The proposed framework simplifies network monitoring for the administrator. Experiments were performed to investigate the effectiveness of the proposed method. The remainder of the paper is organized as follows. Section 2 presents an overview of the AIR concept and conversion of the dynamic status information as I-AIRs. The detailed design and implementation considerations of I-AIRs in the proposed prototype system are discussed in Section 3. Experimental results, along with the system’s performance evaluations are outlined in Section 4. Finally, the conclusions and future issues are presented in Section 5.

2

Automated Network Monitoring Based on Activated Information

For monitoring of communication network by an administrator, much status information distributed in a network is required, such as network traffic, conditions of service processes, and application server logs. Commonly, the information is static; furthermore, an administrator must investigate them one by one, which places a necessary physical and mental load on the administrator. In this study, therefore, this static information is activated to reduce the administrator’s workload. For activation of status information, a concept of an active information resource (AIR) [4] [5] [6] is employed. Each unit of status information is wrapped as an AIR for activation; it is called I-AIR. An I-AIR has its original information resources along with related knowledge and functionalities. Several I-AIRs can cooperate autonomously based on their status information and knowledge. Consequently, our scheme can reduce network management loads

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User Knowledge of Utilization Supporting Functionality of Utilization Supporting Information Resource

Request AIR Interface

Cooperation AIR

Knowledge of Utilization Supporting Functionality of Utilization Supporting Information Resource

Knowledge of Utilization Supporting Functionality of Utilization Supporting Information Resource

AIR Workplace

Fig. 1. Active Information Resource

by presenting the dynamic status information of the network resources during automatic detection and specification of network failures. 2.1

AIR Concept

An AIR is defined as the distributed information resource enhanced with its knowledge as well as functionality to facilitate its resources. Figure 1 shows a conceptual model of an AIR with its support knowledge and functionality. The knowledge of an AIR typically consists of metadata of the information contents and their processing descriptions. The functionality of AIR is about how to analyze and process the users’ query as well as defining the cooperation strategy among the multiple AIRs. An AIR can be implemented using the multi-agent-based approach. Agentbased computing is known as a complementary way to manage the resources of distributed systems because of the increased flexibility in adapting to the dynamically changing requirements of such systems [7]. Essential features of AIRs include: – To extract and process the information contents in response to the query from user (or another AIR) in a knowledge-based manner. – To interact actively and mutually to make full use of the information contents, the embedded support knowledge, and functionality. The effectiveness of AIR has been employed in the context of diverse webbased information retrieval techniques. The prototype systems have exhibited very promising results. 2.2

Applying the AIR Concept to Network Monitoring

Generally, the status information of the communication network is classifiable into two types: static information and dynamic information. For example, the relationship between IP addresses and Mac addresses, host names, domain names, IP-routing, etc., are included as static network information. On the other hand,

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the dynamic information includes number of packet traffic, RMON-MIB, SNMPv2-MIB, logs of network services, etc. To apply the concept of AIR to both types of information for network monitoring, each unit of information is converted to an AIR to form a so-called I-AIR. Conventionally, an administrator collects various status information through periodical polling. She aggregates the data and decides the status of the network system using her know-how. This task can be disaggregated into three stages, such as detection, recognition, and specification of the failure. This task requires much experience as a network manager; therefore, a beginner cannot be employed as an administrator. To support the empirical task of the administrator, an I-AIR includes diverse knowledge and functionality in addition to its original data. For example: – – – –

meta-knowledge about information resources knowledge about failure condition (threshold) knowledge about cooperation with another I-AIR functionality to handle original data

Using this additional knowledge and functionality, I-AIRs can mutually cooperate. The following tasks can be partially supported by AIR: – distributed and effective monitoring of network system – detection of network failure using a threshold – processing of information resources according to the failure with its functionality – improvement of reliability of detection, recognition, and specification of the failure through cooperation among AIRs These features can reduce the overall workload of the administrator.

3

Design and Implementation of I-AIR

In this section, the design of an I-AIR is discussed. The design comprises three vital ingredients: internal support knowledge, functionality for sharing the information contents, and specifications of the information resource itself. 3.1

Design of Knowledge in I-AIR

The support knowledge for sharing information contents is the empirical knowledge of network management which inspects the status information of the network for occurring faults. Essential components of this knowledge are as follows: – I-AIR Identification Knowledge (ID): The ID includes an identification number, task number of I-AIR, etc. – Knowledge about Information Resource (IR): The IR includes a type, an update-time, a format type, etc.

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– Knowledge about Failure Inspection (FI) The FI includes two types of knowledge to inspect the failure: text information to be detected in logs, and a threshold of packets, etc. – Knowledge about Network Periodic Investigation - Control Method (CM): The CM includes the polling time and other conditions for updating of the information resource. – Knowledge about Cooperation Protocol (CP): The CP includes protocol sequences for cooperation with other AIRs. The knowledge contained in an I-AIR as ID, IR, and CP is required mainly in order to operate on the information resource and facilitate communication and cooperation among the I-AIRs. The preeminent characteristic of I-AIR is its autonomous monitoring mechanism, which is supported via FI and CM for the inspection and investigation of the obstacles that hinder the normal network operation. Thus, the performance of I-AIRs in the proposed technique relies heavily on the design of various types of internal support knowledge. 3.2

Design of Functionality of I–AIR

I-AIRs’ functionality deals with the sharing and processing of the information resource for cooperative problem solving during the active fault monitoring and detection phases. In this regard, the design of some essential features is crucial as follows: – – – –

3.3

Functionality Functionality Functionality Functionality threshold

as an Interface to I-AIR internal support knowledge for processing the information resource for transmitting the processed results to other I-AIRs for inspecting the obstacle with respect to the pre-defined

Design of Information Resource

Two I-AIR information resource types are described here. – Simple text format – RDF/XML syntax specification The RDF/XML language is a W3C-recommended framework for describing information resources using machine-readable metadata, which brings about an unprecedented level of automation for the representation and retrieval of information. The plain-text format consists of log-information that is acquired through the syslog (a standard logging solution on Unix and Linux systems). In the proposed approach, the I-AIR functionality extracts a diverse type of log-information during operational management scenarios and converts it to RDF/XML format specifications.

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Table 1. Examples of implemented I-AIRs for network monitoring I-AIR No. 1 2 3 4 5 6 7 8 9 10

3.4

Function I-AIR No. Network Disconnection detector 11 NIC config failure detector 12 SPAM mail detector 13 MSBlaster attack detector 14 Mail send/receive error detector 15 TCP/IP stack failure checker 16 NIC config failure checker 17 HUB failure checker 18 Ruter failure checker 19 Communication failure checker

Function DNS server process checker SMTP server process checker POP server process checker DNS connection checker Network route to host checker Kernel information checker Lease IP address checker Mail server error checker Number of SPAM mail

Implementation of I-AIR

A multi-agent-based approach was adopted for implementation of I-AIRs in the proposed technique. For the effective realization of I-AIR support knowledge and functionality, the multi-agent system is a highly pragmatic choice. The I-AIRs realized with the software agents render the I-AIRs active, which, after being invoked by an outside event, can autonomously perform the task of cooperative problem-solving. The proposed system architecture is supported by an Agentbased Distributed Information Processing System (ADIPS) framework [8], which is a flexible computing environment for designing multi-agent systems. Table 1 illustrates the I-AIRs developed in this study.

4

Evaluate the Effectiveness of I-AIR in Actual Monitoring Task

To evaluate the prototype system’s effectiveness, an experimental NMS system, called AIR-NMS, was set up in the laboratory. The network administrator per-

Host Name AIR workplace

Internet Internet

130.xxx.yyy.zzz Firewall DMZ

air-nms

172.17.0.0/16

172.17.0.0/24 (example.com)

Intra-network

HUB

rtB

rtC

rtD

DNS Server Mail Server

srvA1 Subnetwork-A (subnetA.examle.com) 172.16.0.0/24

pcB1 Administrator

Subnetwork-B (subnetB.example.com) 172.17.1.0/24

pcC1

pine

Subnetwork-C (subnetC.example.com) 172.17.2.0/24

pcD1 Subnetwork-D (subnetD.example.com) 172.17.3.0/24

Fig. 2. Construction of Network Systems and AIR-NMS

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forms the management task according to the conventional manual method, as well as with the I-AIRs based proposed system. He also measures the performance of the proposed approach adopted for the automation of network functions. In the experiment, the time and the number of procedures executed to correct the obstacle were measured after a network obstacle was reported to a subject. 4.1

Experimental Network

Figure 2 demonstrates the practical setup of the environment for experimenting with the I-AIRs. The network system comprises a 100BASE-TX Ethernet with a firewall configured as a Network Address Translation (NAT) firewall, a router, and various personal computers (PCs) arranged in four subnetworks. Subnetwork A is configured as a DeMilitarized Zone range 172.16.0.0/24. The server (sevA1) DNS and Mail application settings are configured. The other three subnetworks (B, C, D) have IP-addresses in the order given as 172.17.1.0/24, 172.17.2.0/24, and 172.17.3.0/24. Moreover, the network management console for managing the whole setup resides in pcB1 of subnetwork B. In subnetwork C, there is a desktop-type PC system (pcC1) with a fixed IP address from the DNS server, and a notebook computer (pine) which acquires the IP-addresses through the DHCP. In addition, Fig. 2 depicts the nodes (PCs, routers, firewall etc.) of the experimental network system. Each node shows the corresponding AIR workplace where the I-AIRs operate actively. For each node, about 15 AIRs were implemented. This implies that nearly 140 I-AIRs were incorporated within the experimental setup. A Linux operating system was used in each PC. 4.2

Experiment I: Various Application Scenarios

In this experimentation technique, several obstacle circumstances are generated and then inspected with and without I-AIR based system. These obstacles might occur by various causes. The task of a subject is to determine only one cause of a failure. 1. Cannot Connect to the Specific Host: In this case, file-transfer from pcD1 to pcB1 is not possible. A rare cause has been presumed, that is, a problem with the settings of Network Interface Card (NIC) of the host computer (pcB1). 2. Transmission of Spam Mail: In this case, a spam mail is transmitted from pcD1. However, the originating location of spam is concealed, so it is required to detect accurately the host that sends out the illicit message. 3. Slow Network: This delinquency is reported in the case of accessing World Wide Web (WWW) connection. The notebook PC (pine) was infected through an attack (from MSBlaster from outside source) at the port 135, thereby hindering its access to the Internet. 4. Mail Sending/Receiving Error: Here, the client network encounters the problem in sending/receiving email because the reason that the SMTP server process is down.

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Table 2. Experimental results (Exp.1) 1. Cannot Connect to the Specific Host A B C D E Average Time Step Time Step Time Step Time Step Time Step Time Step no I-AIR 1056 20 756 20 680 22 771 20 282 40 709.0 24.4 I-AIR 99 5 51 2 125 4 226 5 52 2 110.6 3.6 I-AIR (%) 9.4 25.0 6.7 10.0 18.4 18.2 29.3 25.0 18.4 5.0 15.6 14.8 no I-AIR 2. Transmission of SPAM Mail A B C D E Average Time Step Time Step Time Step Time Step Time Step Time Step no I-AIR 1096 24 221 4 901 23 1155 26 92 5 693.0 16.4 I-AIR 49 3 93 3 83 4 129 2 40 2 78.8 2.8 I-AIR (%) 4.5 12.5 42.1 75.0 9.2 17.4 11.2 7.7 43.5 40.0 11.4 17.1 no I-AIR 3. Slow Network A B C D E Average Time Step Time Step Time Step Time Step Time Step Time Step no I-AIR 208 3 205 3 330 9 323 3 682 35 349.6 10.6 I-AIR 528 4 53 1 61 1 63 1 94 1 159.8 1.6 I-AIR (%) 253.8 133.3 25.9 33.3 18.5 11.1 19.5 33.3 13.8 2.6 45.7 15.1 no I-AIR 4. Mail Sending / Receiving Error A B C D E Average Time Step Time Step Time Step Time Step Time Step Time Step no I-AIR 996 31 369 16 680 22 565 7 1499 49 821.8 25.0 I-AIR 98 4 59 2 125 4 81 2 73 2 87.2 2.8 I-AIR (%) 9.8 12.9 16.0 12.5 18.4 18.2 14.3 28.6 4.9 4.1 10.6 11.2 no I-AIR Management experience: A. 1 year, B. 2 year, C. 2 year, D. 3 year, E. 7 year

Results. The experimental results were compiled into Table 2. The results show that, for each failure situation, with the inclusion of I-AIRs, the management load related to the time taken to resolve a certain fault as well as the number of steps necessary to locate the cause of failure was reduced to an average 20%. 4.3

Experiment II: One Obstacle from Various Causes

An application scenario is tested against various causes for the occurrence of a specific failure condition to demonstrate the flexibility of the proposed approach using I-AIRs. Furthermore, these causes do not occur necessarily in any fixed pattern. The checks to detect these causes are performed randomly. However, using I-AIRs is advantageous because every check is done only once during the course of the fault-localizing process. The failure cause is detected and the main cause behind the failure is reported to the network operator actively.

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causes a. Cable was disconnected. b. The 25th port was closed. c. The 110th port was closed. DNS Server problem d. DNS Server process was downed. e. Config was not available. Mail Server problem f. Mail Server process was down. Table 4. Experimental results among individual administrators (Exp.2) F G H I J Time Step Time Step Time Step Time Step Time Step d 158 9 b 566 8 e 929 23 f 235 5 a 655 19 no I-AIR e 743 24 d 871 12 b 339 9 c 615 9 f 182 5 a 51 1 f 104 2 c 82 3 a 40 1 b 86 2 I-AIR f 85 4 c 106 2 d 52 3 e 74 2 e 128 6 I-AIR (%) 151.1 15.2 14.6 20.0 10.6 18.8 13.4 21.4 25.6 33.3 no I-AIR Management experience: F. 2 year, G. 2 year, H. 3 year, I. 3 year, J. 7 year Table 5. Experimental results among individual failures (Exp.2)

no I-AIR I-AIR I-AIR non I-AIR (%)

a Time Step 655 19 – – 51 1 40 1 6.9

b Time Step 566 8 339 9 86 2 – –

c d e Time Step Time Step Time Step 615 9 158 9 743 24 – – 871 12 929 23 106 2 52 3 74 2 82 3 – – 128 6

f Time Step 235 5 182 5 85 4 104 2

5.3 19.0 23.5 15.3 27.8 10.1 28.6 12.1 17.0 45.3 60.0

Table 3 depicts the failure situation ”Mail Sending/Receiving Error” with some possible causes underlying the occurrence of this anomaly. The task of the subject is to determine the cause of this error. Results. Experimental results computed by each manager while resolving the mail sending/receiving anomaly were compiled into Table 4. Additionally, the results corresponding to each failure cause were accumulated into Table 5. The results demonstrate that the network management overhead regarding the time taken to resolve a certain fault, along with the number of steps necessary to locate the cause of failure, were reduced to 20% on average, which concurs exactly with the results of Experiment 1.

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Summary

This paper presented a novel technique for the automation of management tasks for communication network systems. The foundation of the proposed framework is the use of I-AIRs, which, through active mutual interaction and with the functional network system, can resolve various network-failure situations. A part of the I-AIR knowledge is modified dynamically and frequently according to the operational characteristics of the network. Moreover, experimental results demonstrated a marked reduction in the administrator workload through the use of the proposed automated network monitoring and fault detection functions. Acknowledgement. The authors thank Mr. Satoshi YOSHIMURA for his contribution to this study.

References 1. Stephan, R., Ray, P., Paramesh, N.: Network management platform based on mobile agent. International Journal of Network Management 14, 59–73 (2003) 2. Consens, M., Hasan, M.: Supporting network management through declaratively specified data visualizations. In: IEEE/IFIP 3rd International Symposium on Integrated Network Management, pp. 725–738 (1993) 3. Bouloutas, A.T., Calo, S., Finkel, A.: Alarm correlation and fault identification in communication networks. IEEE Transactions on Communications 42(2,3,4), 523– 533 (1994) 4. Kinoshita, T.: A method for utilizing distributed information resources effectively: Design of active information resource (in japanese). In: Technical Report of IEICE (Japan) AI99-54, 13–19 (1999) 5. Li, B., Abe, T., Sugawara, K., Kinoshita, T.: Active information resource: Design concept and example. In: The 17th International Conference on Advanced Information Networking and Applications, pp. 274–277 (2003) 6. Li, B., Abe, T., Kinoshita, T.: Design of agent-based active information resource. In: The 1st International Conference on Agent-Based Technologies and Systems, pp. 233–244 (2003) 7. Papavassiliou, S., Puliafito, A., Tomarchio, O., Ye, J.: Mobile agent-based approach for efficient network management and resource allocation: Framework and applications. IEEE Journal on Selected Areas in Communication 20(4), 858–872 (2002) 8. Fujita, S., Hara, H., Sugawara, K., Kinoshita, T., Shiratori, N.: Agent-based design model of adaptive distributed systems. Journal of Applied Intelligence 9(1), 57–70 (1998)

Design and Implementation of Interactive Design Environment of Agent System Takahiro Uchiya1, Takahide Maemura2, Xiaolu Li2, and Tetsuo Kinoshita3 1

Research Institute of Electrical Communication, Tohoku University 2 Graduate School of Information Science, Tohoku University 3 Information Synergy Center, Tohoku University 2-1-1 Katahira, Aoba-ku, Sendai, 980-8577 Japan [email protected], [email protected], [email protected], [email protected]

Abstract. The agent-based systems have been designed and developed using the latest agent technologies. However, the design and the debugging of these systems contain some difficult problems due to the situational and nondeterministic nature of the agents, and the effective design-supporting technologies have not been proposed so far. In order to make efficient design process of agent system, we propose an interactive development method of agent system based on the agent-repository-based multiagent framework which focuses on an essential feature of agent system design, i.e., the reuse of existing agents stored in the agent repository. In this paper, we propose an Interactive Design Environment of Agent system called IDEA and demonstrate the effectiveness of the proposed method. Keywords: agent design method, repository-based agent framework.

1 Introduction Agent technology is one of the primary technologies for the next-generation information processing system. Generally speaking, software with new characteristics such as autonomy and sociality is called an agent and a knowledge information system which uses agents as its components is called an agent system. Many kinds of agent systems have been designed and developed using latest agent technologies. However, the design and debugging of agent systems contains some difficult problem due to not only the situational and nondeterministic nature of the agents, but also the lack of effective design-supporting technologies. So far, we have proposed an agent-repository-based agent framework [1, 2], which accumulates the developed agent systems in the agent-repository and enables dynamic organization and reorganization of agent systems by using agent-repository. Using this framework, we designed and developed various agent systems in our previous work [3, 4, 5, 6]. Hereafter, we simply call the agent-repository as the repository. In this paper, in order to provide an efficient and systematic design process of agent system for agent system designers, we propose a new method for developing agent systems in an interactive way, based on the repository-based agent framework. This H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 1088–1097, 2007. © Springer-Verlag Berlin Heidelberg 2007

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method focuses on the important features of agent system development such as the reuse of existing agent system and the cooperation between designers and agent systems. Moreover, an Interactive Development Environment of Agent system called IDEA is proposed and implemented to show the effectiveness of the proposed method.

2 Problems in Agent System Development 2.1 Problems in Agent Design An agent system is composed of various agents that perform the required information processing. In the design of agent system, the design targets should be the agents equipped with various functions and the organization of agents that deal with the problem solving. In a design of an agent of the target agent system, several types of agents can be considered. For example, the following four types of agents can be introduced, i.e., (a) Basic type, (b) Reactive type, (c) Deliberative type, and (d) Composite type, on the basis of the structure and functions of the agent. A designer determines an agent type and implements the functions and knowledge of an agent based on the requirements and usage of the target agent in the design process. Here, we can find a problem such that (P1) the volume of descriptions of the target agent increases according to the volume of functions and knowledge required for both the problem solving and the behavioral control of the agent. On the other hand, from the point of view of the design method of agent, there are two kinds of approaches, i.e., (i) Programming Approach and (ii) Framework Approach. (i) Programming Approach An agent is designed and implemented in a top-down way, by using the existing programming languages such as Java. Since the flexibility of design can be kept in the design process, the agents such as basic type and reactive type can easily be implemented. However, in case of deliberative type and composite type agents, the burden of designer increases due to the design and implementation of knowledge processing mechanisms embedded in all of the agents. (ii) Framework Approach Since this approach provides an agent design support environment based on the specific agent architecture for the designers, the design and implementation of the agents can be done systematically and efficiently. Such an environment is called a framework that provides the facilities such as the knowledge representation scheme, the problem solving function and the agent communication function. In recent years, a lot of frameworks such as ADIPS/DASH [1, 2], JADE [7], AgentBuilder [8], JATLite [9], ZEUS [10] and JACK [14] had been proposed and implemented. These frameworks provide many functions for designing and implementing various agents to designers, where we will encounter the second problem, i.e., (P2) the designers need to learn a lot of know-how about specific agent framework.

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2.2 Problems in Agent System Design Development of an agent system that consists of many agents can be done by two types of design, i.e., top-down design and bottom-up design. (1) Top-down design A top-down design process consists of the following stages, i.e., problem definition, requirements definition, design, implementation, test and verification, as same as the conventional software system. To realize the target agent system, a lot of design processes of both the agents and the organization of agents have to be carried out sequentially or concurrently. To fulfill top-down design, the design methods/tools such as ZEUS [10], MaSE [11], Tropos [12], GAIA [13] and JACK [14], have been proposed. Using these methods, the designers can promote the consistent design of an agent system as well as its component agents systematically; therefore, the unexpected result such as the competitive situations among agents may easily be detected and removed at the design phase. However, it become difficult to deal with the changes of the architecture and functions of the whole system after the development has been finished. (2) Bottom-up design In the bottom-up design, it is assumed that the required agents can be selected from a set of the existing agents and assigned them as the components of the target agent system, without the design and implementation of new agents. It is difficult to find out all needed agents for the target system. However, the designers can concentrate on the design of new agents that realize the required new functions, if some of the existing agents could be reused as a part of the target agent system according to the given requirements. Moreover, in the bottom-up design, the function of autonomous and dynamic organization based on the existing agents can be expected at the runtime. Although the new functions are required to support the designers, the bottom-up design is useful method for the agent system development. But useful designsupporting tools have not been provided for the designers. Although the design and implementation of an agent system can be done in the topdown or bottom-up manner, as one of the existing problem in both methods that (P3) it is hard to test and debug the target agent system due to the non-deterministic and situation-dependent nature of the multiagent organization. The generate-and-test cycle in an explorative design process of agent system should be supported effectively. As mentioned above, there are three problems in order to overcome the difficulties of design and implementation both for the agent and the agent system. Therefore, in this paper, we focus on the bottom-up design based on the framework approach, an interactive development method is proposed and a prototype of design-supporting environment is also demonstrated to show the effectiveness of the proposed method.

3 Concept of Interactive Development of Agent System 3.1 Overview of Interactive Development Method In this section, we propose an interactive development method of agent system. At first, we explain the fundamental idea of the proposed method.

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ƒ Reuse of existing agents: To support a bottom-up design process, the agents and the agent systems that have already been designed and utilized as applications, are stored and managed to reuse them in the design process for new agent system. For instance, the repository of the repository-based agent framework explained in section 3.2 can be utilized as one of the essential mechanisms. ƒ Interaction between designer and agent system: To support the generation-and-

test cycle in the bottom-up design process, some supporting functions are provided for the designers. For instance, an interactive simulation of agents’ behavior over a virtual distributed environment may be useful for the test and debugging of agents. 3.2 Repository-Based Agent Framework The repository-based agent framework ADIPS/DASH [1, 2], as shown in Fig.1, has a mechanism called the repository that is responsible to manage and control the lifecycle of agents in ADIPS/DASH framework. The agents developed by designers are accumulated in the repository in order to generate at runtime and construct the organization at runtime according to the requests given by users or other agents. The repository of ADIPS/DASH framework is one of unique mechanisms compare with other framework mentioned in section 2.1. Repository

Workplace

Service Requirement

Task Announcement

Manager Agent

Creation User

Organization Procedure Contractor Agent 1.Task Announcem ent

Service Provisioning

User Environment

2.Bid

Network 3.Award

Agent 4.Creation

4.Creation

Fig. 1. Repository-based agent framework

3.3 Interactive Design Process of Agent System Let’s explain an interactive design process of agent system. In the following design stages, we focus on the design and implementation stage on a basis of the bottom-up design. A. Problem Definition A problem is defined in this stage, for instance, by using a design specification language and natural language. B. Requirements Definition The requests from users are defined and described as the requirement specifications of the target agent system. For instance, a state transition diagram or an agent knowledge

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description language of ADIPS/DASH framework can be used to represent the specifications. Some simple examples of requirement are shown as below. Example1: (task :name XYZ :attribute1 value1 :attribute2 value2 ... ) Example2: (task :name www-browser :window-system X11 :width ?width :height ?height :color ?color :input url :output (graphic-image text-image audio) ...) Example3: (task :name TownGuide :city Sendai :country Japan ... ) C. Design and implementation An agent system is designed and implemented based on the following secondary processes. C-1. Trying to reuse the existing agent system A designer sends a requirement of the necessary function to the repository to reuse the existing agents and agent system. According to the requirement, each agent in the repository examines whether the given specification is fulfilled or not by itself. As a result, the designer receives the responses from the agents in the repository and selects a suitable agent to be used in the design. C-2. Programming of agent knowledge and function Using an agent knowledge representation language of ADIPS/DASH framework, a designer describes the behavioral knowledge as a set of rules (it is called an agent program in ADIPS/DASH framework). Some parts of functions will be designed and implemented by using the conventional programming language. These are combined with the agents by using the wrapping mechanism of ADIPS/DASH agent. To make the agent programming process simply, a knowledge template such as the extended contract net protocol of ADIPS/DASH agent, are provided for the designers. C-3. Interactive simulation As mentioned in section 3.1, an interactive simulation function of agents’ behavior is provided for a designer to support the generate-and-test cycle in the bottom-up design process. Using a virtual distributed environment defined by the designer, the designer can generate the agents from the repository and observe their behaviors. The behavioral simulation of an organization of agents can also be done, and the same as a single agent. Moreover, a function to exchange messages between designer and agents is provided to test the agent behavior at development phase. Using this function, for instance, the designer can run a part of an agent, which is under development, to simulate its cooperation behavior in the virtual distributed environment. Through the interactive simulation, the designers can detect and modify the deficits of knowledge and functions of agents quickly by returning to the stage C-2. C-4. Registration of agent system to the repository The results of design for the target agent systems can be stored in the repository at any stage of the design process, in order to support a step-wise refinement in the agent system development.

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D. Test and Verification The functions of the target agent system should be verified in the real environment.

4 Design and Implementation of IDEA According to the proposed method explained in the chapter 3, we design and implement a prototype of the Interactive Design Environment of Agent system called IDEA. In order to support the design stage C explained in chapter 3, the following four functions are introduced in the IDEA, as shown in Fig.2. (M1)Mechanism of agent search support

Acquisition

Agent search and acquisition

Registration

Agent registration

Search Agent Repository

(M4) Mechanism of agent registration support

(M2)Mechanism of agent programming support

Agent program Programming of agent knowledge

Simulation environment Agent Repository

Agent Workplace

Development

Agent assistance

Agent Developer

Display and edit of inner state of agent

IDEA (M3)Mechanism of agent simulation support

Internet

Agent

Fig. 2. Construction of the IDEA

(M1) Mechanism of agent search support Fig.3 shows an interface of the agent search phase. There are main three parts, that is, the search condition input field for searching the agents from the repository, the search result display tab and the preview window of the agent knowledge. In the search condition input filed, a designer inputs the requirement specification of a candidate agent, such as an agent name and a function name. When a candidate agent detected in the repository sends a message as its response, the received message is displayed in the search result display tab. Then, the designer can take an agent into the developer’s environment by choosing the agent from this tab. (M2) Mechanism of agent programming support Fig.4 shows a mechanism for agent programming support. This mechanism has an agent-programming editor based on the rule-based knowledge representation of ADIPS/DASH framework. Using this editor, the designer can describe and test the agent programs. (M3) Mechanism of agent simulation support Fig.5 shows an agent monitor for observing the behavior of the agents and the agent organization. The agent inspector shows the inner states of an agent. Using these tools, the designer can browse and modify the agent behavioral knowledge during the

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interactive simulation. Moreover, an ACL editor supports the interactive communication between the designer and the agents under development by the ACL message of ADIPS/DASH framework. (M4) Mechanism of agent registration support This mechanism offers an interface to store the completed agent system to the repository.

Editor of agent program

Fig. 3. Supporting function of agent search phase

Search condition input field

Search result display tab

Preview window of the agent knowledge

Fig. 4. Supporting function of agent programming phase

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Agent inspector

Fig. 5. Supporting function of agent simulation phase

5 Experiment and Evaluation 5.1 Evaluation of Agent Programming Support Using a prototype of the IDEA system, we evaluated the amount of agent knowledge description in the bottom-up development. Two cases of the agent system development have been measured, i.e., (1) without reuse the existing agent system in the repository, and (2) reuse the existing agent system by using the IDEA system. In the case (1), the designers created the protocol, the rule set and the agent knowledge from the scratch. Test and debug had been repeated and the burden of the designers became large. On the other hand, in case (2), the designers can search the reusable agents from the repository, and assign the selected agents as a part of agents of the target system. The knowledge templates such as the contract net protocol template could be utilized effectively. Table.1 shows the results of experiment. We confirmed that the amount of agent knowledge descriptions is reduced to 54% on the average. 5.2 Evaluation of Simulation Support The amount of workload had been measured to evaluate the simulation support function of IDEA. The following measures are introduced, i.e.,

・ The number of fact used as the start conditions :N ・ The number of times of error :k ・ The time required by the completion of correction of the i-th error from a cooperation operation start :Ti (i≤k)

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・ The required time from the operation start to the end in the case of being errorless :Tmin ・ The required time from the operation start to the end using IDEA :Ta The result of this experiment is shown in Table 2. By using the [Ex.2] in the section 5.1 we set up the agent system so that three errors may occur at the beginning, middle stage and the end of operation. The results are as follows. First, Tmin was about 75 seconds. Next, we measured the time of debugging without IDEA. The total time is about 291 seconds. This is because whenever the designer corrects the agent knowledge in the simulation phase, the designer has to reboot the agent system and input the initial fact again and again. Then, the debugging time increases according to the number of errors. Finally, we measured the time of debugging with IDEA. The total time of this condition is about 137 seconds. This is because the rebooting of the system is no longer necessary and the designer can set the initial facts by using IDEA. Table 1. Evaluation of agent programming support Ex.1 Hotel Selection System

Ex.2 Meeting Schedule Adjustment System

Ex.3 Hotel Reservation System

Ex.4 Retrieval System of Manual of UNIX Command

Ex.5 Retrieval System of Presence Information

Ex.6 Ad-hoc Communicat ion Service

Ex.7 Network Manageme nt System

Ex.8 Asynchrono us Messaging System

655

385

266

1863

839

12991

15479

35927

No reuse

Total number of description lines

Reuse

Total number of developed agents

9

4

5

60

8

11

89

23

Number of agents that developer reused using the repository

3

1

4

60

7

10

84

23

Number of agents which is developed newly

6

3

1

0

1

1

5

0

Total number of description lines䠷n䠹

655

385

266

1861

839

13018

15479

35996

Number of reused lines 䠷n-r䠹

316

107

107

1331

419

6894

12596

24635

Number of lines which is newly written 䠷r䠹

339

278

159

530

420

6124

2883

11381

The rate of description curtailment 䠷((n-r)/n)㽢100䠹

48%

28%

40%

71%

50%

53%

81%

68%

Table 2. Evaluation of reduction of debugging work The number of times of starting of agent system Without IDEA

k+1

IDEA

1

The number of times of input the fact

×N

(k + 1) N

Execution time

ΣT + Tmin i

Ta (Tmin < Ta 1) then Set m = m − 1; j = 1 while (all agents-initiators are not examined) Attempt a group breaking activity of agent initiator j;  Set A = A \ {j}, and N = N {j}; Set j = j + 1; End while; Set j = 1 While (all agents-initiators are not examined) Find free individual agents to be attached to j; Set j = j + 1; end while; end if; end while;

cuts. The technological constraints may impose a fixed orientation on the items (2BP|O|G) or allow the items to be rotated by 90◦ (2BP|R|G). The adaptation of AB to the 2BP|O|G is given in Table 2. The goal of an agent initiator is to pack as efficiently as possible the individual agents of its group into a bin while respecting the guillotine, orientation, and capacity constraints. The goal of a free individual agent is to position itself in the bottom left most position of the unused area or vacancy -among all agent initiators’ vacanciesthat maximizes its occupancy ratio. AB creates n free individual agents corresponding  to the n items of N, and sets A = ∅. It initializes the priority list U = I I (where the elements of I = {i ∈ N : li > L/2, wi > W/2} and I = N \ I are ordered in non-ascending order of their areas). It chooses the first m items of U as agent-initiators, removes them from U, and appends them to A. Agent-initiator j ∈ A sets Jj = {j}, and positions itself in the bottom-left most position of the bin; i.e., in position (xj , yj ) = (0, 0). It chooses the cut type by favoring the one that leaves the largest unused area. Consequently, it enters, into its queue of vacancies Cj , the two unused areas {S1 , S 1 } if the cut is horizontal and {S2 , S 2 } if the cut is vertical (cf. Figures 1.a and 1.b, respectively); with each element of Cj being characterized by its attributes which are its dimensions and relative coordinates with respect to its bin. Cj is a firstin- first-out queue; so the first element of Cj is processed first whereas newly created vacancies are tagged to the end of Cj . Having been initialized, j undertakes group formation actions which are directed toward inviting a subset of U to join Jj . It sets its current unused area Sj = (Lj , Wj ) to the first vacancy queued in Cj . It then identifies the best free

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S. Polyakovsky and R. M’Hallah Table 2. The AB system adapted to the 2BP|O|G // Initialization of the AB System // Create n free individual agents; each corresponding to an item of N. Set m = M, and A = ∅. Construct the priority list U of free individual agents. // Iterative Step // While ((m ≥ 1) and (there are free agents in the system)) Let the first m individual agents of U initialize themselves as agents-initiators; append them to A; and remove them from U ; Flag=True; while (Flag=True) Flag=False; Set j = 1; //j is the index of the current agent initiator being examined. // While (j ≤ m) Set i = 1; //i is the counter of vacancies. // While (i ≤ 2) Find a free individual agent to be attached to j (i.e., to be packed in Sj , the first vacancy in Cj ); if (candidate for attachment is found) then Flag=True; Remove Sj from Cj ; else transfer Sj to C j ; end if; Set i=i+1; end while; Set j=j+1; end while; end while; if (there is no free agent in the system) and (m > 1) then Set m = m − 1; j = 1 while (all agents-initiators are not examined) Attempt a group breaking activity of agent-initiator j;  Set A = A \ {j}, and U = U {j}; Set j = j + 1; end while; Set j = 1 while (all agents-initiators are not examined) Find free individual agents to be attached to j for every Sj ∈ C j ; Set j = j + 1; end while; end if; end while;

Fig. 1. Illustrating the cut type and the resulting vacancies

individual agents that can be positioned in Sj , and invites each of them to join its group by sending it an attachment offer. The attachment of any of these free individual agents to Jj increases the packing coefficient of the bin; thus, improves the fitness of j. When receiving an offer from agent initiator j ∈ A, a free individual agent i ∈ U decides whether to reject or accept it by undertaking a group joining action. It sends each potential agent initiator j  ∈ A\{j} a request of subscription asking for the attributes of the vacancies of Cj  that can fit i. Upon receipt of the request from i, agent initiator j  ∈ A \ {j} processes the request by sending

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the suitable elements of its Cj  back to i. Then, i computes its occupancy ratio for each vacancy, and reaches a decision. The occupancy ratio rij of agent i in area Sj is the ratio of the area of i to the area of Sj . If rij is close to 1 and is the highest ratio among all potential agent initiators’ vacancies, i accepts the invitation of j; else, it rejects it. Upon undertaking a decision, i relays the information to j. Agent initiator j records the response of each individual agent it invited; analyzes the responses; chooses the best free individual agent i∗ , and notifies all the free individual agents it invited about its decision. i∗ changes its state from free to busy, and leaves U and N . Subsequently, j attaches i∗ to its group Jj ; and chooses the type of cut to undertake by comparing the areas of S1 = (Lj , Wj − wi∗ ) and S2 = (Lj − li∗ , Wj ). It updates Cj by removing Sj from Cj and tagging the two newly created unused areas to the end of Cj . Finally, it updates the attributes of i∗ (i.e., orientation and coordinates relative to the left bottom corner of the bin). If no free individual agent is attached to Jj , Sj = (Lj , Wj ) is removed from Cj . Sj is permanently removed from the agent initiator’s memory if none of the n items fits Sj ; that is, Lj < min {li }, or Wj < min {wi }. On the other i=1,n

i=1,n

hand, if there are items that fit into Sj but these items are currently busy individual agents or agent initiators, then Sj is appended into the queue of nonfilled vacancies C j . By joining C j , Sj will later be reconsidered for possible filling (with some of these items) when group breaking activities take place. Thereafter, the process proceeds as described in Section 2 with agent initiator j undertaking a group breaking action if its group’s fitness is less than a prefixed threshold level. The data structure adopted in this problem is a special modification of the basic matrix structure (two-dimensional array). It is a V × V matrix, where V is the number of partitions such that V ∈ N, and 1 ≤ V ≤ min{W, H}. Thus, each cell of the matrix contains a set of items whose sizes belong to the range of the cell; i.e., the agents of N are distributed among the cells of this matrix. The proposed matrix data model describes the space of interacting agents. When searching for a suitable free individual agent, an agent initiator residing in one of the matrix cells dynamically updates its individual vision of the matrix to few cells (which satisfy the required parameters). It then explores them starting from the cell, corresponding to the agent whose priority is highest. The adopted data structure speeds the computation time over all instances by 10%. Figure 2 illustrates the variation of run time as V increases. When V = 1, the matrix model reduces to a linear array. The performance of AB when applied to the 2BP|O|G is assessed in terms of runtime and solution quality. AB is compared to the tabu-based heuristics of [5]. AB is coded in Borland Delphi 6.0 with Object Pascal, and run on an Athlon XP 2800. AB’s runtime varies between 10 milliseconds for 40-item instances and 1400 milliseconds for 1000-item instances. It is less than 14 minutes for 100000 items.

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Fig. 2. Run time t as the number of partitions V varies

(No comparable heuristic is known to handle such large problems.) These run times are obviously negligible; thus, will not be further discussed. AB is tested on the instances of [1,6]. For each class, five problem sizes are considered: n = 20, 40, 60, 80, and 100. Each problem type is replicated ten times. The analysis of the results reveals that AB performs on average better than TS for the 2BP|O|G. For all classes, the average ratio of AB is less than TS’s: it is 1.055 for AB versus 1.080 for TS. For the 2BP|R|G, AB is consistently as good or better than TS. Its average ratio is 1.035 versus 1.065 for TS. AB has several advantages in comparison to traditional heuristic approaches [2]. First, its interacting agents strive continuously towards increasing their groups’ fitness. Second, decisions are revokable since agent initiators’ groups can be disassembled, and agents can be reassigned to new groups. Third, the pseudo-parallelism of the approach helps it perform better in terms of solution quality. Fourth, its reconstruction process maintains the “good” part of the solution and rebuilds the “weaker” part; thus, mimics path relinking based heuristics. Last, its stochastic nature allows it to investigate a larger part of the solution space; thus, ends up getting a high quality solution.

4

Conclusion

This paper proposes a general distributed artificial intelligence framework for complex combinatorial optimization problems. It uses a dynamic pseudo-parallel stochastic AB system of active interacting agents; each with its own characteristic (parameters), fitness, and decision process (or rules or logic of work). It applies the principle of self organization of the agents to build solutions, and intensifies the search around their neighborhoods till reaching a local optimum. When the system reaches steady state, AB diversifies the search looking for a near global optima. In this paper, the implementation of AB is illustrated via the NP hard guillotine two dimensional bin packing problem. However, AB, which is modular,

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can be easily adapted to any knapsack-type optimization problem such as early tardy scheduling, districting and timetabling problems. The agents for any new problem are directly inherited from the already developed agents, and adapted to the new problem via the modification of their decision processes and properties. Even though it can be assimilated to a meta-heuristic, AB is not a traditional approach: it is not oriented to solving a particular problem; thus, does not require any major modification to reflect the constraints and the particularity of the problems. Differently stated, AB can be viewed as a new way to design adaptive algorithms. The application of the agent based modeling to complex combinatorial problems will lead to better informed decision making, and to the resolution of large scaled problems.

References 1. Berkey, J.O., Wang, P.Y.: Two-dimensional finite bin packing algorithms. Journal of the Operational Research Society 38, 423–429 (1987) 2. Mukhacheva, E.A., Valeeva, A.F., Kartak, V.M., Filippova, A.S.: Models and methods for orthogonal packing and cutting problems: analytical review. Application to Journal of Information Technologies 5, 1–17 (2004) 3. FIPA Request Interaction Protocol Specification http://www.fipa.org/specs 4. Jedrzejowicz, J., Jedrzejowicz, P.: Agent-Based Approach to Solving Difficult Scheduling Problems. In: Ali, M., Dapoigny, R. (eds.) IEA/AIE 2006. LNCS (LNAI), vol. 4031, pp. 24–33. Springer, Heidelberg (2006) 5. Lodi, A., Martello, S., Vigo, D.: Heuristic and metaheuristic approaches for a class of two-dimensional bin packing problems. INFORMS Journal on Computing 11, 345–357 (1999) 6. Martello, S., Vigo, D.: Exact solution of the two-dimensional finite bin packing problem. Management Science 44, 388–399 (1998) 7. Parinov, S.I.: Toward a theory and agent based model of the networked economy (July 1999) http://rvles.ieie.nsc.ru/parinov/theory/eng/index.htm 8. Parunak, H.V.D.: Agents in Overalls: Experiences and Issues in the Development and Deployment of Industrial Agent-Based Sytems, Intern. J. of Cooperative Information Systems 9(3), 209–228 (2000) 9. Polyakovsky, S., M’Hallah, R.: An agent-based approach to the two-dimensional guillotine bin packing problem. European Journal of Operational Research (submitted 2006)

Constraint-Based Approach for Steelmaking–Continuous Casting Rescheduling Tieke Li1 and Dongfen Guo1,2 1

University of Science and Technology Beijing, Beijing 30 Xueyuan Road, Haidian District Beijing 100083, P.R. China 2 Shijiazhuang Post and Telecommunication Technical College, Shijiazhuang, China, 050021 [email protected]

Abstract. Steelmaking-continuous casting rescheduling problem is an NP-hard problem. In this paper, we present a constraint-based approach for steelmaking– continuous casting rescheduling problem in integrated steel production environment. We treat the steelmaking–continuous casting rescheduling problem as a dynamic constraint satisfaction problem. To maintain the production stability and the material flow continuity after rescheduling, the perturbation measure function of the model is defined as the summation of the absolute deviation between the new start times and the original start times of operations in continuous casting stage, the variable selection and value selection heuristics is designed based on the resource slackness to minimize the change extent of the rescheduling solution. The resource conflicts in the original schedule are incrementally repaired by the variable selection and value selection heuristics. The validity of the proposed model and approach are demonstrated by the computational examples. Keywords: steelmaking-continuous casting; rescheduling; dynamic constraint satisfaction problem; constraint propagation.

1 Introduction Steelmaking-continuous casting is the critical process in steel production, it consists of three stages: steelmaking, refining and continuous casting. Pig iron is transformed into steel liquid by convert furnace (CF) and refining furnace (RF) in steelmaking and refining stages, the steel liquid is then continuously cast into slabs on continuous caster (CC) in casting stage, these slabs will supply to hot rolling stage according to the slab rolling sequence defined by hot rolling schedule. In practical steelmaking-continuous casting process, each stage usually has identical parallel machines, steelmaking-continuous casting scheduling problem has the general characteristic of hybrid flow shop. In addition, steelmaking-continuous casting process has many special constraints, such as the basic job unit in steelmaking and refining stages is Heat, while the basic job unit in casting stage is Cast, which consists of a range of Heats they must continuously cast on casters, i.e., in the last stage of steelmaking-continuous casting process, jobs (Heats) must be divided into H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 1108–1117, 2007. © Springer-Verlag Berlin Heidelberg 2007

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different groups (Cast) and there are precedence constraint and no-break casting constraint within these groups. These special constraints make the scheduling for steelmaking-continuous casting production much more difficult to attack than general hybrid flow shop. Most of the literature on steelmaking-continuous casting scheduling focused on the static problems [1~4]. However, these scheduling systems may not be effective in a dynamic manufacturing environment. During the execution of steel production, machine failure is one of the most frequently occurring unexpected events which can disturb the existing schedule and interrupt the current production, a revised schedule must be arranged in a short time to restore the production. However, fewer research works have addressed the dynamic scheduling problem. LI[5] set up a dynamic scheduling model based on Petri net for steelmaking-continuous production system, given a heuristic algorithm to shorten the total flow time. Cowling[6] proposed a dynamic scheduling method based on real time information, he reduced the continuous casting rescheduling problem to a n / 1 // C single machine sequencing problem, given the rescheduling strategy against the disturbance events of jobs’

processing time changing from p k to p 'k . These researches considered the rescheduling problem in the traditional cold charge situation, where a slab yard exists between steelmaking-continuous casting and hot rolling stages, the material flow continuity and the production stability can be maintained by adjusting the intermediate storage of the slabs, the highlight of the rescheduling is the utility of the machine. In modern steel production factory, steelmaking-continuous casting process and hot rolling process are directly connected at high temperature and form an integrated and synchronized production environment, there is a higher requirement for material flow continuity, steel liquid temperature dropping, and job’s waiting time between adjacent stages. Since there are no intermediate storage areas for buffering between continuous casting and hot rolling stages, any great changes in continuous casting plan may result in great disturbance on its downstream. The goal of steelmakingcontinuous casting rescheduling is not only to restore the consistency in a schedule disturbed by unexpected events, but also to reduce the disruption on downstream so as to maintain the material flow continuity and the production stability. This paper investigates the constraint-based approach for steelmaking-continuous casting rescheduling problem in integrated steel production environment. Section 2 presents the DCSP model of the steelmaking-continuous casting rescheduling problem. Section 3 describes the constraint-based approach for steelmakingcontinuous casting rescheduling. Section 4 presents the experimental results of the computational examples. Finally, conclusions are presented in section 5.

2 The DCSP Model of the Rescheduling Problem 2.1 Dynamic Constraint Satisfaction Problem

A constraint satisfaction problem (CSP) is a 3-tuple: CSP Θ = (V, D, C) where V is a set of variables {v1 , v 2 , , v n } , D is a function mapping the variables to n

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corresponding domains of values d1 , d 2 , , d n , and C is a set of constraints on subsets of the variables in V. Let α Θ be a complete assignment, mapping each variable v i ∈ V to a value in its corresponding domain d i . α Θ is a solution to Θ , if and only if each constraint in C holds under α Θ . A dynamic constraint satisfaction problem (DCSP) is a sequence of CSP which evolves by time, where each one differs from its predecessor by constraint additions or deletions[7]. Consider the case of a CSP Θ transformed into a new CSP Θ1 , the transformation is captured in terms of two sets C add and C del respectively standing for the set of constraints about to be removed from, and added to the constraint C Θ , C Θ1 = C Θ ∪C add \ C del . An optimal solution to a DCSP is a solution to the altered CSP that is minimally different to a previously satisfaction solution. This solving idea has important meaning to many practical rescheduling problems. Sakkout[8]treated the job shop dynamic scheduling problem as a dynamic constraint satisfaction problem, given a probe-based backtrack search approach to minimize the perturbation in dynamic scheduling. Wang[9] modeled the scheduling of the product development projects as a dynamic constraint satisfaction problem, proposed the meta-heuristic approaches including simulated annealing and genetic algorithms to repair the disrupted schedule with the minimum cost of resource constraint violation. In this paper, we model the steelmaking–continuous casting rescheduling problem as a dynamic constraint satisfaction problem, and propose a constraint-based approach to minimize perturbation of the rescheduling solution. 2.2 DCSP Model of Steelmaking-Continuous Casting Rescheduling Problem Notations The notations used in model and approaches are defined as follows:

i –index of Heat; q – index of Cast; j – index of stage ( j =1 denotes steelmaking stage, j =2 denotes refining stage, j =3 denotes continuous-casting stage); m –index of machine; I – set of Heats, I = {1,2,

N} ;

K q –set of Heats in qth Cast; K –set of Cast, K = {1,2,

Q} ;

M j –set of machines in stage j ; oij –operation of ith Heat in stage j ;

cap _ r(oij ) –the number of capacity demanded by oij ; cp(m) –the capacity of machine m ; M (oij ) –the machine used by oij ; AO(m) –set of operations using machine m ; st (oij ) –start time of o ij ; et (oij ) –end time of o ij ; p(oij ) –processing time of o ij ; Init _ st (oij ) –start time of o ij in relaxation problem; FH (q ) –the first Heat of the qth Cast;

δ j –the allowed value of the Heat’s waiting time between stages;

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DCSP model The CSP model of steelmaking-continuous casting scheduling problem is defined as follows: min ε = ∑ max(0, Init _ st (o ij ) − st (o ij )) + ∑ max(0, st (o ij ) − Init _ st (o ij )) . i∈I , j =1

i∈I , j =3

(1)

Subject to: st (oij ) ≥ 0 , ∀i ∈ I , j = 1,2,3 .

(2)

st (oi , j +1 ) ≥ st (o ij ) + p(oij ) , ∀i ∈ I , j = 1,2,3 .

(3)

∑ oij ∈ AO ( m ) st ( oij ) ≤t ≤ st ( oij ) + p ( oij )

cap _ r (oij ) ≤ cp(m) , ∀i ∈ I , m ∈ M j , j = 1,2,3 .

(4)

et (oij ) = st (oij ) + p (o ij ) , ∀i ∈ I , j = 1,2,3 .

(5)

st (oi +1, j ) ≥ et (oij ), ∀i, i + 1 ∈ K q , q ∈ K ; j = 3 .

(6)

st (oFH ( q ),3 ) ≥ Tq , ∀q ∈ K .

(7)

0 ≤ st (oij +1 ) − et (oi , j ) ≤ δ j , ∀i ∈ I ; j = 1,2 .

(8)

K1 ∩ K 2 ∩ K 3

∩ K Q = φ (Null) .

K1 ∪ K 2 ∪ K 3

∪ KQ = I .

(9) (10)

The objective function (1) is to minimizes the Heat’s waiting time and the tardiness of Casts production; (2) ensures the operation’s start time should be greater than zero; (3) is the precedence constraints of operations which are defined by processing route; (4) is the resources capacity constraint; (5) ensures the operation’s processing cannot be interrupted; (6) is the precedence and the no-break constraints within a Cast; (7) means the start time of the Cast q should match up with the hot rolling production plan; (8) ensures the Heat’s waiting time should be less than the allowed value; (9) and (10) ensure that one Heat can only be assigned to one Cast. The unexpected machine failures can be viewed as additions or deletions of constraints to the original problem. Steelmaking-continuous casting rescheduling problem can be regarded as a dynamic constraint satisfaction problem. This provides a mechanism for minimizing the change extent in the solution of the redefined

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problem. The DCSP model of steelmaking-continuous casting rescheduling problem is a 6-tuple: (11) Θ n +1 = (Θ n , α n , C , t , m, δ ) . Θ

add

invalid

Where Θ n+1 denotes the redefined problem, Θ n denotes the original problem;

α Θ n denotes the solution of Θ n . t invalid denotes the period of machine failure, during this period, the resource’s capacity cp(m) changes , this can be viewed as the fact that add a new constraint C add : cp(m) = 0, ∀t ∈ t invalid to the original problem Θ n , the constraints of redefined problem becomes C Θ n +1 = C Θ n ∪ C add . δ is a perturbation measure function that evaluates the difference between two complete assignments to Θ n and Θ n+1 . In integrated steel production environment, continuous casting production plays an important part of supplying slabs to hot rolling production. Great changes in start times of the operations in casting stage will heavily affect the material flow continuity and the hot rolling production stability. In addition, the start times of operations in original schedule are the optimal start times under considering the constraints of steelmaking-continuous casting production and matching up with the hot rolling production. In the revised schedule, the start times of the operations in casting stage should keep changes to a minimum. So the perturbation measure function of the model is defined as the summation of the absolute deviation between new start times and original start times of operations in continuous casting stage.

δ (α Θ n ,α Θ n +1 ) =

∑

o ij ∈O, j=3

st new (oij ) − st original (oij ) .

(12)

3 Rescheduling Approach Modern steel production factory has adopted the automatic control system and computer information management system. The real-time data acquisition system based on the industrial control computers and sensors can monitor the state of machine resource, supply real-time feedback information to production scheduling system. This advanced automation and information production environment not only brings challenges to dynamic production scheduling, but also provides a feasible implementation environment for dynamic production scheduling system. This research is based on the real-time feedback information system. The occurring time and recovering time of machine failure events can be predicted in advance by real-time data acquisition system. 3.1 Basic Idea of the Rescheduling Approach

We build the rescheduling approach based on dynamic constraint satisfaction and repair-based technology. Firstly, the operations related to the failed machine form the

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minimal rescheduling task set A ' . During the solving process, any operation that loses its value will be added to A ' . The inconsistencies in the original schedule are repaired by variable selection and value selection heuristics. The temporal constraint propagation and resource constraint propagation are implemented to enhance the consistency of temporal and resource constraint. When the rescheduling task set A ' becomes null, the approach computes the perturbation measure function value of the current solution, then compares it with the one of current best solution, selects the better solution as the current best solution. 3.2 Formula Used in Rescheduling Approach

(a) The resource capacity slackness Let slack _ cap(m, t1, t 2 ) denotes the resource capacity slackness of machine m in time window [ t1 , t 2 ] . Let cap(m, t1, t 2 ) denotes the available capacity of machine m in time window [ t1 , t 2 ] . Let demand(o ij , m, t1 , t 2 ) denotes the capacity quantity demanded by operation o ij in time window [ t1 , t 2 ] . The resource capacity slackness is defined as follows:

slack _ cap(m, t1 , t 2 ) = cap(m, t1 , t 2 ) −

Where

∑ demand(oij , m, t1, t 2 ) .

o ij ∈AO( m )

(13)

cap(m, t1 , t 2 ) = cp(m) × ( t 2 − t1 ) .

demand(oij , m, t1 , t 2 ) = overlap(oij , m, t1 , t 2 ) × cap _ r (o ij ) . ⎧⎪0, iff [st (o ij ), et (oij )] ∩ [ t1, t 2 ] = 0 overlap(o ij , m, t1, t 2 ) = ⎨ . ⎪⎩min{et (oij ), t 2 } − max{st (oij ), t1}, otherwise (b) The resource conflict value Let Δf m denotes the resource conflict value , if M(oij ) = M(o rj ) Δf m = p(o ij ) + p(o rj ) − (max{et (oij ), et (o rj )} − Min{st (oij ), st (o rj )}) .

(14)

(c) The variable selection and value selection heuristics Variable selection rule: Select an operation o ij with the earliest start time from A ' as the next scheduling variable. Value selection rule: Firstly, compute the slackness of resource capacity of m ( m ∈ M j )in time window [ st (o ij ), et (o ij ) ]; Secondly, select a machine with the maximal slackness of resource capacity as value to assign to the selected o ij , i.e.,

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M(o ij ) = m m = max{slack _ cap(m, st (oij ), et(o ij )), m ∈ M j }. if

more

than

one

machine have the same maximal slackness, then randomly select one machine as value to allocate to operation o ij . (d) The feasible start time window Let [ est(o ij ) , lst(o ij ) ] denotes the feasible start time window of operation o ij lst(oij ) = lst (oi, j+1 ) − p(oij ), j = 1,2 .

(15)

est(o ij ) = est (o i, j+1 ) − p(o ij ) − δ j, j=1,2 .

(16)

(e) The temporal constraint propagation and resource constraint propagation st(oij ) ≤ st (oi, j+1 ) − p(o ij ), j = 1,2 .

(17)

st (o r +1, j ) ≥ st (o rj ) + p(o rj ), where o rj ≺ o r +1, j , M (o rj ) = M (o r +1, j ), j = 1,2,3 .

(18)

3.3 Description of the Rescheduling Approach

Step0: Let scheΦ1 denotes the initial revised solution, scheΦ 2 denotes the current best solution, scheΦ3 denotes the current solution. δ 0 is the perturbation measure function of scheΦ 2 . Maxstart is the maximal number of restarting, M is a very large positive number, C is the counter of restarting number, let scheΦ 2 = scheΦ1 ,

δ0 =M Step1: Determine the rescheduling tasks in original schedule and adding them into A ' Step2: Variable selection and value selection If A ' ≠ φ , select an operation from A ' according to the variable selection rule, then select a machine according to the value selection rule, allocate this machine to the selected operation, go to Step3; if A ' = φ , save this current solution to scheΦ1 , go to Step5. Step3: Resource conflict checking and eliminating According to (14), compute Δf m . If Δf m > 0 , there is a resource conflict, add a precedence constraint between the operations related to this conflict, prune the start time of the operation oij , st (o ij ) = max{est (o ij ), st (o ij ) − Δf m } , go to Step4; If Δf m ≤ 0 , keep the start time of oij no change, go to Step4.

Step4: Constraint propagation After reassigning a value to the selected operation, the temporal and resource constraint propagation are performed, the start times of other operations are updated

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in their feasible start time window, the precedence constraint consistency and resource constraint consistency are enhanced, go to Step2. Step5: Let scheΦ3 = scheΦ1 . Step6: Check if there are resource conflicts in scheΦ3 , if no conflicts exist, go to Step8; else, go to Step7. Step7: First, randomly select a conflict; relax the right bound of the feasible start time window of the operations related to this conflict. Then search a new start time for these operations to eliminate this conflict. Next, implement temporal and resource constraint propagation go to Step6. Step8: Compute the perturbation measure function value δ of the scheΦ3 . If δ< δ 0 , then scheΦ 2 = scheΦ3 , δ 0 = δ , C = C + 1 . If C > Maxstart or δ 0 = 0 , go to Step9; else, go to Step5. Step9: Output the final rescheduling solution scheΦ 2 .

4 Computational Examples To demonstrate the validity of the proposed rescheduling approach, 10 groups of practical production data are used to test the performance of it. The propose approach has been implemented using C++ and run on Pentium 1.4GHZ computer. Table 1 is one group of practical production dada. Table2 is the original schedule; Table 1. Practical production data q =1, T1 =95 j

1 2 3

Mj

3 3 2

i

p (o ) ij

1 50 45 35

2 50 45 35

3 50 45 35

4 50 45 35

q =2, T2 =160

5 50 40 35

6 50 40 35

7 45 40 35

8 45 40 35

9 45 40 35

10 45 40 35

11 35 30 40

12 35 30 40

We randomly generate 4 machine failure events and use the proposed rescheduling approach to cope with these five real-time disturbance events. Table 3 summarizes the rescheduling results and lists the operations their processing plans have been changed in the revised schedule, the numbers or letters with the star symbol on their right corner express the start time or processing machine have been changed. The operations they are not shown in Table3 keep the start time and processing machine no change. δ 0 is the perturbation measure function value of the final revised schedule.

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Index of Heats

st( o i1 )et( o i1 )

1 2 3 4 5 6 7 8 9 10 11 12

Processing machine

0-50 35-85 60-110 85-135 145-195 180-230 75-120 110-155 135-180 180-225 235-270 275-310

1#CF 2#CF 3#CF 2#CF 1#CF 3#CF 1#CF 3#CF 2#CF 2#CF 1#CF 3#CF

Refining stage st( o i2 )et( o i2 )

Processing machine

50-95 85-130 120-165 155-200 195-235 230-270 115-155 155-195 190-230 225-265 270-300 310-340

1#RF 2#RF 1#RF 3#RF 2#RF 1#RF 3#RF 2#RF 1#RF 3#RF 2#RF 3#RF

Continuous casting stage st( o i3 )- Processing machine et( o i3 ) 95 130 165 200 235 270 160 195 230 265 300 340

1#CC 1#CC 1#CC 1#CC 1#CC 1#CC 2#CC 2#CC 2#CC 2#CC 2#CC 2#CC

Table 3. Rescheduling results Real-time events

Operation’s processing plan have been changed in the revised schedule

δ0

t invalid

machine failure

o ij

st new

1

t=50-100

1#RF

o12

t=50

3#RF*

2

t=90-125

2#RF

o 21

t=20*

2#CF

o 22

t=70*

3#RF*

o 32

t=100*

1#RF

o 42

t=145*

1#RF*

o 31

t=40*

3#RF

o 51

t=130*

1#CF

o 81

t=90*

3#CF

o 91

t=135

3#CF*

o10,1

t=180

1#CF*

Index

3

4

t=155-190

t=155-190

3#RF

2#CF

Processing machine 0 0

0

0

5 Conclusions In this paper, we modeled the steelmaking-continuous casting rescheduling problem in integrated steel production environment as a dynamic constraint satisfaction problem, proposed a constraint-based approach to repair the inconsistency introduced

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by the machine failure events. The computational results show the proposed approach can minimize the deviation between new start times and original start times of operations in continuous casting stage, reduce the disturbance on hot rolling production and then keep the material flow continuity and production stability. In the future research, we will improve the proposed approach to cope with the rescheduling problem initiated by other unexpected events, such as the uncertain processing times, uncertain due date of slabs, as well as the ill quality of slabs etc.

References 1. Tang, L., Liu, J., Rong, A.: A mathematical programming model for scheduling steelmaking-continuous casting production. European Journal of Operational Research 120, 423–435 (2000) 2. Harjunkoski, I., Grossmann, I.E.: A decomposition approach for the scheduling of a steel plant production. Computers and Chemical Engineering 25, 1647–1660 (2002) 3. Guanghang, L.I.U., Tieke, L.: A steelmaking-continuous casting production scheduling model and its heuristic algorithm. System engineering 6, 44–48 (2002) 4. Pacciarelli, D., Pranzo, M.: Production scheduling in a steelmaking-continuous casting plant. Computers and Chemical Engineering 28, 2823–2835 (2004) 5. Xiaofeng, L.I., Liyun, X.U., Huihe, S., et al.: Dynamic model of steelmaking and continuous casting system and heuristics algorithm of dynamic scheduling. Journal of Shanghai Jiaotong University 35(11), 1659–1662 (2001) 6. Cowling, P.I., Johansson, M.: Using real time information for effective dynamic scheduling. European Journal of Operational Research 139(2), 230–244 (2002) 7. Dechter, R., Dechter, A.: Belief maintenance in dynamic constraint networks. In: Proceedings of the AAAI, pp. 37–42 (1988) 8. Sakkout, H.E., Wallace, M.: Probe backtrack search for minimal perturbation in dynamic scheduling. Constraints 5(4), 359–388 (2000) 9. Wang, J.: Constraint-based schedule repair for product development projects with timelimited constraints. International Journal of Production Economics 95, 399–414 (2005)

Study on Loop Problem in Opening Database for Chinese Chess Programs Shi-Jim Yen1 , Tai-Ning Yang2 , Jr-Chang Chen3 , and Shun-Chin Hsu4 1

Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan [email protected] 2 Department of Computer Science, Chinese Culture University, Taipei, Taiwan [email protected] 3 Department of Applied Mathematics, Chung Yuan Christian University, Chung Li, Taiwan [email protected] 4 Department of Information Management, Chang Jung Christian University, Tainan, Taiwan [email protected]

Abstract. A Chinese chess program systematically constructs a large tree-based opening database by collecting knowledge from chess masters, books and game records in the opening phrase. However, those games with loops are not managed properly in the database. The perpetual situations are not recorded correctly in the database, and therefore the program will play a draw in an advantageous position and a loss in a draw position. This study describes a solution to the loop problem in opening database. Keywords: heuristic search, computer Chinese chess, opening database, loop problems.

1

Introduction

Chinese chess (Xiang Qi) is one of the most popular board games worldwide, being played by approximately one billion people in China, Taiwan and wherever Chinese have settled. Having a long history, the modern form of Chinese chess was popular during the Southern Song Dynasty (1127AD-1279AD). Chinese chess is a two-player, zero-sum game with complete information. Chinese chess expert knowledge was developed 800 years ago. The world has many excellent human players. The skill of a Chinese chess program can be compared to that of human players. Table 1 shows the space states complexity and the game tree complexity of chess, Chinese chess, Shogi and Go. The space states complexity of Chinese chess was estimated by Allis [1]. The game tree complexity is based on a branching factor of 38 and an average game length of 95 plies [12]. The complexity of other games was estimated by Bouzy and Cazenave [3] and Iida et.al. [6]. The complexity of Chinese chess is between that of chess and H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 1118–1126, 2007. c Springer-Verlag Berlin Heidelberg 2007 

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Shogi. Now that Deep Blue has beaten Kasparov, Chinese chess may become the next popular board game at which a program will defeat a top human player. In fact, Chinese chess programs ELP and SHIGA have beaten some 7-dan players in some human tournaments in Taiwan. A program is expected to defeat a top human player before 2012. Table 1. Space states complexity and game tree complexity of chess, Chinese chess, Shogi and Go Game

Space states complexity Game tree complexity

Chess 1050 Chinese Chess 1048 Shogi 1071 Go 10160

10123 10150 10226 10400

The opening phase of Chinese chess refers to the first 8 12 rounds after a game begins. During this phase, the main objective is to occupy better positions, to establish an advantageous position for later combat, and furthermore, to achieve command over the pieces of opponents and thus a strong battle array. Thus, the opening phase is the foundation of the game [12]. How well the opening phase is played will directly and strongly influence the mid-game and endgame. The current design of the opening phase of Chinese chess is based on an effective and sound opening database system. The database is constructed by gathering games from numerous books and game records, and by extracting and adjusting the abundant master knowledge [4]. Based on the opening database, each move is statistically analyzed, and the optimal move is thus identified. Combined with mid-game programs, the opening database can clearly improve the strength of Chinese chess programs [5]. Certain methods of designing chess programs automatically increase the quality of opening databases [7], take the habitual behaviors of opponents into consideration, and then adopt an appropriate response strategy [10]. In game records of Chinese chess, the same boards frequently appear. Loops due to repetitive boards affect the opening database structure, and create difficulty in gathering statistics about moves. In this study, we develop an efficient data structure and update algorithm to maintain database integrity. This data structure and algorithm solve the problem in gathering statistics about moves in a tree-structured opening database. The organization of this paper is as follows. Section 2 describes the basic concept of a Chinese Chess opening database. Section 3 discusses the influence of the loop problem. Section 4 gives a data structure for of loop. Section 5 shows how to process loop in the opening database. Three key findings of this paper are given in Section 6. A simple repetition rule of Chinese chess is provided in Appendix A.

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Opening Database

Computers simulate human thinking in order to get the best move to attack and defend based on the current situation, and create a game tree during the process. When a game tree expands to a specific depth, each leaf node is assigned a score calculated from an evaluation function, and the best move is identified by the mini-max method [9]. Because the main task during the opening phase is to move pieces to advantageous positions, no obvious fighting occurs, and thus the limited difference between the two sides produces a blind spot in the evaluation function. [12] To overcome this problem, an opening database system was designed using techniques such as framework, tree structure and hash function [4]. Mass good quality games records played by masters from internet are collected and analyzed to retrieve statistical information, including the usage rate and winning rate of each move from each board. The strength of each move can now be compared, and this information can be used as a reference during mid-game play [5]. The architecture of the proposed knowledge-base system has a hash area and an overflow area. The hash function calculates a hash address using move plies and piece positions on board as keys. The address corresponds to a specific slot in the hash area if the slot is available. Otherwise, the board is saved in a slot in the overflow area, and this slot and the specific slot in the hash area are linked. Each node in the database is assigned a specific board status, which has three types of information - position, statistics and links. The position records the ply and the distribution of all pieces used by a hash function. The statistical information includes winning ratio, usage rate, and the differences among win, draw and lose. The links maintain the connection between the hash and overflow areas, and record all moves but alter the board from its previous to its current status [4].

3

Influence of the Loop Problem

In Chinese chess, a repetition occurs when the same position reappears for the third time. The rules that govern perpetual moves (moves repeated in the same situation) vary among in China, Hong Kong and Taiwan. In this paper, this problem will be discussed based on Asian rules [2]. An English translation of part of the Asian rules can be found in [11]. Forty rules govern perpetual situations in Asian rules, some of which are complex for an amateur player. Some simplified repetition rules are listed in [8][12]. A repetition is said to have occurred when the same position is repeated on the board for the third time. Sometimes this occurs when both players are making non-attacking moves (perhaps aimless ones), in which case the game is a draw. More commonly a repetition occurs because one side is perpetually attacking (threatening) a specific opponent piece (e.g. the King, but could be almost any piece). Here these perpetually attacking moves will be called forcing, and any the third-time attacking move is ”forbidden”. Appendix A lists a simple and almost complete summary of repetition rules.

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The loop resulting from repetitions deeply influences the way that opening databases operates. While updating scores using the mini-max method or gathering statistics of game information related to the node, the program will get an inaccurate result, or even worse, will run into an infinite loop. Therefore, loops must be fully recorded in the database so that programs can suggest the correct move. To simplify the process of updating the score using the mini-max method, we delete loops in the opening database in our previous Chinese chess program. When a game contains a loop, the same positions, nodes A and B, are identified; then all nodes between A and B are deleted, and all descendent of B now become those of A. Therefore, before a game is added to the database, all loops in the game are deleted, and the structure of the database becomes a tree without any loop. Although the loop-deletion method simplifies the loop problem, if the program uses scores as a guide in choosing moves, the result will be a draw which actually should be a win, and a loss which actually should be a draw, because loops are not recorded in the database.

4

Data Structure of Loop

To record a loop accurately and completely, a mark is made on each node of the loop with the shortest distance to the leaf. The marked nodes can not have any other child nodes besides those belonging to the loop. Figure 1 indicates that the node sequence ABCDA exists in the database, in which nodes BCDA form a marked loop, and the two As nodes are repetitive nodes with the same position. If a loop node has a child node whose position is different from all of the loop nodes, then the same board with different plies appears on different nodes. Figure 1 shows that nodes E and G are the child nodes of the two nodes As, respectively. This situation affects the operation of the database in two ways: First, when updating score using the mini-max method, one node will have two different scores representing the scores of a loop node and that of a nonloop node respectively. The program must design another mechanism to choose between the two different scores. Figure 1 shows that node A chooses the best score based on three scores from child node G (score 60), loop node B (score -20), and loop ABCDA (score 0, the score can not be shown in Figure 2 because it is an infinite loop since both players repeat the same moves.). Second, the move the opponent chooses will affect the score obtained with mini-max method. An ancestor node in the tree shadows a descendent node such that the nodes with the same position have inconsistent scores. Figure 2 shows that nodes E and G are both child nodes of the two As nodes by playing different moves, and have scores of 100 and 60 respectively. When updating the score, since node B chooses the move to node F, which has a score of -20, the ancestor node A must choose the move to node G which has a score of 60. Hence, although both E and G are child nodes of node A, worse move to node G (score 60) is chosen rather than that to node E (score 100), which is wrong.

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Fig. 1. An example of a loop in the opening database

Fig. 2. Solution of loop problem in the example in Fig. 1

In the tree structure of the database system designed here, all loop nodes are stored at the lowest level of the game tree, and child nodes of loop nodes are moved to upper corresponding nodes with identical positions. Figure 2 shows that loop nodes CDAB have no other child node, and the ancestor node A can choose the better move to node E whose score is 100. In order to make the most of limited time, or achieve a draw when in an inferior situation, a player adopts repetitive plays to repeat among several positions, for

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example through circulated checking and freeing. Such games will have loops that are not in the end of a game. To allow all loops to only exist in the lowest level of the database, we adopt the following steps: Before adding a game to the database, we split the game containing loops into multiple games, including a game without the loops, and games whose positions are from the beginning to the loop position and let the loop to be the last moves of the game. After this procedure, these derivative games are added to the database.

5

Implementation

A new game can be added to the database as follows. First, we flip the first move according to the symmetry of the board. Second, we transform the following moves according to the move order relation. Then, we add the game into the hash area of the database if the specific slot is available, or add it to the overflow area if the slot is occupied. Because each node records the relation between the parent nodes and child nodes, the tree structure is maintained. Finally, we update the score of each node with the mini-max method, and gather statistical information on winning ratio, usage rate, and the difference among the frequencies of wins, draws and losses. If the game being added contains loops, the loops must be stored in the lowest level of the tree structure. First, the loops inside a game must be removed, and the game must be added into the database, as mentioned in section 4.2. Next, the combination of loop nodes must be processed with the nodes existing in the database. This section adopts the method of extending the loop nodes, and then duplicating all the non-loop nodes of the lower level to the upper level. This approach avoids the situation in which the program must choose between loop moves and non-loop moves. Incidentally, if loop nodes are ignored, the database itself is a complete tree structure, making it easy to update scores using the mini-max method. Figure 3 shows three situations about the combination of loop nodes and non-loop nodes. 1. A loop exists in the database, and we want to add a game containing no loops, as shown in Figure 3(a). 2. No loop exist in the database, and we want to add a game containing no loops, as shown in Figure 3(b). 3. A loop exists in the database, and we want to add a game containing a loop, as shown in Figure 3(c). To maintain the architecture that no loops are within the tree structure, when nodes of a new game intersect with existing loop nodes in the database, the loop nodes must be extended into an independent branch that contains only one child, and the branch must be duplicated to the upper node with the same position as the loop nodes. Let LOOP represent the set of nodes whose move results in the same board appearing repeatedly. Let loop head represent the last node in LOOP whose board is the same as that of leaf in the game. If the game is a draw due to

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Fig. 3. Three situations about the combination of loop nodes and non-loop node

repetitive boards, and if the loop nodes exist in the database, we take the following steps. First, nodes in database downward whose positions are the same as those of loop nodes recursively are found, and these nodes then are joined to the LOOP set, with the last one being set as a new leaf. Then, the intersect node upwards from which an independent loop must be extended is identified. Finally, all nodes between loop head and the intersect node are duplicated to those below the leaf node. After an extension is done, if the extended nodes still collide with another node in the database, the above procedure is repeated. Ultimately, only one branch of the loop exists at the bottom of the game tree. After an independent branch of the game is extended, the situation in which the game nodes collide with the loop nodes in the database must be extended. Besides, let the upper loop nodes reserve child nodes and statistic information more completely, making it necessary to duplicate all descendent nodes of a lower node to the upper node with the same position. As this point, the nodes are scanned from leaf to root, and loop nodes are encountered before non-loop nodes, and each node is processed depending on whether it is a loop node or not, as shown below. Loop nodes are processed in the first step. First, because adding a new game result in the loop nodes (say LOOP A) which contained in the database no longer form a complete and independent branch, LOOP A must be extended. Loop nodes (say LOOP B) of the new game are then extended to the child nodes of the corresponding upper loop node in LOOP A. Finally, all child nodes of the last extended node in LOOP A are duplicated to the child nodes of the corresponding upper loop node in LOOP B. Non-loop nodes are encountered in the second step. Because only non-loop nodes of the new game collide with the loop nodes in the database, all that have to do is to duplicate the nodes of the new game to the child nodes of the uppermost loop node after extending the spoiled structure of the loop nodes.

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Conclusions

This study offers a method of managing the loop problem created by playing repetitive moves so as to cause the same board to appear reiteratively in Chinese chess opening database. This study reaches three key findings, as following. 1. If loop nodes are ignored, the database is a complete tree structure, and is compatible with the previous system we developed. 2. While updating statistical information using the mini-max method, this method does not trap into an infinite loop because no loop nodes exist inside the game tree. 3. Nodes with the same board in a lower level are moved to an upper level, so complete information is stored in upper level nodes, and the mid-game program can rapidly retrieve the optimum move. The opening database stores loops in the leaf of the database such that repetitive boards could be completely contained and correct scores can be obtained using the mini-max method. The program does not result in draws that should be wins, or in losses that should be draws.

References 1. Allis, L.V.: Searching for Solutions in Games and Artificial Intelligence. Ph.D. Thesis, University of Limburg, Maastricht, The Netherlands (1994) ISBN 90-9007488-0 2. Asian Xiangqi Federation (in Chinese) (2003) http://tysung.cjb.net/xq/ index.html 3. Bouzy, B., Cazenave, T.: Computer Go: An AI oriented survey. Artificial Intelligence 132, 39–103 (2001) 4. Huang, S.L.: Strategies of Chinese Chess Opening. World Culture Inc. Press (1991) 5. Hsu, S.C., Tsao, K.M.: Design and Implementation of an Opening Game Knowledge-Base System for Computer Chinese Chess (in Chinese). Bulletin of the College of Engineering, N.T.U., (53), 75–86 (1988) 6. Iida, H., Sakut, M., Rollason, J.: Computer Shogi. Artificial Intelligence 134, 121– 144 (2002) 7. Lincke, T.R.: Strategies for the Automatic Construction of Opening Books. In: Marsland, T., Frank, I. (eds.) CG 2001. LNCS, vol. 2063, pp. 74–86. Springer, Heidelberg (2002) 8. Marsland, T.A.: A Review of Game-Tree Pruning. ICCA Journal 9(1), 3–19 (1986) 9. Shannon, C.E.: Programming a Computer for Playing Chess. Philosophical Magazine 41, 256–257 (1950) 10. Walczak, S.: Improving Opening Book Performance through Modeling of Chess Opponents. In: ACM Conference on Computer Science, 53–57 (1996) 11. Wu, E.: English Translation Drafts of Asian Rules (2002) http://www.clubxiangqi.com/rules/ 12. Yen, S.J., Chen, J.C., Yang, T.N., Hsu, S.C.: Computer Chinese Chess. ICGA Journal 27(1), 3–18 (2004)

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Appendix A: A Simple Repetition Rule of Chinese Chess The following is a simple and almost complete summary of repetition rules as they pertain to forcing moves. 1. If only one side attempts to repeat a position with a forbidden move, then they are judged to have lost (i.e., a forbidden move is illegal). 2. If both sides repeat the position using forcing moves, the game is ruled a draw at the third repetition. 3. If both sides repeat non-forcing (non-threatening) moves, the game is ruled a draw at the third repetition. 4. Sometimes a move can be judged in two ways (as, for example, being both a threat move and a threat to checkmate by continual checks in following moves), the move that is forbidden is selected. 5. A side can ask the referee to adjudicate a draw if no piece has been taken, and no more than ten checks have been made by the opponent within the last 40 moves. Forbidden forcing moves are defined as follows. 1. Perpetual checks on the King are forbidden. 2. Perpetual threats on an unprotected piece (except on a Pawn that has not crossed the River) are forbidden. 3. Perpetual threats on a Rook by Cannons and/or Horses are forbidden.

Job Shop Scheduling Optimization Using Multi-modal Immune Algorithm Guan-Chun Luh and Chung-Huei Chueh Department of Mechanical Engineering, Tatung University, Taipei, Taiwan, R.O.C. [email protected]

Abstract. A multi-modal immune algorithm is utilized for finding optimal solutions to job-shop scheduling problem emulating the features of a biological immune system. Inter-relationships within the algorithm resemble antibody molecule structure, antibody-antigen relationships in terms of specificity, clonal proliferation, germinal center, and the memory characteristics of adaptive immune responses. In addition, Gene fragment recombination and several antibody diversification schemes were incorporated into the algorithm in order to improve the balance between exploitation and exploration. Moreover, niche scheme is employed to discover multi-modal solutions. Numerous well-studied benchmark examples were utilized to evaluate the effectiveness of the proposed approach. Keywords: multi-modal immune algorithm, job-shop scheduling problem, clonal proliferation, Gene fragment recombination, antibody diversification.

1 Introduction Scheduling problems exist almost ubiquitously in real-world applications including distribution, transportation, management, and manufacturing, especially in the industrial engineering world. Many scheduling problems on manufacturing industries are quite complex and very difficult to solve using conventional optimization techniques. It has been the subject of extensive research and captured the interest of researchers from different research communities such as operation research, management science, and industrial engineering since the early 1950s. Its main focus is concerned with the allocation of finite resources to tasks to improve the resource utilization. It is well known that the job-shop scheduling problem (JSSP) is one of the most complicated and typical production scheduling problems. The purpose is to improve the production efficiency and reduce the processing duration to gain profits. The JSSP may be described as follows: given n jobs, each composed of several operations that must be processed on m machines. Each operation uses one of the m machines with a deterministic processing time and each machine can process at most one operation at a time. Once an operation initiates processing on a given machine it must complete processing on that machine without interruption. Each job consists of a specific set of operations, which have to be processed according to a given technical precedence order. The operation sequences on the machines are usually scheduled to H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 1127–1137, 2007. © Springer-Verlag Berlin Heidelberg 2007

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minimize makespan, the total time required to complete all jobs. The total number of all possible schedules including feasible and infeasible solutions is (n!)m. A comprehensive survey of job shop scheduling techniques can be found in [1]. In the last decade, numerous biological-inspired methods have been employed to solve practical size JSSP. Among these include simulated annealing [2], tabu search [3], ant colony optimization [4], genetic algorithms [5], and immune algorithm, and [6-10]. Genetic algorithms (GAs), powerful tools based on biological evolution mechanisms and natural selection theory, have received considerable attention as the optimal design efforts. Methods based on GAs have become a popular and effective way for solving large-scale combinational optimization problems, including job shop scheduling. GAs are considered powerful in terms of global optimization, nevertheless, they have several drawbacks regarding local searches. Tazawa et al. [11] has identified two of them as lack of local search ability, and premature convergence. Consequently, a number of researches utilized biological immunity-based optimization approaches [12, 13] to overcome these two drawbacks implicit in genetic algorithms. Xu and Li [6] combined the immune theory and the GAs for solving theJSSP. Similarly, Miyahita [7] applied modified immune algorithm for the JSSP and showed that the calculate time was less than employing GAs. Coello et al. [8], Chandrasekaran et al. [9] and Zhou et al. [10] applied artificial immune system to the JSSP. Two main principles were included in these immune algorithms: clonal proliferation and affinity maturation. Obviously, their immune algorithms did not utilize the whole feature of the biology immune system. To highlight the significant features and completeness of the immune system, a multi-modal immune algorithm (MMIA) [13] is adopted in this study to solve JSSP. Inter-relationships within the algorithm resemble antibody molecule structure, antibody-antigen relationships in terms of specificity, clonal proliferation, germinal center, and the memory characteristics of adaptive immune responses. In addition, Gene fragment recombination and several antibody diversification schemes were incorporated into the algorithm. Moreover, it is intuitive that there exist many different operations with equal makespan as the number of jobs and machines increase. This suggests that with the increase in sizes of the job shop, the resulting JSSP becomes to have multiple solutions and hence a niche scheme is essential to find all the possible solutions.

2 Biological Immune System The immune system protects living bodies from the invading of foreign substances, called antigens, including viruses, bacteria, and other parasites. Lymphocytes float freely in blood and lymph nodes, and patrol everywhere for antigens, then gradually drift back into the lymphatic system, to begin the cycle all over again. There are mainly two types of lymphocytes, namely B-cells and T-cells, which play an important role in immunities. The former takes part in the humoral immunity that secretes antibodies by clonal proliferation, and the latter takes part in cell mediated immunity. One class of T-cells, called Killer T-cell, destroys the infected cell whenever they recognize the infection. The other class which triggers clonal expansion and stimulate/suppress antibody formation is called Helper T-cell.

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The basic unit of an antibody molecule is composed of four polypeptide chains: two identical light chains and two identical heavy chains. The grouping of two different types of gene fragments constructs the light chains and the combination of three different types of gene fragments forms the heavy chains. Additionally, antibody gene fragments could move and rearrange themselves within the genome (inherited DNA) of a differentiating cell. The set of rearranged gene fragments is then transcribed and translated into a complete heavy or light chain. Consequently, the gene fragments required to produce an antibody are encoded in a set of antibody library named germ-line DNA library, each library containing a set of components or fragments of antibodies. An antibody’s paratope can bind with an antigen’s epitope according to their affinity value. In terms of affinities, the immune system selfregulates the production of antibodies and diverse antibodies. After binding to antibody receptors, an antigen stimulates the B cell to proliferate and mature into plasma cells and memory antibodies through the process known as clonal proliferation or clonal selection. In clonal proliferation, random changes (e.g. hypermutation) are introduced to the variable region genes, and occasionally one such change will lead to an increase in the affinity of the antibody. These higher-affinity matured cells are then selected to enter the pool of memory cells. The antibody is not only diversified through a hypermutation process but mechanisms whereby rare Bcells with high affinity mutant receptors can be selected to dominate the immune response (donor of B-cell). On the contrary, those cells with low affinity receptors, or the self-reaction cells, must be efficiently eliminated. Three major categories are reported to increase the diversity of antibodies.

3 Job Shop Scheduling Using MMIA Several different representations have been employed for chromosome-based optimization for JSSP [14-16]. This study utilizes operation-based representation [17] for JSSP in that it guarantees feasible solutions although it is not a one-to-one mapping between the coding space and solution space. Corresponding to the JSSP, the antigen and antibodies serve as objective and associated solutions (i.e. schedules) in a computational model and are expressed as follows:

Abi ≡ x i = possible schedules, i = 1,2,..., N Ab AgAbi ≡ f (x i )

(1)

where Abi (or xi) indicates the ith antibody/schedule while AgAbi indicates the affinity between an ith antibody and antigen/objective, NAb is the number of antibodies. The antibody continuously evolves until a match is found with the specific antigen. The whole procedure is described below: [Step 1] Random initialization of antibody population Similar to the genetic algorithms, the initial integer-string antibody population is randomly generated. An operation-based representation is used in this study. This representation names all operations for a job with the same number and then interprets it according to the order of occurrence in the given antibody. Consequently, an antibody contains n×m genes for an n jobs and m machines problem. Each job appears

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in the antibody m times, and each repeating gene/integer number does not indicate a concrete operation of a job but refers to a unique operation. [Step 2] Gene classification and makespan computation Imitating the biological immune system, each locus of antibody/schedule is classified into two kinds of genes: heavy-chain gene (H) and light-chain gene (L) according to a predefined light/heavy chain-length ratio γ and a job/gene order of occurrence appearing in the identical machine. For an n-job m-machine problem, the number of light-chain genes nL in each antibody is calculated using equation nL = round (γ × n) , where operator “round” means rounded to integer. In addition, the components of light chains are assigned to the latest nL jobs appearing in the same machine. A 3-job and 3-machine scheduling problem is presented here to explain how the encoding scheme operates. Table 1 indicates the normal format showing the processing time and the routing for these three jobs on three machines, respectively. Fig. 1 illustrates the antibody representation and Gantt chart of an antibody [3 1 2 2 1 3 1 2 3], where numbers 1, 2, and 3 stand for jobs 1, 2, and 3, respectively. Each job appears exactly three times in an antibody since it needs three operations/machines. Corresponding to the machine sequence and processing time given in Table 1, the order of machine processing for job 1 is 1-2-3 while 1-3-2 and 2-1-3 are machine orders for job 2 and job 3, respectively. Consequently, the related machine list and time list for this schedule are [2 1 1 3 2 1 3 2 3] and [3 3 1 5 3 2 2 3 3] as Fig. 1 shown. If the light/heavy chain-length ratio γ is set to 0.3, then the number of lightchain genes nL equals to 1 since nL=round(0.3×3)=1. Consequently, the genes (3, 2, 3) located at the last locus of each machine are assigned to light chain as the black blocks illustrated in Fig. 5. As a result, the structural representation of the antibody Table 1. Processing time tij and job routing k of a 3×3 scheduling data

tij J1 J2 J3

O1 3 1 3

O2 3 5 2

O3 2 3 3

k J1 J2 J3

O1 1 1 2

O2 2 3 1

O3 3 2 3

Fig. 1. Antibody representation and corresponding schedule (Gantt chart)

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[3 1 2 2 1 3 1 2 3] is defined as [H H H H H L H L L] since the light-chain genes (3, 2, 3) are located at the 6th, 8th, and 9th locus of the antibody, respectively. Finally, the makespan of this antibody/operation is 14 as the Gantt chart demonstrated. [Step 3] Calculating antibody-to-antigen affinity The antibody-to-antigen affinity value (AgAbi) is utilized to illustrate the combinatorial intensity between antigen/objective and the ith antibody/schedule and the resemblance among antibodies expressed as follows: AgAbi =

obji min{makespani | i = 1, 2, ⋅ ⋅⋅, N Ab } with obji = makespani SCi

(2)

where makespani indicates the makespan value of the ith antibody/schedule and obji is its associated normalized value. In addition, similar to the sharing or niching schemes implemented in multi-modal genetic algorithms, the relationship among antibodies is evaluated according to the similarity count SCi expressed as: N Ab

∑ count ij

SCi =

j =1

N Ab

,

i = 1,2,⋅ ⋅ ⋅, N Ab ; j = 1,2,⋅ ⋅ ⋅, N Ab

(3)

with n⋅m

count ij =

∑ Abijk k

(4)

n⋅m where the similarity count at the kth locus among antibodies Abi and Abj is ⎧⎪1 Abijk = ⎨ ⎪⎩0

if the jobs at the k locus of Abi and Ab j are identical else

(5)

A higher affinity value means that an antibody has a higher activation with an antigen and a lower similarity with the other antibodies. Therefore, the higher the affinity value, the higher the probability that the antibody may be selected as the donor to enter the germ-line DNA library for gene fragment rearrangement. After affinity values of all antibodies are calculated, the best (highest affinity) antibody will be placed into the germinal center for clonal proliferation with the remaining antibodies proceeding to Step 5 awaiting donor selection.

[Step 4] Clonal proliferation of the most matched antibody In biological immune systems, only antibodies stimulated by antigens enter the germinal center. In the proposed MMIA, the most-matched (maximum affinity value) antibody derived from the earlier step is chosen for hypermutation with a user-defined rate and proliferation number. To prevent excessive discrepancies, hypermutation only takes place in the locus of light-chain genes (L). Note that hypermutation is a kind of local search operator since it only occurs in the light-chain genes. To avoid yielding illegal or infeasible schedule after hypermutation operation, the associated gene with job j of the same machine has to mutate to job i at the same time (i.e. reciprocal exchange within the same machine number) when a light-chain gene of an antibody mutates from job i to job j.

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After the hypermutation process, mature antibodies that have a greater affinity than un-proliferated antibody are differentiated into plasma and memory antibodies preserved and updated in the memory pool. Further, the resulting bad antibodies are deleted as immature antibodies imitating the cell apoptosis process in natural immune system. Resulting plasma antibodies combined with the remaining antibodies derived from Step 3 are then proceed to next step for donor antibody selection. As to the memory pool, only the most diverse antibodies with high affinity survive. On the other hand, those antibodies with low affinity and high similarity will be removed from the memory pool (cell apoptosis). In this step, diversity is evaluated by checking the similarity count for each memory antibody. In addition, a part of memory antibodies are induced into the germ-line DNA library (as per Step 6) according to a user-defined inducing rate.

[Step 5] Tournament selection for donor antibodies Emulating to the construction of germ-line DNA libraries in biological immune system, this study uses a tournament selection scheme to select donor antibodies exhibiting higher affinity values to assemble germ-line DNA libraries. According to the predefined tournament size, antibodies/schedules are chosen randomly for competition with the surviving winner being turning into a donor antibody. [Step 6] Germ-line DNA library construction As described in the previous section, the genetic material used to produce antibody molecules is stored in germ-line DNA libraries, each one containing a fragment of an antibody gene. Components from the memory antibodies and the donor antibodies construct the germ-line DNA library. [Step 7] Gene fragment rearrangement In natural immune system, antibodies are produced through a random recombination of selected fragments from a germ-line DNA library. In MMIA, new antibodies/schedules are created via gene fragments rearrangement process. For simplicity, a 3×3 scheduling example is employed to explain the procedure as Fig. 2 illustrates. First, a seed antibody is chosen randomly form the gene library. Then its genes/jobs of the 1st machine located at the 2nd, 3rd, and 4th loci are replaced by the associated genes/jobs (1st-, 3rd-, and 6th-bit) of the donor antibody randomly selected from the gene library. Next, another donor antibody is chosen randomly and its genes/jobs belonging to the 2nd machine take place the corresponding components of the newly generated seed antibody. Similar processes are repeated to create new seed antibody until the genes/jobs of all machines are rearranged. [Step 8] Antibody diversification Matching a large variety of antigens requires an equal level of diversity in antibody type. In the proposed MMIA, this was achieved by mimicking the following six diversification mechanisms found in biological immune systems: Somatic point mutation (Fig. 3), Somatic recombination (Fig. 4), Gene conversion (Fig. 5), gene inversion (Fig. 6), and gene shift (Fig. 7), Nucleotide addition (Fig. 8).

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Fig. 2. Illustration of fragmental rearrangement

Fig. 3. Somatic point mutation illustration

Fig. 5. Gene conversion

Fig. 4. Somatic recombination illustration

Fig. 6. Gene inversion

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Fig. 7. Gene shift

Fig. 8. Nucleotide addition illustration

It should be noted that the six diversification mechanisms described in [Step 8] are selected to implement randomly in the antibody diversity process.

[Step 9] Stopping criterion The whole process will stop when the generation equals to a pre-defined number. Otherwise the process reverts to [Step 2] for iteration. In the final stage, the best and most diverse solutions are stored in the memory pool.

4 Computation Results and Discussion For carrying out the necessary computations and evaluating the performance of the proposed immune algorithm, the program for computing JSSP was developed using C++ language. Table 2 lists the associated parameters adopted in the proposed MMIA for scheduling optimization. These parameters were determined through numerical experiments after multiple simulation runs. Table 2. Immune algorithm parameters for solving JSSP 6x6 10x5 15x5 20x5 30x10 Instance size (n × m) Iteration number 100 500 1000 Antibody population size n× m 2×( n×m) Antibody length 36 50 75 100 200 Diversity probability 0.1 0.1 0.1 (somatic point mutation) Hypermutation rate 0.5 0.5 0.5 Light/heavy chain-length ratio 6:4 5:5 5:5 Number of proliferation 6 8 10 Inducing ratio 0.1 0.2 0.2 Bit number in Gene shift 2-bit 2-bit Bit number of nucleotide random number between 1 to n Tournament size (n×m)/10 ( n×m)/5

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Eighteen benchmark instances from two classes of standard JSSP test problems were employed to illustrate the effectiveness of the proposed algorithm. One problem prefixed by FT is from Fisher & Thompson [17] while the other 17 problems prefixed by LA are from Lawrence [18]. Problem dimensions vary from 6 to 30 jobs and 5 to 10 machines. All test instances can be downloaded from the OR-Library at http://mscmga.ms.ic.ac.uk. Table 3 lists, for each test problem, its dimension (number of jobs and number of machines), name, the best-known solution, and the computation results found by MMIA. It shows that the proposed MMIA discovers optimal and near-optimal solutions (LA19 and LA21) in all instances. As expected, a set of multiple solutions derived for each benchmark instance, respectively. Table 4 lists the schedules derived for the case FT06, respectively. It should be noted that the last three optimal schedules are identical to the results derived by separate researchers in [9, 19-21]. The results show that MMIA is an effective and efficient algorithm for solving JSSP. Table 3. JSSP instances and the computation results derived using MMIA

Instance

Size

FT06

6x6

LA02 LA04 LA06 LA08 LA10 LA13 LA17 LA21

10x5 10x5 15x5 15x5 15x5 20x5 10x10 15x10

Best known MMIA Instance solution LA01 55 55 655 590 926 863 958 1150 784 1046

655 590 926 863 958 1150 784 1088

LA03 LA05 LA07 LA09 LA11 LA15 LA19 LA31

Size 10x5 10x5 10x5 15x5 15x5 20x5 20x5 10x10 30x10

Best known MMIA solution 666 597 593 890 951 1222 1207 842 1784

666 597 593 890 951 1222 1207 857 1784

Table 4. Multiple optimal schedules derived for FT06 problem

Multiple optimal schedules (5 solutions) [3 2 3 1 4 6 2 3 6 2 5 6 4 3 3 2 4 5 1 4 5 6 4 1 2 1 3 5 1 5 4 2 5 6 1 6] [3 2 3 1 4 6 2 3 6 6 1 2 5 4 3 5 3 2 4 5 1 1 5 6 4 1 2 2 5 3 4 5 4 6 1 6] [3 2 3 1 4 6 2 3 6 6 4 2 5 5 3 2 4 5 1 1 5 6 4 1 2 1 3 2 5 3 4 5 4 6 1 6] [3 2 3 1 4 6 2 3 6 6 4 2 5 5 3 2 4 5 1 1 1 6 4 1 2 5 3 2 5 3 4 5 4 6 1 6] [3 2 3 1 4 6 2 3 6 6 4 2 5 5 1 2 4 5 3 1 1 6 4 1 2 5 3 2 5 4 3 5 4 6 1 6]

5 Conclusion In this study, a novel concept for handling multi-modal job-shop scheduling optimization has been presented by using an immune algorithm to imitate the features of a biological immune system. Operation-based representation is adopted to guarantee feasible schedules and the goal is to minimize the makespan of a scheduling. The exploration and exploitation of solutions within a search space are realized through the antibody molecule structure, integration of clonal proliferation, germ-line gene library, gene fragment rearrangement, and memory antibodies, further

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assisted by six diversification schemes. The proposed methodology enhances accuracy and diversity via the procedures of clonal proliferation and schemata recombination implemented through the process of gene fragment rearrangement. Also, niche antibody is utilized to find multiple solutions. The computation results of the 18 benchmark instances demonstrate the effectiveness of the proposed immune algorithm. The capability of discovering multiple schedules illustrates the flexibility and diversity of the proposed MMIA.

References 1. Jain, A.S., Meeran, S.: A State-of-the-Art Review of Job-Shop Scheduling Techniques. European Journal of Operations Research 113, 390–434 (1999) 2. Steinhöfel, K., Albrecht, A., Wong, C.K.: Two Simulated Annealing-Based Heuristics for the Job Shop Scheduling Problem. European Journal of Operational Research 118, 524–548 (1999) 3. Ponnambalam, S.G., Aravindan, P., Rajesh, S.V.: A Tabu Search Algorithm for Job Shop Scheduling. The International Journal of Advanced Manufacturing Technology 16, 765–771 (2000) 4. Blum, C.: An Ant Colony Optimization Algorithm to Tackle Shop Scheduling Problems. Technical Report TR/IRIDIA/2003-01, IRIDIA, Université Libre de Bruxelles, Belgium 5. Wang, L., Zheng, D.-Z.: An Effective Hybrid Optimization Strategy for Job-Shop Scheduling Problems. Computers & Operations Research 28, 585–596 (2001) 6. Xu, X.-D., Li, C.-X.: Research on Immune Genetic Algorithm for Solving the Job-Shop Scheduling Problem. International Journal Advanced Manufacturing Technology DOI 10.1007/s00170-006-0652-x 7. Miyahita, M.: An application of immune algorithms for job-shop scheduling problems. In: Proceedings of the 5th IEEE International Symposium on Assembly and Task Planning, Besancon, France, pp. 146–150 (2003) 8. Coello Coello, C.A., Rivera, D.C., Cortés, N.C.: Use of Artificial Immune System for Job Shop Scheduling. In: Timmis, J., Bentley, P.J., Hart, E. (eds.) ICARIS 2003. LNCS, vol. 2787, pp. 1–10. Springer, Heidelberg (2003) 9. Chandrasekaran, M., Asokan, P., Kumanan, S., Balamurgan, T., Nickolas, S.: Solving Job Shop Scheduling Problems Using Artificial Immune System. International Journal Advanced Manufacturing Technology 31, 580–593 (2006) 10. Zhou, Y., Li, B., Yang, J.: Study on Job Shop Scheduling with Sequence-Dependent Setup Times Using Biological Immune Algorithm. International Journal Advanced Manufacturing Technology 30, 105–111 (2006) 11. Tazawa, I., Koakutsu, S., Hirata, H.: An Immunity Based Genetic Algorithm and its Application to the VLSI Floorplan Design Problem. In: Proceedings of 1996 IEEE International Conference on Evolutionary Computation, pp. 417–421 (1996) 12. Luh, G.-C., Chueh, C.-H., Liu, W-W.: MOIA: Multi-objective immune algorithm. Engineering Optimization. 35, 143–164 (2003) 13. Luh, G.-C., Chueh, C.-H.: Multi-Modal Topological Optimization of Structure Using Immune Algorithm. Computer Methods in Applied Mechanics and Engineering 193, 4035–4055 (2004) 14. Park, L.-J., Park, C.H.: Genetic algorithm for job shop scheduling problems based on two representational schemes. Electronics Letters 31, 2051–2053 (1995)

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15. Gonçalves, J.F., de Magalhães Mendes, J.J., Resende, M.G.C.: A Hybrid Genetic Algorithm for the Job Shop Scheduling Problem. Technical Report, TD-5EAL6J. AT&T Labs (2002) 16. Yamada, T., Nakano, R.: Genetic algorithms for job-shop scheduling problems. In: Proceedings of Modern Heuristic for Decision Support, London, UK, pp. 67–81 (1997) 17. Fisher, H., Thompson, G.L.: Probabilistic Learning Combinations of Local Job-Shop Scheduling Rules. In: Muth, J.F., Thompson, G.L. (eds.) Industrial Scheduling, pp. 225–251. Prentice-Hall, Englewood (1963) 18. Lawrence, S.: Resource constrained project scheduling: an experimental investigation of heuristic scheduling techniques. Technical Report, Carnegie Mellon University, Pittsburgh (1984) 19. Ventresca, M., Ombuki, B.M.: Meta-heuristics for the job shop scheduling problem. Technical report, CS-03-12. Department of Computer Science, Brock University (2003) 20. Yang, S., Wang, D.: A new adaptive neural network and heuristics hybrid approach for job-shop scheduling. Computers & Operations Research 28, 955–971 (2001) 21. Yu, H., Liang, W.: Neural network and genetic algorithm-based hybrid approach to expanded job-shop scheduling. Computers & Industrial Engineering 39, 337–356 (2001)

Simulated Annealing Algorithm for Solving Network Expanded Problem in Wireless ATM Network Der-Rong Din Department of Computer Science and Information Engineering, National ChangHua University of Education, Taiwan, R.O.C. [email protected]

Abstract. In this paper, the network expanded problem (NEP) which optimally assigns new adding and splitting cells in PCS (Personal Communication Service) network to switches in an ATM (Asynchronous Transfer Mode) network is studied. In NEP, the locations of cells (or Base Stations, BSs) in PCS network are fixed and known, but new switches should be installed to ATM network and the topology of the backbone network may be changed. Given some potential sites of new switches, the problem is to determine how many switches should be added to the backbone network, the locations of new switches, the topology of the new backbone network, and the assignments of new adding and splitting cells in the PCS to switches of the new ATM backbone network in an optimal manner. The goal is to do the expansion in an attempt to minimize the total communication cost under budget and capacity constraints. The NEP is modelled as a complex integer programming problem. Since finding an optimal solution to this problem is Impractical. A simulated annealing (SA) algorithm is proposed to solve this problem. In the proposed SA, several heuristics are encoded into the perturbation to generate good solutions. Experimental results indicate that the proposed simulated annealing algorithm can get better performance than heuristic algorithm.

1

Introduction

The rapid worldwide growth of digital wireless communication services motivate a new generation of mobile switching networks (such as wireless ATM[1]) to serve as infrastructure for such services. In the architecture which based on wireless ATM presented in [1], the base stations (BSs or cells) were directly connected to the ATM switches. In the process of designing PCS (Personal Communication Service) network, first, the telephone company determined the global service area(GSA) and divided the GSA into several smaller coverage areas. For each area, a BS was established and connected to a switch of the backbone network to 

This work was supported in part by National Science Council (NSC) of R.O.C. under Grant NSC-95-2221-E-018-012.

H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 1138–1147, 2007. c Springer-Verlag Berlin Heidelberg 2007 

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Fig. 1. Example of the network expanded problem in the two layers wireless ATM network

form a two-level wireless ATM network. This topology may be out of date since more users may joint and use the PCS. Some areas not been covered may have users needed to be served. The service demands of some areas may increase and exceed the capacities provided by the BSs and switches. Though, the wireless ATM system should be expanded so that the PCS can provide better quality of services to users. Two methods can be used to expand the capacities of system: (1) adding new cells to the wireless ATM network so that those non-covered areas can be covered by new cells; (2) reducing the size of the cell so that the capacity of the system can be increased. In practice, this can be achieved by using cell splitting[2] process. The cell splitting process establishes new BSs at specific points in the PCS and reduces the cell size by a factor of 2 (or more). For the given two-level wireless ATM network, cells in PCS are divided into two sets. One is the set of cells which are built originally and assigned to fixed switches on the ATM network. The other is the set of cells which are newly added or established by performing the cell splitting process. Moreover, the locations of all cells in PCS network are fixed and known, but the number of switches in ATM network may be increased. Given some potential sites of new switches, the capacity of the switch, and the designing budget, the problem is to determine the number of switches should be added to the backbone network, the locations of the new switches, the connections between the new switches and original backbone network, and the assignment of new and splitting cells in the PCS to switches in an optimal manner. The goal is to do the expansion in an attempt to minimize the objective cost under budget and capacity constraints. For the cell assignment problem (CAP), Merchant and Sengupta [3] considered the CAP problem. In [4,5], this model of CAP was extended. Moreover, in [6], the extended cell assignment problem (ECAP) has been investigated and formulated. In ECAP, the new adding and the splitting cells were assigned to the switches of the ATM network so that the objective cost can be minimized. In ECAP, the number of new and splitting cells were not greater than the remaining capacities provided by the original ATM network. The objective cost considered in this paper has two components: one is the LU (location update or handoff) cost that involve two switches, and the other is the cost of cabling (or trucking) [3,4,5,6,7]. Assume that the LU costs of intra-switch handoffs involving only one switch are negligible. In this paper, each new or splitting cell is to be connected to only one

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switch. The budget constraint is used to constrain the sum of following costs: (1) the sum of the switch setup cost, (2) the backbone link setup cost between two switches, and (3) the local link setup cost between cells and switches. In this paper, a more complex problem is considered. Following the objective function formulated in [3,4,5,6,7], new cells and new switches should be introduced into the two-level network. In this paper, the locations of new switches, the connections between switches, and the assignments of new and splitting cells should be determined so that the objective cost can be minimized under budget and capacity constraints. This problem is denoted as network expanded problem (NEP) in wireless ATM environment. Obviously, finding an optimal solution for it is impractical due to exponential growth in execution time. Simulated annealing (SA) is a stochastic computational technique derived from statistical mechanics for finding near globally-minimum-cost solutions to large optimization problems. In this paper, a simulated annealing algorithm is developed to find an approximate solution of the NEP problem.

2

Problem Formulation

For the backbone network, assume that: (1) each cell is connected to a switch through a local link , (2) the switches are interconnected with a specified topology through backbone links, (3) the number of cells can be handled by a new switch cannot exceed CAP , (4) at most one switch can be installed at a given potential site, (5) all links of the current backbone network are kept in place, (6) a switch site in the current network is also a switch site in the expanded network, and (7) the backbone network topologies are preserved in the expanded backbone network. Moreover, assume the information described below are fixed and known: (1) the location of the new cells and the handoff frequency between cells, (2) the potential switch sites, (3) the setup cost of switch at a particular site, (4) the local link setup cost between cells and switches, and (5) the backbone link setup cost between switches. The goal is to find the minimum-cost expanded network subject to all of the above assumption, facts and constraints (described later). Let CG(C, L) be the PCS network, where C is a finite set of cells and L is the set of edges such that L ⊆ C × C. Assume C new ∪ C old = C, C new ∩ C old =∅, new C new be the set of new and splitting cells where |C | = n , and Cold be the set old of original cells where |C | = n. Without loss of generality, cells in C old and C new are numbered from 1 to n and n + 1 to n + n , respectively. If cells ci and cj in C are assigned to different switches, then an inter-switch handoff cost is incurred. Let fij be the frequency of handoff per unit time that occurs between cells ci and cj . All edges in C are undirected and weighted; cells ci and cj in C are connected by an edge (ci , cj ) ∈ L with weight wij , where wij =fij +fji , wij = wji , and wii = 0[4,5]. Let Gold (S old , E old ) be the currently exist ATM network, where S old is the set of switches with |S old | = m, E old ⊆ S old × S old is the set of edges, sk , sl in S old , (sk , sl ) in E old , and Gold is connected. The topology of the ATM network Gold (S old , E old ) will be expanded to G(S, E). Let S new be the set of potential sites of switches. Without loss of

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generality, switches in S old and S new are indexed from 1 to m and m+1 to m+m , respectively. Let (Xsk , Ysk ) be the coordinate of switch sk , sk ∈ S old ∪S new , k=1, 2, ..., m + m , (Xci , Yci ) be the coordinate of cell ci , i=1, 2, ..., n + n ; and dkl be the minimal communication cost between the switches sk and sl on network G(S, E), sk , sl ∈ S; k, l=1, 2, ..., m + m . The total communication cost has two components, the first is the cabling cost between cells and switches, and the other is the handoff cost which occurred between two switches. Let lik be the cabling cost per unit time between cell ci switch sk , (i=1, 2, ..., n + n ; k=1, 2, ..., m + m ) and assume lik is the function of Euclidean distance between cell ci and switch sk . Assume the number of calls that can be handled by each cell per unit time is equal to 1 and CAP denotes the cell handling capacity of each new switch sk ∈ S new , (k = m + 1, m + 2, ..., m+ m ). Let Capk be the number of remaining cells that can be used to assigned cells to switch sk ∈ S old ,(k=1, 2, ..., m). Let qk =1, (k = 1, 2, ..., m + m ) if there is a switch installed on site sk ; qk =0, otherwise (as known, qk =1, for k= 1, 2, ..., m). Let setupk be the setup cost of the switch at site sk ∈ S, k=1, 2, ..., m + m (as known setupk = 0, for k=1, 2, ..., m). Let xik = 1 if cell ci is assigned to switch sk ; xik = 0, otherwise; where ci ∈ C, i= 1, 2, ..., n + n , sk ∈ S, s=1, 2, ..., m + m . Since each cell should be assigned to one switch, the m+m constraint k=1 xik = 1, for i=1, 2, ..., n + n should be satisfied. Further, the constraints on the call handling capacity is that: for the new switch sk , n+n  i=n+1 xik ≤ CAP, k = m + 1, m + 2, ..., m + m , and for the existing switch n+n sk , i=n+1 xik ≤ Capk , k = 1, 2, ..., m. If cells ci and cj are assigned to different switches, then an inter-switch handoff cost is incurred. To formulate handoff cost, let yij take a value of 1, if both cells ci and cj are connected to a common switch; yij = 0, otherwise. The cost of handoffs per unit time is given by Handof f Cost =

    n+n  n+n  m+m  m+m 

i=1

j=1

k=1

wij (1 − yij )qk ql xik xjl Dkl ,

(1)

l=1

where Dkl is the minimal communication cost between switches sk and sl on G(S, E). The objective function is : Minimize T otal cost = Cabling Cost + α × Handof f Cost =

  n+n  m+m 

i=1

k=1

lik xik + α

    n+n  n+n  m+m  m+m 

i=1 j=1

k=1

(2)

wij (1 − yij )qk ql xik xjl Dkl ,

l=1

where α is the ratio of the cost between cabling communication cost and interswitch handoff cost. Let ekl be the variable that represents the link status between two switches sk and sl . If ekl =1 then there is a link between two switches sk and sl (sk , sl ∈ S old ∪ S new ); ekl =0, otherwise. Let uik be link setup cost of constructing the connection between cell ci ,(i = n + 1, n + 2, ..., n + n ) and switch sk (k=1, 2, ..., m + m ), and assume uik is the function of Euclidean distance between cell ci and switch sk . Let vkl be link setup cost of constructing the

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Fig. 2. (a) Configuration of the SA, (b) SLCC, (c) Configuration used to represent the relation between the locations of switches and the assignments of cells

connection between switch sk and switch sl ,(k, l=1, 2, ..., m + m ), and assume vkl is the function of Euclidean distance between switch sk and switch sl . Define old eold ; eold kl =1 if there is a backbone link in G kl =0, otherwise for k, l =1, 2, ...,m. The following constraints must be satisfied: EC =

 m+m 

qk setupk +

  m+m  m+m 

uik xik qk

(3)

i=n+1 k=1

k=m+1

+(

  n+n  m+m 

(ekl − eold kl )vkl qk ql )/2 ≤ Budget

k=m+1 l=1 

xik ≤ qk , f or k = 1, 2, ..., m + m . ekl ≤ qk and

3 3.1

(4) 



ekl ≤ ql , f or k = 1, 2, ..., m ; l = 1, 2, ..., m + m . (5)

Simulated Annealing Algorithm for NEP Configuration Space

The object of NEP problem is to find the location of the new switch, the topology of backbone network, and the assigning switch of cell, so that the objective function is minimized under the capacity and budget constraints. To do this, the configuration space is designed to be the set of possible solutions. To solve the network expanded problem in wireless ATM network, three types of configurations, known as switch location and connection configuration (SLCC), backboneaugment configuration (BAC), and cell-assignment configuration (CAC) are introduced and illustrated in Fig. 2(a). The switch location and connection configuration in the form of nonnegative integer decides the activation or deactivation of the new switch and the connection of the corresponding new switches. The number of switches with positive integer in the SLCC is assumed to be equal to m (|C new | − k=1 Capk )/CAP , that is, base on the assumption that the cost of setup a new switch is more expensive than the cost of establishing several backbone links. The SLCC can also be used to represent the backbone links between new switches and other (new or old) switches. To make the design of backbone network simply, the links and switches selected in SLCC together with

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the network Gold will form a base connected backbone network GB . The network GB can be improved by adding backbone links described in backbone-augment configuration (BAC) if the budget not yet been use up. The cell-assignment configuration (CAC) defines the assigning switches of cells. The new cell can only be assigned to old switches with unused capacity or the new switch whose value in SLCC is greater than zero. The detail information of three types of configurations are described as follows: (1) Switch location and connection configuration (SLCC): Since there are m potential sites for the choice of news switches, a nonnegative integer encoding method is used to represent whether the site is selected or not. An integral array SLCC[m + 1, ..., m + m ] is used to represent the choice. If SLCC[k] > 0 (m + 1 ≤ k ≤ m + m ) then a new switch is located at potential site sk ; SLCC[k] = 0, otherwise. Moreover, the content of the element in SLCC is used to represent the connections between switches. Since the existing ATM network is connected. Thus, to form a new connected backbone network, the only thing should be done is to keep the information of how the new switch is connected to another switches. The value of SLCC[m + 1, ..., m + m ] is used to represent the connections of new switches to another switches. If SLCC[k] = l(m + 1 ≤ k ≤ m + m , 1 ≤ l ≤ m + m ) then there is a backbone link between switches sk and sl . (2) Backbone-augment configuration (BAC): The network GB formed by Gold and the link described in SLCC can be improved by adding backbone links to the backbone-augment configuration (BAC) if the budget not yet been use up. The implementation of the BAC is a linked list, which consists of nodes used to represent the link of backbone link, is used. (3)Cell-assignment configuration (CAC): A coding scheme that uses positive integer numbers is used to represent the assigning switches of cells. The cell–assignment configuration is shown in Fig. 2(c), where the ith cell belongs to the CAC[i]-th switch. It should be noticed that, the cell-assignment configuration can be divided into two sets, the first set of cells which represents the assignment of cells in C old is fixed in running of SA. Thus, the first set of cells can be ignored since it is unchanged during experiments. 3.2

Initial Configuration Generation

Since the content of SLCC does effect the contents of the BAC  and CAC. The number of switches to be selected is equal to (|C new | − m k=1 Capk )/CAP and the switch is randomly selected. Once the location of new switches have been selected, the connections of switches should be updated according to the selection of new switches. To do this, let switch-pool for connection (SPC) be the set of numbers indicated switches which will be used to determine SLCC. Thus SP C = {1, 2..., m} ∪{i | SLCC[i] > 0, m + 1 ≤ i ≤ m + m }. To generate the connection of new switches, the value of element of array SLCC is randomly selected from SP C. This process will guarantee that each new switch will be connected to an exist switch. Similarly, the assignments of cells to switches should be updated according to the selection of new switches. Let switch-pool for assignment (SPA) be the set of numbers indicated switches which can be

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used in choosing of SLCC. For switch sk , 1 ≤ k ≤ m, Capk “k” are inserted into set SP A. For switch sk , if m + 1 ≤ k ≤ m + m , if SLCC[k] > 0, CAP “k” are inserted into set SP A. Note that, the SPC should be updated if the a new switch is selected. To assign cells to switches, the value of element in array CAC is randomly selected a number from SP A and removed it from SP A. After performing the cell assignment, if the budget not yet been use up the link which connected two switches in SP C is randomly selected to argument the backbone network. The objective cost defined in (2) is used as the cost function. In the proposed SA, only the feasible solution can be generated, thus there is no need for designing the penalty function. Only generating feasible solutions can expedite the converge and improve the performance of the proposed SA. 3.3

Perturbation Mechanism

Simulated annealing algorithm uses perturbation to change the configuration from one state to another state. After perturbation, the resulted configuration still a constraint-satisfied one after performing perturbation. In the following, several types of perturbations are introduced into the SA algorithm for solving the NEP problem. (1) P1: Cell exchanging perturbation (CEP): Random selected two cells in C new , say ci and cj which assigned to switch sk and sl , respectively. The assignment of these cells are exchanged, that is, ci assigned to sl and cj assigned to sk . (2) P2: Switch exchange perturbation (SEP): Random selected two switches sk and sl in S new , all cells assigned to these switches are exchanged. (3) P3: Switch migrating perturbation (SMP): The perturbation randomly selected switch sk in SLCC with positive value and change it to 0 and another switch sl (k = l) from “0” to the original value of SLCC[k]. All cells assigned to switch sk are reassigned to sl , all backbone links which connected to sk should be deleted and new backbone links connected to sl should be established. (4) P4: Backbone link perturbation (BLP): Randomly selected a backbone link in current backbone remove it and random selected a new backbone link to added. Let pi be the probability of transforming current configuration to a new one by 4 applying the perturbation Pi, i=1, 2, ..., 4, respectively. Assume that Σi=1 pi = 1. i Let AP0 = 0 and APi = Σj=1 pj be the accumulated probability of pi , i=1, 2, ..., 4. 3.4

Cooling Schedule

The parameters of the SA are described as follows. (1) Decrement of the control parameter : The decrement rule in SA is defined as follows: Tk+1 = γTk , where γ is empirically determined and discussed in Section 4.(2) The final value of the control parameter : The iterative procedure is terminated when there is no significant improvement in the solution after a pre-specified number of iterations. (3) The length of Markov Chains: In this paper, the chain length is empirically determined and discussed in Section 4.

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Simulated Annealing Algorithm of NEP Problem

The details of the simulated annealing is described as follows: Algorithm: Simulated Annealing Step 1. For a given initial temperature T , perform initial configuration generation algorithm to generate initial configuration IC . The currently best configuration (CBC) is IC, i.e. CBC = IC, and the current temperature value (CT ) is T , i.e. CT = T . Determine i pj , i=1,2,...,12. pi , i = 1, 2, ..., 10, AP0 = 0 and APi = Σj=1 Step 2. If CT = 0 or the stop criterion is satisfied then go to Step 7. Step 3. Generate a random number p in [0, 1), if APi−1 ≤ p ≤ APi , (i = 1, 2, ..., 4) then new configuration (N C) is generated by applying the P i perturbation schema. Step 4. The difference of the costs of the two configurations, CBC and N C is computed, i.e. ΔC = E(CBC) − E(N C). Step 5. If ΔC ≥ 0 then the new configuration N C becomes the currently best configuration, i.e. CBC = N C. Otherwise, if e−(ΔC/CT ) > random[0, 1), the new configuration N C becomes the currently best configuration, i.e. CBC = N C. Otherwise, go to Step 2. Step 6. The cooling schedule is applied, in order to calculate the new current temperature value CT and go to Step 1. Step 7. End.

4

Experimental Results

In order to evaluate the performance of the proposed algorithm, the simulated annealing algorithm is implemented and applied to solve problems that were randomly generated. The results of these experiments are reported below. In all the experiments, the implementation language was C ++ , and all experiments were run on a Windows XP with a Pentium IV 3.0Ghz CPU and 512MB RAM. A hexagonal system in which the cells were configured as an H-mesh is simulated. The handoff frequency fij for each border was generated from a normal random number with mean 100 and variance 20. The CAP of new switch is set to be 5, the potential sites of the new switch are assumed be the same as the locations of the switches in S. The GSA is assumed in the 2-D plane, with width and length equal to 1000 (unit). The cabling cost of the local link is equal to the distance between cell and switch multiplied by 1 and the cabling cost of the backbone link is equal to the distance between switches multiplied by 10. The setup cost of the each switch is assumed to be equal to 100. Assume |C old |=50, |C new |=50. Total budget is assumed to be 6000. The backbone network is randomly generated with 100 backbone links. Assume |S old |=12, α=0.001, the cell assignment of the set of cells C old to switches S old is determined be the simulated annealing algorithm proposed in [4]. After performing cell assignment, the remaining capacity of each switch can be obtained. In this section, first, the effects of the various parameters of each simulated annealing algorithm are discussed. All simulations are run ten times and the average result is presented. (1) Effect of the chain length: In Fig. 3(a), the average result of 10 runs with the different chain length is presented. In this experiment, assume chain length is in {10x|1 ≤ x ≤ 10 ∧ x ∈ N }. It can be found that the algorithm can get better result when the chain length is set to 60. The CPU time in seconds also showed in Fig. 3(b). (2) Effect of accepted ratio: In Fig. 3(c), the average result of 10 runs with the different values of accepted ratio is presented. In this experiment, assume accepted ratio (γ) is in {0.05x|13 ≤ x ≤ 20 ∧ x ∈ N }.

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Fig. 3. Results of the network expanded problem in different values of parameter

Fig. 4. Results of the network expanded problem in different values of Budget

It can be found that the algorithm can get best result for (γ=0.95). The CPU time in seconds also showed in Fig. 3(d). Effect of budget: In Fig. 4(a), the effect of the budget is presented. In this experiment, assume Budget is in {4000, 5000, 6000, 7000, 8000}. It can be found that as the budget increase, the total cost of the NEP can be reduced. The CPU time in seconds also showed in Fig. 4(b). The “min” in Fig. 4(b) be the cost obtained by designing backbone network as a complete graph. The performance of the proposed simulated annealing algorithm is examined and compared here. Let |C old |=50, |C new |= 50, 100, or 150, |S old | = 12, the value of CAP is in {5, 6, 7, 8, 9, 10}. α = 0.001, the best results of the simulated annealing algorithm after running 10 times are compared with the greedy Table 1. Comparison of SA and HA |C old | |C new | CAP 50 150 5 50 100 5 50 50 5 50 150 6 50 100 6 50 50 6 50 150 7 50 100 7 50 50 7 50 150 8 50 100 8 50 50 8 50 150 9 50 100 9 50 50 9 50 150 10 50 100 10 50 50 10

HA Cost SA Cost ratio = SA/HA × 100% HA Time SA Time 66575 56157 84.35% 611.08 152.5 53524 45052 84.17% 190.54 102.8 58125 41526 71.44% 7.87 51.3 66179 55231 83.46% 611.08 152.5 52600 44188 84.01% 190.54 107.8 56759 43361 76.39% 7.03 51.2 65471 54537 83.30% 611.08 152.5 51689 43493 84.14% 190.54 105.7 55020 45312 82.36% 5.78 51.3 65100 53736 82.54% 611.08 152.5 51554 42984 83.38% 190.54 107.8 54886 44167 80.47% 4.28 51.8 64150 53551 83.48% 611.08 152.5 50865 42834 84.21% 190.54 105.7 41977 33288 79.30% 3.6 51.1 63266 52656 83.23% 611.08 152.5 50577 42765 84.55% 190.54 105.7 38182 32548 85.24% 2.21 51.5

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heuristic algorithm HA[8]. The results were shown in Table. 1. It is easy to find that the SA gets better results then HA.

5

Conclusions

In this paper, the network expanded problem (NEP) which optimally assigns new and splitting cells in PCS network to switches on an ATM network is investigated. This problem is currently faced by designers of mobile communication service and in the future, it is likely to be faced by designers of PCS. Since finding an optimal solution of the NEP is NP-hard, a stochastic search method based on the simulated annealing approach is proposed to solve it. Simulation results showed that simulated annealing algorithm is robust for this problem. Experimental results indicate that the algorithm can run efficiently.

References 1. Cheng, M., Rajagopalan, S., Chang, L.F., Pollini, G.P., Barton, M.: PCS mobility support over fixed ATM networks. IEEE Communication Magazine 35(11), 82–91 (1997) 2. Rappaport, T.S.: Cellular radio and personal communications, vol. 1. IEEE Press, New York (1995) 3. Merchant, A., Sengupta, B.: Assignment of cells to switches in PCS networks. IEEE/ACM Trans. on Networking 3(5), 521–526 (1995) 4. Din, D.R., Tseng, S.S.: Simulated annealing algorithms for optimal design of two– level wireless ATM network. Proceeding of NSC 25(3), 151–162 (2001) 5. Din, D.R., Tseng, S.S.: Heuristic algorithm for optimal design of two-level wireless ATM network. Journal of Information Science Engineering 17(4), 665–674 (2001) 6. Din, D.R., Tseng, S.S.: Heuristic and simulated annealing algorithms for solving extended cell assignment problem in wireless ATM network. International Journal of Communication Systems 15(1), 47–65 (2002) 7. Din, D.R., Tseng, S.S.: A solution model for optimal design of two-level wireless ATM network. IEICE Transactions on Communications, IEICE Trans. Commun., E85-B(8), 1533–1541 (2002) 8. Din, D.R.: A heuristic algorithm for the network expanded problem on wireless ATM environment. Technical Report, NCUE-2004-04-01, Taiwan, R. O. C

An Intrusion Detection Based on Support Vector Machines with a Voting Weight Schema Rung-Ching Chen and Su-Ping Chen Department of Information Management, Chaoyang University of Technology, 168, Jifgong E. Rd., Wufong Township, Taichung County, 41349, Taiwan, R.O.C. {crching,s9414638}@cyut.edu.tw

Abstract. Though IDS (Intrusion Detection System) have been used for many years, the large number of returned alert messages leads to management inefficiencies. In this paper, we propose a novel method based on SVM (Support Vector Machines) with a voting weight schema to detect intrusion. First, TF (Term Frequency), TF-IDF (Term FrequencyInverse Document Frequency) and entropy features are extracted from processes. Next, these three features are sent to the SVM model for learning and then for testing. We then use a general voting schema and a voting weight schema to test attack detection rate, false positive rate and accuracy. Preliminary results show the SVM with a voting weight schema combines low the false positive rates and high accuracy. Keywords: Intrusion Detection System, TF-IDF, Entropy, Support Vector Machine.

1

Introduction

The Internet plays an increasingly important role in communications in modern society. However, attacks on network security are a common problem, and network security is critical for many organizations. Firewalls, widely used, are a good way to prevent attacks, but cannot monitor packets in real time on the network. Intrusion detection, which monitors the network in real time, has been studied by many researchers. However, Intrusion Detection Systems (IDS) generally produce high false positive rates, making them unattractive to information technology managers [13, 18, 4]. The major difference between firewall and IDS system is that firewall is a manual, passive defense system. By contrast, the IDS collects packets online from the network. After collecting them, the IDS will monitor and analyze these packets, acting as the ”second line of defense”. It provides the results of its detection analysis, either attacks or normal behaviors, to managers. An ideal IDS system has a 100% attack detection rate along with a 0% false positive rate, but attaining those goals is difficult. Detecting illegal behaviors on the host or network is the major object of IDS. The IDS monitors the network and sends a warning message to the managers if it detects an attack. H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 1148–1157, 2007. c Springer-Verlag Berlin Heidelberg 2007 

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Briefly, the aim of intrusion detection is to identify malicious attacks. There are two main methods of identifying intrusion detection in IDS systems: misuse (signature-based) and anomaly (behavior-based) [20]. Misuse detection establishes a pattern or a signature form so that similar attacks can be detected. It may be used in personal computers, like an antivirus program. The main drawback of misuse detection is it cannot detect new attacks, though new forms of attack are constantly being generated. Misuse detection is based on patterns stored in a database that contains possible signature of attacks. If the system matches the data with the attack pattern, the IDS regards it as an attack. Consequently, misuse detection provides a lower false positive rate. Currently, misuse detection systems such as Snort, developed by Marty Roesch [14], are being produced for network security. Anomaly detection is a behavior-based method based on statistical theory that establishes a normal activity profile for system detection. Anomaly detection can detect new attacks, but has higher false positive rates. Behavior-based methods focus on later attacks, and ignore earlier attacks. In general, the majority of attacks will be detected in the later period, and the greatest damage will occur in the later period as well. Thus, anomaly detection not only detects attacks but also prevent intrusions. Entropy has been used in intrusion detection for a long time. B. Balajinath et al. used entropy in a learning behavior model of intrusion detection in 2001 [2]. TF-IDF has often been applied to IDS, too. For example, Wun-Hwa Chen et al. compared SVM to ANN for intrusion detection using a TF-IDF-based method [22]. Previously, researchers commonly used ANN for intrusion detection [7, 16] but in recent years SVM has been increasingly applied in intrusion detection [22, 21]. According to experimental results, in most cases SVM has outperforms the ANN method. In this paper, we propose a classification method based on SVM with a voting weight schema to detect intrusion [17]. The operations of the SVM include two phases: a learning phase and a testing phase. In the learning phase, three SVM learning models are constructed based on TF, TF-IDF and entropy features. In the testing phase, five types of SVM models, TF feature based SVM, TF-IDF feature based SVM, Entropy based SVM, voting SVM and voting weight SVM are tested. The remainder of the paper is organized as follows. We give a system overview in section 2. Section 3 introduces the support vector machine and the features of the VW-SVM. In Section 4, we present details of voting policy. Experimental results are given in section 5. Conclusions are presented in section 6.

2

The System Overview

Our framework is shown in Figure 1. First, KDD Cup 1999 Data is transferred to a matrix [15]. Then the system extracts TF, TF-IDF and entropy features

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from the matrix. Three SVM models are then constructed to detect intrusions. Finally, a VW-SVM method is utilized to detect intrusion. It is briefly described as follows: (1) Preprocessing: A process can be composed of many system calls. A system call is a text record [10]. In this phase, useless data will be filtered and modified. For example, it is necessary to convert some text items into numbers. (2) Features selection: The system regards the processes and system calls as a two dimensional matrix. The system maps system calls to keywords and processes to documents, so the TF and TF-IDF values can be obtained from the matrix. The system also calculates the entropy of each process from the matrix. (3) SVM classification: The system uses TF, TF-IDF and entropy features to train the SVM models. The three SVM category models will then be utilized to predict the category of a given process. (4) Voting weight policy: After the three SVM models classify the process, the output of each SVM will be utilized for a hybrid system using voting weight to determine which category the process should be placed in.

Fig. 1. The framework of VW-SVM

3

The Input Features of the SVM

In our system, the processes and system calls are regarded as a two dimensional matrix. System calls are mapped to keywords and processes are mapped to documents. Thus, the TF and TF-IDF values for the features can be obtained from the matrix. In this section, we will introduce the operations of the SVM and how the TF, TF-IDF and entropy feature values are extracted.

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Support Vector Machine

The SVM uses a high dimension space to find a hyper plane in order to perform binary deviation, where the error rate is minimal [21]. The SVM can handle the problem of linear inseparability. The SVM uses a portion of the data to train the system and finds several support vectors that represent training data. These support vectors will be formed into a model by the SVM, representing a category. According this model, the SVM will classify a given unknown document by the following classification decision formula: (xi , yi ), . . . , (xn , yn ), x ∈ Rm , y ∈ {+1, −1}

(1)

Where (xi ,yi ), . . . ,(xn ,yn ) are training samples, n is the number of samples, m is the input dimension, and y belongs to the category of +1 or -1 respectively. In a linear problem, a hyper plane is divided into two categories. Figure 2 shows a high dimension space divided into two categories by a hyper plane. The hyper plane formula is: (w • x)+b=0

Fig. 2. The hyper-plane of SVM

The classification formula is: (w • xi ) + b > 0 if yi = +1

(w • xi ) + b < 0 if yi = −1

(2)

However, for many problems it is not easy to find a hyper plane to classify the data. The SVM has several kernel functions that users can apply to solve different problems. Selecting the appropriate kernel function can solve the problem of linear inseparability.

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3.2

TF and TF-IDF Feature

In general, the importance of system calls is determined by two factors in the collected processes. The first factor is the relative frequency of the appearance of the system calls in the processes, called Term Frequency (TF). We used the TF features for SVM training samples. The second factor is the quantity of processes containing the system call, or Document Frequency (DF). TF shows the relative frequency of a given system call appearing in processes. If the appearance frequency of a system call in the process is higher than in other processes, it means that this system call is able to represent the current process better than other processes. We can thus use the product of TF and Inverse Document Frequency (TF-IDF) to represent the importance of a system call in the process [19, 10, 24, 8]. The formula for calculating TF-IDF for our system is: wi = log(

N fi ) ×  2 ni i fi

(3)

wi : The component of the process vector corresponding to system call i ni : The number times of system call i appear in the whole data set fi : The number times of system call i appears in the process N : The number of processes appears in the whole data set 3.3

Entropy Feature

In recent years, entropy has been utilized in intrusion detection research [2, 6]. Shannon proposed that entropy is related to the uniform distribution [3, 11]. Entropy values represent whether the information is sparse or not, and the probability of terms. Here, entropy is applied to calculating the weight of processes. By the definition, the entropy can be expressed as follows. PT = −

n 

si log si

(4)

i=1

si is the probability of Uniform Distribution of the system calls i in process T, while n is the number of system calls. Each process has an entropy value.

4

The Voting Policy

The original concept of multiple recognizers selects greater than one recognizer to perform the classification. One classification result is determined by these recognizers. In our system, only SVM is used as the classification tool. The idea is to choose different features to construct SVM models because different features have different characteristics. In the hybrid system, many different kinds of pattern classifiers have their own strengths and weaknesses. The most direct form of voting is when all the individual recognizers must agree; otherwise the pattern is rejected. However, this condition is too strict. More common operators

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and less strict forms of voting are more useful where substitution errors are not lethal [1,17]. The majority voting rule states as follows:  1 ek (x) = i, i ∈ Λ Tk (x ∈ Ci ) = (5) 0 Otherwise  1 if TE (x ∈ Ci ) > K 2 E(x) = (6) −1 Otherwise where TE (x ∈ Ci ) =

K 

(x ∈ Ci ), i = 1, . . . , M

(7)

k=1

The combined interpretation of equations 5 and 6 is that the pattern is classified as class Ci if over half of the recognizers say pattern x ∈ Ci . ek (x) is the output of SVM model and Λ is the range of normal. In our system, the value of M is 2, and a given process is classified as either normal or as an intrusion. In this case, each recognizer has the same weight in deciding the results. The output value of each SVM is either +1 or 0, meaning that when the value of TE (x) is greater than half of the number of SVM models, the decision is that the process is a normal operation. Because the precision of the different SVM models varies, we use different voting weights in each of the different SVM models when making voting decisions. We modify the equations 6 and 7 as follows.  1 if f (TE (x ∈ Ci )) > ΘT E(x) = (8) −1 Otherwise where TE (x ∈ Ci ) =

K 

Wk × Tk (x ∈ Ci ),

i = 1, . . . , M

(9)

k=1

Wk is the weight of a SVM model k and the value of K is 3 (which are TF SVM, TF-IDF SVM and entropy SVM) in our system. However, the formula only considers the positive value outcome of a vote, but in many cases, the vote results in a negative value, also an important attribute. Thus, we let the Tk value be 1 or -1, replacing ”0” with ”-1”. Even in the SVM model, the output values of the weight for positive 0 and negative 1 are different, as we found in our experiments. So we modified formula 5 as follows:  1 ek (x) = i, i ∈ Λ Tk (x ∈ Ci ) = (10) −1 Otherwise  1 if f (TE (x ∈ Ci )) > ΘT E(x) = (11) −1 Otherwise where TEk (x ∈ Ci ) = Wks × Tk (x ∈ Ci ),

k = 1, . . . , M ; S ∈ {−1, 1}

(12)

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f (TE (x ∈ Ci )) = α × TE1 + β × TE2 + γ × TE3

(13)

Wk1 ,

but if Tk is -1, the If the value of Tk is 1, the system selects weight value system selects a weight value Wk−1 for system operations. M is the number of SVM models. In the system, the value of M is 3. The function f summarizes the voting weights (α,β,γ), multiplexing the weighting output of the SVM models. ΘT is a threshold for detecting the output of the intrusion system.

5

Experiment and Discussion

The experiments were implemented on a Windows XP Professional operating system using the Pentium-4 2.4G MHz of computer with 512 MB RAM in Java language. The data were collected from the KDD Cup 1999 Data, originally from the 1998 DARPA Intrusion Detection Evaluation Program [15]. This dataset was used in the third international knowledge discovery and data mining tools competition. The data include ”bad” connections and ”good” connections. In ”bad” connections, attacks fall into four main categories: (1) (2) (3) (4)

DOS: denial-of-service R2L: unauthorized access from a remote machine U2R: unauthorized access to root privileges probing: surveillance and other probing

60,000 processes used as training data, while 60,094 processes were used for testing data. The system has 41 system calls, whose frequency in this dataset has been presented and calculated. Libsvm was used as our classification tool [12]. The experimental materials and results output to a website [23]. Three important formulas are utilized to evaluate the performance of the system [24], the attack detection rate, false positive rate and system accuracy listed as follows. Attack Detection Rate =

FPR =

T otal number of detected attacks × 100% T otal number of attacks

T otal number of misclassif ied processes × 100% T otal number of normal processes

Accuracy =

T otal number of corrected detected × 100% T otal number of processes

(14)

(15) (16)

While the system finds the TF, TF-IDF and entropy feature values, the system uses them to train three SVM models. Before running the SVM, we used a scaling step to reduce the range of the input data. Scaling increases the accuracy of SVM [9] by avoiding a large range of for the input numbers, and reducing calculation complexity. The kernel values are determined by inner feature vectors. In the experiment, the kernel used was RBF. Table 1 shows the number

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of testing results using TF-SVM, TF-IDF SVM, Entropy SVM, a general voting schema (Voting-SVM) and a voting weight schema(VW-SVM). In a general voting schema, if more than two SVM models present the same decision, the system chooses this result for the final output of the Voting-SVM. The voting weight approach classifies a process into a given category after calculating the weight. In the experiment, the three models (TF, TF-IDF and entropy features) in turn yielded three types of classification results. Based on the results of the three models, we determined the classification strength of the feature. Because judgments may not be consistent, a voting weight schema determines the category of processes [16]. The threshold of T here we set as -0.18. Table 2 illustrates each parameter value in the experiment. The weight w upper index ”1” means normal behavior, a ”-1” means abnormal behavior. For example in TF-SVM, is set to 0.4; is equal to 0.3 but is equal to 0.7. The two weight values in TF-SVM are different. Though we focus on the false positive rate of TF in Table 3, it has the better performance. As a result we increased the weight value of . Table 1. Output of testing data Method

Attacks Discovered TF-IDF-SVM 52293 TF-SVM 51876 Entropy-SVM 52396 Voting-SVM 53423 VW-SVM 52983

Real 53649 53649 53649 53649 53649

Normal sessions Misclassified 504 150 2213 935 336

Real 6445 6445 6445 6445 6445

Table 2. Parameter table SVM model Voting Weight TF-IDF-SVM β = 0.3 TF-SVM α = 0.4 Entropy-SVM γ = 0.3

SVM output weight W21 = 0.5, W2−1 = 0.5 W11 = 0.3, W1−1 = 0.7 W31 = 0.5, W1−1 = 0.5

Table 3 summarizes the testing results. They include attack detection rate, false positive rate, and accuracy. The attack detection rate of the TF-IDF SVM is 97.47%, while that of the entropy-SVM is 97.66%. The TF-SVM has an attack detection rate of 96.70%. However, when we use the Voting-SVM framework, the Attack Detection Rate rises to 99.58%. The accuracy of the TF-IDF-SVM is 96.91% that of the Entropy-SVM is 96.80%, while the TF-SVM is 94.23%. However, when we use the VW-SVM framework, the total accuracy rises to 98.35%. Further, the TF-SVM has a 2.3% false positive rate, while the VotingSVM method has a 14.50% false positive rate. The Voting-SVM has the best attack detection rate, but its false positive rate is poor by comparison with other methods.

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When the experiment uses TF-SVM to learn and test, performs well on false positives but its accuracy is poor. The TF feature calculates the relative frequency of system calls that appear in the processes. The TF-IDF adds one property to its analysis: system calls are the most important feature of the processes. The Entropy-SVM model performs poorly, generating unacceptable numbers of false positives. When we train the Entropy-SVM, each process only uses a entropy value to represent the process. If the weight method is not used, Voting-SVM also performs poorly, generating unacceptable numbers of false positives. From the overall view, the VW-SVM has the best false positive rate and accuracy. In addition, its attack detection rate is close to that of the Voting SVM. The voting weight is thus the best choice for intrusion detection among the systems studied in this research. Table 3. Performance of the five methods in detecting attacks Method Attack TF-IDF-SVM TF-SVM Entropy-SVM Voting-SVM VW-SVM

6

Detection Rate False Positive Rate Accuracy 97.47% 7.8% 96.91% 96.70% 2.3% 94.23% 97.66% 34.33% 96.80% 99.58% 14.50% 98.06% 98.76% 5.21% 98.35%

Conclusions

In this paper, we have proposed an intrusion detection method using an SVM based on a voting weight schema, whose feature vectors include the TF, TF-IDF and entropy of the processes. The TF-IDF represents common relations between system calls and processes, while the entropy values represent the distribution of system calls in a given process. The TF-SVM, TF-IDF-SVM, entropy-SVM, Voting-SVM and VW-SVM were then compared. The VW-SVM results have higher accuracy than the other methods. In addition, its attack detection rate is nearly as high as that of Voting SVM, while its false positive rate is nearly as low as that of TF-IDF-SVM. Based on these considerations, the voting weight method is the best choice for the intrusion detection. In future research, we will incorporate domain ontology into the IDS to improve its accuracy.

References 1. Pandya, A.S., Macy, R.B.: Pattern Recognition with Neural Networks in C++. IEEE Press, New York (1995) 2. Balajinath, B., Raghavan, S.V.: Intrusion detection through learning behavior model. Computer Communications 24, 1202–1212 (2001) 3. Shannon, C.: Bell System Tech. J. (1948)

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4. Denning, D.E.: An intrusion-detection model. IEEE Trans. Software Eng. SE-13, 222–232 (1987) 5. Naiman, D.Q.: Statistical anomaly detection via httpd data analysis. Computational Statistics & Data Analysis 45, 51–67 (2004) 6. Yeung, D.Y., Ding, Y.: Host-based intrusion detection using dynamic and static behavioral models. Pattern Recognition 36, 229–243 (2003) 7. Giacinto, G., Roli, F., Didaci, L.: Fusion of multiple classifiers for intrusion detection in computer networks. Pattern Recognition 24, 1795–1803 (2003) 8. Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Information Processing & Management, pp. 513–523 (1988) 9. Guide of LIBSVM.: http://www.csie.ntu.edu.tw/∼ cjlin/papers/guide/guide.pdf 10. Feng, H., Kolesnikov, O., Fogla, P., Lee, W., Gong, W.: Anomaly detection using call stack information. In: Proceedings of the 2003 IEEE Symposium on Security and Privacy, p. 62. IEEE Computer Society Press, Los Alamitos (2003) 11. Kao, H.Y., Lin, S.H., Ho, J.M., Chen, M.S.: Mining web informative structures and contents based on entropy Analysis. IEEE Trans. on Knowledge and Data Engineering 16, 41–55 (2004) 12. LIBSVM – A Library for Support Vector Machines. Chih-Chung Chang and ChihJen Lin http://www.csie.ntu.edu.tw/∼ cjlin/libsvm 13. Botha, M., Solms, R.V.: Utilising fuzzy logic and trend analysis for effective intrusion detection. Computers & Security 22, 423–434 (2003) 14. Roesch, M.: Snort-lightweight intrusion detection for network. In: Proceedings of LISA’99: 13th System Administration Conference, Seattle, Washington, pp. 229– 238 (1999) 15. MIT.: http://www.ll.mit.edu/IST/ideval/data/1998/1998 data index.html 16. Depren, O., Topallar, M., Anarim, E., Kemal Ciliz, M.: An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks. Expert Systems with Applications 29, 713–722 (2005) 17. Chen, R.C., Hsieh, C.H.: Web page classification based on a support vector machine. Expert System with Applications 31, 427–435 (2006) 18. Mukkamala, S., Sung, A.H., Abraham, A.: Intrusion detection using an ensemble of intelligent paradigms. Computer Application 28, 167–182 (2005) 19. Rubin, S., Jha, S., Miller, B.: Automatic generation and analysis of NIDS attacks. In: Proceedings of 20th Annual Computer Security Application Conference, vol. 00, pp. 28–38. IEEE Computer Society, Los Alamitos (2004) 20. Verwoerd, T., Hunt, R.: Intrusion detection techniques and approaches. Computer Communications 25, 1356–1365 (2002) 21. Vapnik, V.N.: The nature of statistical learning theory. Springer, New York (1995) 22. Chen, W.H., Hsu, S.H., Shen, H.P.: Application of SVM and ANN for intrusion detection. Computers & Operations Research 32, 2617–2634 (2005) 23. Website, http://163.17.27.154/cyut.jsp 24. Liao, Y., Rao Vemuri, V.: Use of K-Nearest Neighbor classifier for intrusion detection. Computer & Security 21, 439–448 (2002)

An Ontology-Supported and Fully-Automatic Annotation Technology for Semantic Portals Sheng-Yuan Yang Department of Computer and Communication Engineering, St. John’s University, 499, Sec. 4, TamKing Rd., Tamsui, Taipei County 25135, Taiwan [email protected] Abstract. We employ the techniques of ontology and linguistics to develop a fully-automatic annotation technique, which, when coupled with an automatic ontology construction method, can play a key role in the development of semantic portals. Based on this technique, we also demonstrate a semanticportal prototype which defines how a semantic portal is interacting with the user by providing five different types of interaction patterns, including keyword search, synonym search, POS (Part-of-Speech)-constrained keyword search, natural language query, and semantic index search. Our primarily demonstrations show that it can indeed retrieve better semantic-directed information to meet user requests. Keywords: Automatic Webpage Annotation, Ontology, Semantic Portal, Internet Applications, Intelligent Interfaces.

1 Introduction The Web has been drastically changing the availability of electronically available information. An information portal, which can help people effectively use this voluminous repository, is becoming ever important. Tim Berners-Lee visions “Semantic Web” as the next generation of web by allowing the web pages on the Semantic Web to be machine-readable [11]. Techniques to realize this vision include the techniques centered on domain ontology [11], which help domain knowledge conceptualization. Webpage annotation is another important technique, which adds relevant domain knowledge and meta-information into a web page to enhance its semantics [1]. Logic inference is yet another important technique, which allows the development of various web services. These techniques can effectively relieve the bottlenecks of the current Web [6,18]. Automatic webpage annotation can even reduce the high cost of pre-processing of web pages into a semantic web by humans [4,9]. We conjecture that these techniques can equally help us overcome the problems associated with general portals and develop a fully-automatic annotation technique, which, when coupled with an automatic ontology construction method, can play a key role in the development of semantic portals. Based on this technique, we also demonstrate a semantic-portal prototype [5] which defines how a semantic portal is interacting with the user by providing five different types of interaction patterns, including keyword search, synonym search, POS (Part-of-Speech)-constrained keyword search, natural language query, and semantic index search. Our primarily demonstrations show that it can indeed retrieve better semantic-directed information H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 1158–1168, 2007. © Springer-Verlag Berlin Heidelberg 2007

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to meet user requests. The “Einstein” domain is chosen as the target application of our semantic portal and will be used for explanation in the remaining sections.

2 Semantic Portal Architecture Fig. 1 illustrates the architecture of our semantic-portal prototype. Pre-Processor is responsible for removing HTML tags and punctuations from the original web pages, and storing them in “Pre-Processed WebPages Database.” Semantic Webpages Generator then bases on the database to produce three supporting knowledge bases. The first knowledge base is “Stemmed WebPages Database” created by Stemmer, which stems words and removes stop and noisy words on a pre-processed web page. The second is “POS-attached WebPages Database” created by the POS Tagger, which analyzes the parts-of-speech (POS) of the words on a web page and transforms the web page into a list of words with POS. The third is “Annotated WebPages Database” created by Automatic Annotator, which automatically identifies the concepts and relationships contained in a web page, derives a content summary for the web page, and constructs a semantic index for the web page. These knowledge bases, along with Ontology Database, support Portal Interface (Fig. 2) to provide a better semantic portal. In other words, Portal Interface applies these five knowledge bases during the interaction with the user by providing five different types of search mechanisms (described before). The user can choose one of the five patterns to input his/her queries. Answer Generator finally produces semantic-directed answers to respond to the user query. Finally, Ontology Database provides the most fundamental semantics, as illustrated in Fig. 3, developed based on the method described in [20] using Orig in al W ebP ag es

PreProcessor

Pre-P rocessed W ebPages

Stem m er

PO S Tag ger

Auto m atic Anno tato r

Stem m ed W ebPages

POS -attched W ebPages

An notated W eb Pages

Sem an tic W eb pag es Generato r

O nto logy Datab ase

P ortal Interfac

User

Fig. 1. Semantic portal architecture

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Protégé2000 [13]. In the figure, nodes represent ontology concepts; links labelled with “isA” denotes the parent-child relationship between concepts, which allows inheritance of features from parent classes to child classes; links labelled otherwise represent some reference relationships from a concept to another concept, where ”*” denotes multiple instance reference [5].

Answer

Query

User Interface

Answer Generator

Query M anager

Keyw ord

Keyw ord Matcher

Keyword and POS

Keyw ord+POS M atcher

Synonyms

Synonym s M atcher

NL question

Pseudo NL Answerer

Keyw ord Sem antics

Sem antics Index Searcher

Fig. 2. Portal interface workflow

isA

Creator*

Creator* Scientist

People

College Theory*

Theorem

DateOfDeath

DateOfBirth

Papers*

University

Paper

Date

Fig. 3. Part of Scientist Ontology hierarchy

Fig. 4. Query Ontology

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In addition to the scientist ontology, we also defined a query ontology to facilitate the description of queries. Fig. 4 shows the query ontology, where the root node represents class “Query” which contains a set of fields defining the semantics of the class, each field representing one attribute of “Query,” e.g., QueryType, Name, etc. The links labelled with “io” represent relationship “instance_of”, relating an instance to the Query class. The figure shows four query instances, including Who, What, Which, and Where, each standing for a specific solution pattern. For example, query instance “Who” has “People” as the value of its QueryType attribute, which means the answer to the query instance must belong to the People class. Finally, we have used Protégé’s APIs to develop a set of ontology services, which provide primitive functions to support the applications of the ontologies. The ontology services currently available include transforming query terms into canonical ontology terms, finding definitions of specific terms in ontology, finding relationships among terms, finding compatible and/or conflicting terms against a specific term, etc.

3 Semantic Webpages Generator 3.1 Stemmer Fig. 5 illustrates the workflow of Stemmer. Qtag is used here to help remove stop words, passing only major terms to WordNet-based Stemmer. Specifically, Qtag

Fig. 5. Workflow of Stemmer Table 1. Part of POS tags used by Stemmer and POS Tagger POS Tag Meaning 1 BE be 2 BEDR were 3 BEDZ was 4 BEG being 5 BEM am 6 BEN been 7 BER are 8 BEZ is 9 CC conjunction, coordinating (and) 10DO do 11DOD did 12DON Done 13DOG Doing 14DOZ Does 15DT determiner, general (a, the, this, that) 16IN preposition(on, of) 17MD modal auxiliary (might, will) 18TO infinitive marker (to) 19WDT det, wh- (what, which, whatever, whichever) 20WP pronoun, wh- (who, that) 21WP$ pronoun, possessive wh- (whose) 22WRB adv, wh- (how, when, where, why)

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employs probability with a sliding window [19] to determine POS of the words in a web page. The output of Qtag contains detailed POS tags (Examples shown in Table 1), which are further categorized into five categories, namely, noun, verb, adjective, adverb, and DC (for Don’t Care) for the purpose of removing stop words. Finally, WordNet-based Stemmer employs WordNet, which is a comprehensive vocabulary [21], to transform the major terms into their stems and stores them in Stemmed WebPages Database. 3.2 POS Tagger Fig. 6 illustrates the workflow of POS Tagger, which is very similar to the Stemmer. Qtag is used here to actually produce a POS for each word of a web page. WordNetbased Stemmer is then used as in Stemmer to do word stemming.

Pre-Processed WebPages

Qtag

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Fig. 6. Workflow of POS Tagger

3.3 Automatic Annotator Fig. 7 illustrates the architecture of Automatic Annotator. Concept Identifier and Relationship Identifier work together to extract the concepts and relationships contained in a web page, supported by the domain ontology. Text Summarizer

Fig. 7. Automatic Annotator architecture

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employs Text-Miner, developed by IBM [8], to identify important sentences in order to derive a summary for the web page. Semantic Index Generator identifies the most important sentence in the summary as the semantic index, supported by the domain ontology again. Once the semantic index is identified, it is treated as the center theme of the webpage and we can use it to refine the summary. Basically, we employ the vector space model [15] to calculate the correlations between the semantic index and the sentences in the webpage summary, and accordingly adjust their appearance order or remove some less relevant sentences. This helps us develop a summary which contains sentences that are not only highly-weighted but also highly relevant to the center theme. Finally, XML Translator adds all of these into a web page and transforms it into an annotated web page. Note that Automatic Annotator not only annotates a web page, but also adds relevant synonyms into the annotated web page, with the help of Concept Identifier and the domain ontology [5].

4 System Demonstrations First, suppose the user chooses to query by keywords, Query Manager will directly invoke Keyword Matcher, which uses full-text search to compare the keywords with the words in all of the web pages stored in Pre-Processed WebPages Database. This process is the same as the classical keyword match employed by most general portals. Two major issues are associated with the keyword-based query method, however. First, it only retrieves web pages which contain syntactically the same keywords. Second, keywords of different parts of speech may represent different meanings. The second issue can be easily coped with by allowing the user to use the Word+POS

Fig. 8. Search result of keyword+POS match

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Match method, which requires the user to associate a part-of-speech with each input keyword. Query Manager then invokes Keyword+POS Matcher to search in POSattached WebPages Database for web pages which contain the keywords appearing in the correct part of speech, as illustrated in Fig. 8. We employ two methods to cope with the first issue in our system. The first method not only invokes Synonyms Matcher to search in Annotated WebPages

Fig. 9. Search results in response to stemmed keywords

Fig. 10. Search results in response to a natural language query

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Database for web pages containing the synonyms of the entered keywords, but also tackles morphological changes by returning a web page containing “writing,” given input keyword “wrote,” as shown in Fig. 9. Alternatively, we allow the user to post natural language query in order to deal with the first issue. The user can place a natural language query by using NL-Based Query method, when Query Manager invokes Pseudo NL Answerer to propose answers. Fig. 10 shows an example of NL question “Who are Einstein’s sons?”; Query Manager answers “Edward (Albert)” and “Hans Albert.” In addition to the above four user interface methods, we define a new keyword query method called semantic index match. The user can choose the method and enter usual keywords; Query Manager will invoke Semantic Index Searcher to search in Annotated Webpages Database for all web pages whose semantic index contains the keywords and accordingly displays the semantic indices for the user, as illustrated in Fig. 11. Note that the return page contains a list of URLs, each followed by its semantic index. This approach is different from general keyword-based search engines, which usually return the title and the first sentence of each web page.

Fig. 11. Search results in response to semantic index search

5 Related Works and Comparisons The Semantic Web or semantic portals heavily rely on webpage annotation, which inevitably becomes one of the hottest research topics. SHOE (Simple HTML Ontology Extension) is equipped with a manual Knowledge Annotator [17], which is a Java program allowing the user to annotate web pages graphically. OntoMatAnnotizer [14] is a user-friendly interactive webpage annotation tool. It supports the user with the task of creating and maintaining ontology-based DAML+OIL markups,

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e.g., creation of DAML-instances, attributes and relationships. Annotea [3] in LEAD (Live and Early ADoption) uses special RDF annotation schema to add comments (a kind of annotation) as metadata. S-CREAM (Semi-automatic CREAtion of Metadata) [7] allows creation of metadata and is trainable for a specific domain. The above annotation mechanisms are either manual or semi-automatic, which impose heavy labor on humans facing a voluminous amount of web pages. Some automatic annotation mechanisms appeared in the literature. For instance, [12] describes two algorithms which use redundancies in the content of table and detail pages to help information extraction. [16] proposes a strategy based on active recommendation to support the semantic annotation of contested knowledge to promote annotators’ interest. [10] devises conflict detection patterns based on different data, ontology at different inference levels and proposes the corresponding automatic conflict resolution strategies for image annotation. Amilcare [2] is an information extraction system which helps automatic annotation with learning capability. Its power of linguistic analysis stems from Gate, which performs tokenization, sentence identification, POS tagging, gazetteer lookup and named entity recognition. Gate functions virtually in the same way as the combination of Qtag, Concept/Relationship identifier, and Text-Miner in our system during automatic annotation. However, since Gate is easier to be used, it will be planned to employ in our future semantic-based systems. In addition, Amilcare is salient in employing the (LP)2 algorithm, a supervised algorithm that falls into a class of Wrapper Induction System using LazyNLP. (LP)2 can induce rules which help insert annotations in the texts and rules which correct mistakes and imprecision in the annotations generated by the former rules. We reckon that as an information extraction system, Amilcare works very similarly to part of our system. Its adaptive feature, supported by the underlying learning capability, however, provides yet another level of automation in webpage annotation and deserves more attention.

6 Conclusions and Discussions We have developed a fully-automatic annotation technique, which employs the techniques of ontology and linguistics. It supports the five patterns of interaction of the portal interface by generating annotated webpages, stemmed webpages, and POSattached webpages with the help of domain ontology. Our technique is an interesting contribution in terms of the following features. First, it is a fully automatic annotation tool. The tool not only helps develop annotated web pages during the construction of the portal, but also provides the user with an automatic online annotation process. The user can use the process to annotate the web pages specific to his/her interests. Second, it enriches webpage annotation by content summary and semantic index as two facets of representative semantics of a web page. Finally, it improves the traditional keyword-based retrieval mechanism by providing word-stemming, partsof-speech, natural language processing, and semantic index search. These extensions help capture the true intention of the user and return better information. For easy demonstration of the techniques in our semantic-portal prototype, the current implementation runs on a very simple “scientist” domain. However, we believe even if the domain is scaled up, our techniques still be applicable. The idea is this: we are not directly scaling up our ontology. Instead we can create a complex

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system by integrating a set of simple systems through a multi-agent architecture which is supported by a set of simple domain ontologies. By exploiting the capability of Protégé2000, which supports the creation, extension and cooperation of a set of domain ontologies, we really need not make much change to our ontology-supported semantic portal in order to transform it into a complex portal. What we really need to focus on is how to make a set of ontology-supported systems cooperate effectively, which is under our investigation. The ontology plays a very important role in our annotation technique. One major difficulty is its construction, which currently still relies on the help of domain experts. We are planning to investigate the technique of automatic ontology construction in the future. We believe our automatic annotation technique, when coupled with the automatic ontology construction technique, can help proliferate the techniques of Semantic Web and in turn promote the development of better Semantic Portals. Acknowledgments. The author would like to thank Y.H. Chang for her assistance in system implementation. This work was supported by the National Science Council, R.O.C., under Grants NSC-89-2213-E-011-059, NSC-89-2218-E-011-014, and NSC95-2221-E-129-019.

References 1. Albanese, C., Calabrese, A., D’Amico, A., Mele, F., Minei, G., Kostakopoulos, L.: Webpages Annotation and Adaptability: A Semantic Portal on the International Space Station. In: Proc. of the 2nd Italian Semantic Web Workshop: Semantic Web Applications and Perspectives, Trento, Italy (2005) 2. Amilcare.: (2002) Available at http://nlp.shef.ac.uk/amilcare/amilcare.html 3. Annotea.: (2001) Available at http://www.w3.org/2001/Annotea/ 4. Benjamins, V.R., Contreras, J., Corcho, O., Gomez-Perez, A.: Six Challenges for the Semantic Web. In: Proc. of the KR2002 Workshop on Semantic Web, Toulouse, France (2002) 5. Chang, Y.H.: Development and Applications of Semantic Webs. Master Thesis, Dept. of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan (2003) 6. Fensel, D., Hendler, J., Lieberman, H., Wahlster, W.: Creating of Semantic Web. Available at http://informatik.uibk.ac.at/users/c70385/ftp/paper/mit.introduction.pdf (2000) 7. Handschuh, S., Staab, S., Ciravegna, F.: S-CREAM: Semi-automatic CREAtion of Metadata. In: The 13th International Conference on Knowledge Engineering and Knowledge Management, Siguenza, Spain, pp. 358–372 (2002) 8. IBM Intelligent Miner for Text (1998) Available at http://www.searchtools.com/tools/ ibm-imt.html 9. Kiyavitskaya, N., Zeni, N., Cordy, J.R., Mich, L., Mylopoulos, J.: Semi-Automatic Semantic Annotation for Web Documents. In: Proc. of the 2nd Italian Semantic Web Workshop, Trento, Italy (2005) 10. Lee, C.Y., Soo, V.W.: The Conflict Detection and Resolution in Knowledge Merging for Image Annotation. Information Processing and Management 42(4), 1030–1055 (2006) 11. Lee, T.B., Hendler, J., Lassila, O.: The Semantic Web. Scientific American 284(5), 35–43 (2001)

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12. Lerman, K., Getoor, L., Minton, S., Knoblock, C.: Using the Structure of the Web Sites for Automatic Segmentation of Tables. In: Proc. of the 2004 ACM SIGMOD International Conference on Management of Data, Paris, France, pp. 119–130 (2004) 13. Noy, N.F., Sintek, M., Decker, S., Crubezy, M., Fergerson, R.W., Musen, M.A.: Creating Semantic Web Contents with Protégé-2000. IEEE Intelligent Systems 16(2), 60–71 (2001) 14. OntoMat.: Available at http://annotation.semanticweb.org/tools/ontomat (2003) 15. Salton, G., Wong, A., Yang, C.S.: A Vector Space Model for Automatic Indexing. Communications of the ACM 18(11), 97–107 (1975) 16. Sereno, B., Shum, S.B., Motta, E.: Semi-Automatic Annotation of Contested Knowledge on the World Wide Web. In: Proc. of the 2004 International World Wide Web Conference, New York, USA, pp. 276–277 (2004) 17. SHOE.: (2000) Available at http://www.cs.umd.edu/projects/plus/SHOE/KnowledgeAnnotator.html 18. Stollberg, M., Thomas, S.: Integrating Agents, Ontologies, and Semantic Web Services for Collaboration on the Semantic Web. In: Proc. 2005 AAAI Fall Symposium Series on Agents and the Semantic Web, Arlington, Virginia, USA (2005) 19. Tufis, D., Mason, O.: Tagging Romanian Texts: a Case Study for QTAG, a Language Independent Probabilistic Tagger. In: Proc. of the 1st International Conference on Language Resources & Evaluation, Granada, Spain, pp. 589–596 (1998) 20. Uschold, M., Gruninger, M.: Ontologies: Principles, Methods and Applications. The Knowledge Engineering Review 11(2), 93–136 (1996) 21. WordNet 2.1.: Available at http://wordnet.princeton.edu/ (2005)

Development and Evaluation of an Intelligent Colour Planning Support System for Townscapes Yuichiro Kinoshita1 , Eric W. Cooper2 , and Katsuari Kamei1 1

Department of Human and Computer Intelligence, Ritsumeikan University, Kusatsu, Shiga 525-8577, Japan [email protected], [email protected] 2 Department of Computer Science, Ritsumeikan University, Kusatsu, Shiga 525-8577, Japan [email protected]

Abstract. Aesthetics of townscapes have been a main factor in urban development. This paper introduces IroKage, an intelligent colour planning support system. The system offers improved colour schemes for existing townscapes based on three elements: colour harmony, impressions of the townscape, and cost for the change of colours. The system is constructed using an evolutionary algorithm and the Kansei engineering approach. After the construction, system evaluation is conducted. The subjects evaluate fifteen colour schemes output from the system in terms of colour harmony and the ideal impressions for the townscapes using the semantic differential method introduced by Osgood et al. The results of the evaluation demonstrate that the system has sufficient ability to propose appropriate colour schemes for the ideal town impressions.

1

Introduction

The problem of how to live in a comfortable environment has attracted attention with the changing expectation of residents, and the townscape is a central factor in urban-development problems. Aesthetics of townscapes have been studied in various fields [1, 2], and the colour scheme of the buildings is one of the most influential factors. In considering colours of the buildings, each nearby colour in the row is a key point. Proper evaluation of colours has to consider the whole street, not only individual buildings. Several colour studies have been reported as colour harmony principles [3]. Using one of those principles may enable evaluation of the colours in the whole street and allow a better colour scheme for the townscape to be found. However, characteristics of the target town also need to be respected throughout the colour planning process. In addition to the form and material of the buildings in the town, the climate and the historical background of the town are also elements affecting the characteristics, and these elements evoke particular impressions as well. Although several previous colour plans have included characteristics and impressions of towns, most of them have been based on anecdotal reports or experience and have been conducted for individual localities. H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 1169–1179, 2007. c Springer-Verlag Berlin Heidelberg 2007 

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Colour harmony model Ideal town impression

Kansei evaluation model

Current townscape colours

Townscape colour propositions

Cost evaluation model Colour combination evaluation unit generated colours

evaluation scores

Colour combination generation/selection unit Colour planning support system IroKage

Fig. 1. Block diagram of the colour planning support system

In our studies, an intelligent colour planning support system for townscapes, called IroKage, was developed [4]. The system helps to improve existing townscapes by considering colour harmony as well as the town impressions. This paper focuses on the system evaluation based on townscape colour schemes output from the system.

2

System Overview

The inputs to the colour planning support system are the current townscape colours and ideal town impressions expressed by adjectives. The system offers several colour propositions for the townscape, as shown in Fig. 1. In the system, the colour combination generation/selection unit generates a large number of colour combinations. The colour combination evaluation unit evaluates the colour combinations generated from the generation/selection unit one by one. First, the colour combination generated is evaluated from the approach of colour harmony. Moon and Spencer brought together the concepts in previous colour harmony principles and proposed equations to calculate aesthetic measure for colour harmony [5–7]. We expanded their aesthetic measure for townscape colours and constructed a colour harmony model [4] using Takagi-Sugeno type fuzzy reasoning [8]. The inputs to the model are townscape colours and the output is the aesthetic measure. In addition to colour harmony, impressions of the generated colour combination are evaluated in respect of their suitability when compared to the ideal impressions of the townscape. The differences between the ideal impressions of the townscape and the psychological response for the generated colour combination is calculated. The combination having the smallest difference is evaluated as the appropriate combination. Handling the impressions of the townscape requires quantification of psychological responses. A Kansei evaluation model to quantify the human responses is constructed by linking town impressions and

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townscape colours [9]. Kansei is a Japanese term which means human sensibility or impression. The targets of this system are mostly townscapes that already exist. A critical issue is finding the most effective colour scheme changes at the lowest cost. The cost evaluation model calculates the cost to convert the current townscape colours to the generated colours. In terms of the colour changing cost, the repaint cost is assumed to be almost the same even if the new colour is similar to the original colour. The number of colour changes is therefore simply applied for the cost evaluation. Based on scores obtained by the evaluation unit, the generation/selection unit selects several colour propositions for the output of the system.

3 3.1

Quantification of Town Impressions Evaluation Experiments of Town Impressions

In order to manage town impressions in the system, psychological responses to townscape colours are quantified from the Kansei engineering approach [10]. This technique translates target psychological responses into perceptual design elements. First, evaluation experiments were conducted to investigate the relationship between town impressions and townscape colours. Adjectives allow expression of the psychological responses. In this study, adjectives express the impressions of the townscape. Approximately 470 adjectives related to the town impressions were collected from dictionaries and previous studies. After the collection, similar adjectives were combined and they were paired with opposite meanings. Finally, the number of adjectives was reduced to sixteen pairs. One hundred colour picture samples were prepared for the evaluation experiments. The pictures show the front side of a row of houses. The samples were made by altering the same picture while maintaining all the other conditions. The wall and roof colours of the houses were selected from a wide variety of chromatic and achromatic colours, and the window frame colours were selected from a set of achromatic colours. Computer-based evaluation experiments were conducted for 20 subjects, four females and sixteen males, using the semantic differential (SD) method [11]. One picture sample and the sixteen adjective pairs are placed on the computer screen. In the experiments, the subjects select a suitable response on the five-step SD scale for each pair of adjectives. The subjects evaluated all of the 100 picture samples, which were shown at random. 3.2

Selection of Adjectives

After the experiments, appropriate adjectives were selected based on the results of the above experiments. An evaluation in the centre of the SD scale implies that the adjective was evaluated as neutral. The adjectives with many neutral evaluations may be irrelevant for expressing town impressions. Four adjectives

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Y. Kinoshita, E.W. Cooper, and K. Kamei Table 1. Pairs of adjectives related to townscape impressions adjective No. adjectives 1 cold – warm 2 unrefined – refined 3 restless – calm 4 unfriendly – friendly 5 uncomfortable – comfortable 6 artificial – natural 7 typical – individualistic 8 conservative – progressive 9 quiet – lively 10 old-fashioned – modern 11 awkward – elegant 12 western – eastern

were excluded for this reason. Variance of evaluation scores is also a factor to be considered. We checked the variance for each sample but every pair of adjectives showed low variance. No adjective was excluded in this process. Consequently, the twelve adjectives shown in Table 1 were selected. 3.3

Construction of Kansei Evaluation Model

With the selected adjectives, a Kansei evaluation model is constructed. The behaviour of Kansei response is usually non-linear and it is difficult to manage Kansei data using conventional methods. Some previous studies adopted neural networks for Kansei data [12, 13]. Neural networks are one of the best methods for modelling non-linear data. The model was constructed using a neural network for each pair of adjectives. Sixty-two experimental results were selected as the training data for the neural networks and 20 results were selected as the testing data for the model validation. Both training and testing data were selected to consist of various, dissimilar colour combinations. Each colour in the data was selected to have unique hue, brightness or saturation attributes. The input items to each neural network are the wall colours, roof colours, and window frame colours of the houses in the picture samples. Every colour is expressed in the CIELAB colour system [14]. The three CIELAB values, L∗ , a∗ and b∗ , are used for the wall colours and roof colours, and only L∗ is used for the window frame colours because the use of chromatic colours is generally limited. Since there are three buildings in the sample picture, the input data becomes 21 values. The output from each neural network is the SD scale response for the town impression. Every value is given in [0, 1]. Each neural network has three layers and the numbers of units are 21, 30, and 1 on the input, hidden, and output layers, respectively. Every network was trained using back-propagation learning to an accuracy of 3.125 × 10−4 mean square error, which is equivalent to 0.1 on the SD scale of 1 to 5.

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L* a* b* L* a* b* L* L* a* b* L* a* b* L* L* a* b* L* a* b* L* 1

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Fig. 2. Encoding of individuals. Black arrows represent candidate crossover points.

3.4

Performance Test

After the model construction, a performance test was conducted. The 20 testing data were input into the constructed model, and the errors between the output from the model and the average of the experimental results were calculated. The average accuracy of the twelve neural networks was 7.321 × 10−3 mean square error, 0.374 on the SD scale of 1 to 5. This result indicates that our Kansei model has adequate ability to evaluate the town impressions.

4 4.1

Townscape Colour Generation and Selection Approach

For the implementation of the colour combination generation/selection unit in the system, the problem is the method to select appropriate combinations under the evaluation scores with the three different objectives: colour harmony, impression and cost. For selecting appropriate colour combinations to output from the system, we focuse on Pareto optimal solutions. In a maximization problem, a is said to dominate b when fe (a) ≥ fe (b)

(∀e ∈ {1, 2, . . . , n}, a = b)

(1)

where fe represents a vector function, and a and b represent two decision vectors. The decision vectors that are not dominated by any other vector are called Pareto optimal solutions. In this study, those solutions are searched from an evolutionary algorithm approach [15]. 4.2

Encoding

The system uses wall, roof and window frame colours of three buildings, nine colours in total. In the evolutionary algorithm, the colours are expressed in the CIELAB colour system and encoded by value encoding. Each wall colour and roof colour consists of the three CIELAB values, L∗ , a∗ and b∗ . Each window frame colour consists of L∗ value only. Seven values therefore express the colour scheme of one building. In total, one individual includes 21 values, as shown in Fig. 2. Every L∗ value is given in [10, 90] while a∗ and b∗ values are given in [-40, 40].

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Fig. 3. Wall colour alteration patterns in the initialization and mutation processes. Pattern (a) alters all attributes of the target colour while pattern (b) only alters the lightness attribute. Pattern (c) alters the hue and saturation attributes. Table 2. Candidate roof colours colour grey brown green red blue

4.3

L∗ 45 35 30 45 45

a∗ b∗ 0 0 20 25 −15 10 40 25 0 −35

Fig. 4. Configuration of the townscape samples

Colour Search Algorithm

The generation/selection unit generates the population of the first generation by altering several colours in the current townscape input. First, up to three colours are chosen as the target(s) for alteration. When a wall colour is chosen as the alteration target, one of the three alteration patterns shown in Fig. 3 is randomly applied. When a roof colour is chosen as the target, the colour is randomly replaced with one of the five candidate roof colours shown in Table 2. For a window frame colour, the target L∗ value is randomly replaced with either ‘10’ or ‘90.’ The individuals generated are evaluated based on the three evaluation models. Based on the evaluation scores, individuals are selected as the parents of the next generation. For the selected parents, single point crossover is executed at one of the eight crossover points shown in Fig. 2. After that, each of nine colours in an individual mutates with a given probability. The target colours are altered in the same manner as the initialization processes. Finally, those individuals will be the population of the next generation. The system stores all individuals through all generations and selects the highest-ranking individuals for output.

5 5.1

System Evaluation Townscape Colour Development

Townscape colour development is conducted using our system, IroKage. Three townscape samples are prepared as the targets of the colour development. Every

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Table 3. Colours in the townscape Table 4. Parameters for the evolutionary algorithm samples sample building No. No. 1 2 1 3 1 2 2 3 1 2 3 3

L* 80 50 80 80 45 30 80 75 65

wall a * b* 0 10 20 20 0 15 10 10 0 0 0 20 0 0 20 20 0 0

L* 45 45 30 45 45 45 45 45 45

roof frame a * b* L * 0 0 10 0 0 90 15 10 10 0 0 90 0 0 90 0 0 90 0 0 10 0 0 90 0 0 90

population size 100 crossover probability 0.9 mutation probability 0.03 number of generations 300

sample consists of three buildings in a row. Fig. 4 shows the configuration of the samples and Table 3 shows colours in the samples. Here, natural, warm and lively are selected as the ideal impressions for Samples 1, 2 and 3, respectively. The system searches for Pareto optimal solutions using the evolutionary algorithm with the parameters shown in Table 4. Tables 5, 6 and 7 show examples of the system output for Samples 1, 2 and 3, respectively. The bold face numbers in the tables indicate modified colours. For Sample 1, the system modified the wall colour of Building 1, light blue, and/or the wall colour of Building 2, purple, in most cases. Those colours were replaced with brownish, greenish and yellowish colours. For Sample 2, the system output warm colours, yellowish and reddish colours. These match to the ideal impression warm. For Sample 3, the system mainly output colour schemes with high saturation and high lightness colours. 5.2

Evaluation Methods

In order to make sure that the system output is appropriate, evaluation experiments are conducted. The participants of the experiments evaluate colour schemes output from the system in respect of colour harmony and the ideal impressions using the SD method. As the targets of evaluation, a set of samples is prepared. The sample set consists of fifteen samples repainted in the output colour schemes shown in Tables 5, 6 and 7 and three original colour schemes shown in Table 3. However, the participants are not informed that the sample set includes the original colour schemes. For the evaluation of colour harmony, a pair of adjectives, disharmonious—harmonious, is used. For the evaluation of impression, one pair of adjectives is selected from Table 1 depending on the ideal impressions (e.g., artificial—natural for Sample 1). In total, the participants therefore evaluate eighteen samples with the two pairs of adjectives. The experiments were conducted in a computer-based environment. The participants sit 70 cm away from a CRT display. The participants sit 70cm away from a CRT display. Fig. 5 shows the screen appearance of the experiments. One of the samples is placed at the centre of the screen with a black background, and one pair of adjectives is placed on the lower part. There is a five-step scale

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Table 5. System output examples for Sample 1 with the ideal impression natural result building wall No. No. L * a * b* 1 80 0 10 1A 2 50 4 30 3 80 0 15 1 80 0 10 1B 2 39 16 13 3 80 0 15 1 80 12 13 1C 2 50 1 15 3 80 0 15 1 80 0 10 1D 2 40 9 27 3 80 0 15 1 62 1 37 1E 2 46 11 30 3 80 0 15

L* 45 45 30 45 30 30 45 45 45 45 45 45 45 45 30

roof a* 0 0 15 0 15 15 0 0 0 0 0 0 0 0 15

b* 0 0 10 0 10 10 0 0 0 0 0 0 0 0 10

frame L* 10 90 10 10 90 10 10 10 10 10 90 10 10 90 10

Table 6. System output examples for Sample 2 with the ideal impression warm result building wall No. No. L * a * b* 1 80 13 15 2A 2 45 0 0 3 30 0 20 1 81 1 19 2B 2 45 0 0 3 67 7 27 1 80 8 16 2C 2 68 15 30 3 30 0 20 1 86 2 2 2D 2 45 0 0 3 67 23 24 1 78 11 22 2E 2 45 19 19 3 68 15 28

L* 45 45 45 45 45 45 45 45 45 45 45 45 45 35 45

frame roof a * b* L * 0 0 90 0 0 90 0 0 90 0 0 90 0 0 90 0 0 90 0 0 90 0 0 90 0 0 90 0 0 90 0 0 90 0 0 90 0 0 90 20 25 90 0 0 90

between the adjectives and one value Table 7. System output examples for Samis highlighted as the current selection. ple 3 with the ideal impression lively The participants move the selection result building frame wall roof and choose a suitable response using No. No. L * a * b* L * a * b* L * 87 10 7 45 0 0 90 1 arrow and space keys on a keyboard. 3A 2 75 20 20 45 0 0 90 The samples and adjectives are shown 3 65 33 12 45 0 0 90 in random sequence to reduce influ1 80 0 0 45 0 0 90 ence from the order of the presenta3B 2 75 20 20 45 0 0 90 3 65 33 2 45 0 0 90 tion. 3C

5.3

Results and Discussion 3D

1 2 3 1 2 3 1 2 3

80 75 65 80 34 71 80 74 42

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0 0 0 0 0 0 0 0 0

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The experiments were conducted for seventeen participants, fourteen males 3E and three females. Figs. 6, 7 and 8 show the average evaluation scores of the experiments for Samples 1, 2 and 3, respectively. In Fig. 6, Sample 1, with the original colour scheme, was evaluated as 1.88 for colour harmony (standard deviation 1.05) and 1.35 for the natural impression (standard deviation 0.49). Those scores indicate that the sample had poor colour harmony and no ideal impression. This kind of townscape is the main target of our system. Compared to the original sample, all the results output from the system, 1A–1E, achieved good evaluation scores for both colour harmony and the natural impression. Especially, Result 1E was evaluated as 4.12 for colour harmony (standard deviation 0.78) and 3.71 for the natural impression (standard

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Fig. 5. Screen appearance of the experiments

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Fig. 6. Evaluation results for Sample 1 (lively) 5

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Fig. 7. Evaluation results for Sample 2

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Fig. 8. Evaluation results for Sample 3

deviation 1.05) with the two colour changes. The system successfully improved colour harmony and realized the ideal impression. In Fig. 7, Sample 2 was evaluated as 3.35 for colour harmony (standard deviation 1.27) and 1.29 for the warm impression (standard deviation 0.47). It represents that the original sample already had good colour harmony. Although this kind of townscape is not the target of our system, the results from the system, 2A–2E, showed high scores for the warm impression. Not only for the impression, Results 2B and 2E even achieved better colour harmony. The colour harmony score for Result 2E was 3.82 (standard deviation 1.01) while the impression evaluation score was 4.41 (standard deviation 0.62). For some results, colour harmony scores were somewhat decreased. However, the decreases were not significant compared to the improvements in the warm impression. Like Sample 2, Sample 3 also had good colour harmony but did not have enough lively impression. For this sample, colour harmony score was getting decreased concurrently with increasing of the impression score. Here, the

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relationship between colour harmony and the lively impression can be considered a trade-off where it is difficult to improve the two objectives at the same time. In this case, the system is expected to output the optimal colour schemes with variety. The output from the system included lively impression samples, such as Result 3A as well as harmonious samples, such as Result 3C, as shown in Fig. 8. The use of the evolutionary algorithm helped to obtain a variety of results as Pareto optimal solutions.

6

Conclusions

In this paper, the intelligent colour planning support system for townscape, IroKage, was introduced. The system was constructed using an evolutionary algorithm with colour harmony, impression and cost evaluations. The evaluation of IroKage demonstrated that the system has sufficient ability to improve colour harmony and realize ideal impressions. The system successfully proposed appropriate colour schemes.

References 1. Lynch, K.: The Image of the City. MIT Press, Cambridge, MA (1960) 2. Nasar, J.L.: The Evaluative Image of the City. Sage Publications, Thousand Oaks, CA (1998) 3. Judd, D.B., Wyszecki, G.: Color in Business, Science and Industry. Wiley, New York, NY (1975) 4. Kinoshita, Y., Sakakura, Y., Cooper, E.W., Hoshino, Y., Kamei, K.: Townscape colour planning system using an evolutionary algorithm and Kansei evaluations. In: Proc. IEEE Int. Conf. Fuzzy Syst. pp. 931–938 (2006) 5. Moon, P., Spencer, D.E.: Geometric formulation of classical color harmony. J. Opt. Soc. Am. 34(1), 46–59 (1944) 6. Moon, P., Spencer, D.E.: Area in color harmony. J. Opt. Soc. Am. 34(2), 93–103 (1944) 7. Moon, P., Spencer, D.E.: Aesthetic measure applied to color harmony. J. Opt. Soc. Am. 34(4), 234–242 (1944) 8. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst., Man, Cybern. 15(1), 116–132 (1985) 9. Kinoshita, Y., Cooper, E.W., Hoshino, Y., Kamei, K.: Kansei and colour harmony models for townscape evaluation. Proc. Inst. Mech. Eng., I J. Syst. Control Eng. 220(8), 725–734 (2006) 10. Nagamachi, M.: Kansei Engineering: A new ergonomic consumer-oriented technology for product development. Int. J. Ind. Ergonomics 15(1), 3–11 (1995) 11. Osgood, C.E., Suci, G.J., Tannenbaum, P.H.: The Measurement of Meaning, University of Illinois Press, Urbana, IL (1957) 12. Hsiao, S.W., Huang, H.C.: A neural network based approach for product form design. Design Studies 23(1), 67–84 (2002)

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13. Kosaka, H., Watanabe, K.: A Kansei product design system using neural network. Kansei Engineering International 3(4), 31–36 (2003) 14. Agoston, G.A.: Color Theory and Its Application in Art and Design. Springer, Berlin (1987) 15. Coello Coello, C.A.: An updated survey of GA-based multiobjective optimization techniques. ACM Comput. Surv. 32(2), 109–143 (2000)

A Testing Device for the Human Ability to Utilise Beneficial Environmental Features Blagovest Vladimirov, Hiromi Mochiyama, and Hideo Fujimoto Nagoya Institute of Technology, Nagoya, Japan [email protected] Abstract. In this paper, we propose a device for testing the human ability to utilise, even unconsciously, beneficial environmental features. An important characteristic of the proposed testing device is that additional stimuli, correlated to the correct task outcome, are introduced during the test without informing the subject. The device tests whether the subject will notice the subtly provided stimuli and use them, consciously or not, to improve the task performance. The use of these additional stimuli is confirmed by observing a sudden drop in the subject’s performance (slump-like effect) when the additional stimuli are reversed, i.e., correlate to the wrong task outcome. We present several facts suggesting that the ability tested by the proposed device supports the human skill acquisition. An illustrative example of a specific implementation of the proposed device, based on a visual alternative choice task with additional audio stimuli, is presented to explain the testing process.

1

Introduction

Many high-quality products and services rely on the availability of skilled professionals. The competitiveness and the success of product or service companies depend on the ability to find skilled employees, emphasising the need of better understanding and reliable assessment of the desired employee characteristics. The purpose of this research is to develop a device for testing human skill acquisition potential (Fig. 1). Such device could help identifying prospective employees.

Fig. 1. A testing device for identifying subjects that show skill acquisition potential

In practice, general personality aspects of potential employees have been assessed with various tests like Synthetic Personality Inventory (SPI)1 , etc. In 1

http://www.recruit-ms.co.jp

H.G. Okuno and M. Ali (Eds.): IEA/AIE 2007, LNAI 4570, pp. 1180–1189, 2007. c Springer-Verlag Berlin Heidelberg 2007 

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addition to general cognitive abilities, Ackerman [1] investigated also the relation between perceptual speed and psycho-motor abilities, and performance in Air Traffic Control task, which is representative of the class of short term skill acquisition tasks. However, compared to the general abilities mentioned above, abilities that are more closely related to the skill acquisition process itself have not received enough attention. In this paper, we propose a device for testing the human ability to utilise, even unconsciously, task relevant, beneficial environmental features for performance improvement. A specific implementation of the proposed device is presented in the next section, together with an example illustrating the testing process. A discussion of the idea behind the proposed device and its relation to the human skill acquisition process follows in the third section. The last section concludes with comments on the current results and directions for further development.

2

Testing Device and Illustrative Example

Here, we describe a specific implementation of the proposed testing device based on an alternative choice task, and present an illustrative example of the test. First, the device and the task are described as seen from the tested subject’s point of view. Then, follows an explanation of the behind-the-scene part that shows the actual idea of the test. Finally, we give an example illustrating the testing process and interpretation of the results. 2.1

Device and Task from the Subject’s Point of View

Device. Figure 2a shows the configuration of the proposed device as seen by the tested subject. The device consists of a computer with a program implementation of an alternative choice task, a visual display (monitor) and a keyboard. Task. The task to be performed is Bounce-or-not task, in which one of two possible motion patterns is presented on the visual display. The subject is asked to guess which pattern was shown and to reply with a key-press. The two possible motion patterns, Bouncing and Not Bouncing, are shown in the left part of Fig. 2b. Each motion pattern is illustrated with drawings showing the positions of the moving objects. In the top row, at time t0 , two discs are shown in the upper corners of the screen. Then, these two discs move at the same speed along the diagonal lines toward the opposite corners. The numbers and arrows are added here only to illustrate the discs’ trajectories, and are not shown to the subject. In the middle row, at time tc , the discs overlap at the centre of the screen. In the bottom row, at time tf , the final positions of the discs are different for the two motion patterns. In the Bouncing pattern, after bouncing off one another at time tc , the discs end in the corners on the same side of the screen as their initial positions. In the Not Bouncing pattern, the discs stream through at time tc and end in the corners on the opposite side of the screen.

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a)

b)

Fig. 2. a) The testing device, as seen by the subject, is a computer implementation of an alternative choice task. The subject has to recognise the motion pattern presented on the video display and to respond with a key-press. b) Explanation of the Bounce-ornot task, given to the subject. The two possible motion patterns, shown on the left, are Bouncing and Not Bouncing. A typical test pattern uses similar colours for the discs, as shown on the right. The task is to recognise whether the discs in the test patterns have bounced or not.

The two discs are displayed in colour. When the difference between the discs’ colours is large, as shown in the two patterns on the left in Fig. 2b, distinguishing bouncing from not bouncing is easy. The actual patterns presented in the test, as the one illustrated on the right in Fig. 2b, use small distance between the disc’s colours, making the task much more challenging. Procedure. During the test, randomly selected motion patterns are presented in a sequence and the subject is asked to guess correctly as many as possible. At the end of each sequence, the proportion of correct answers is calculated and shown to the subject. 2.2

Behind-the-Scene Part of the Device and the Task

Device. Figure 3a shows the full configuration of the testing device. This configuration includes an audio display (speakers) that is placed away from the subject’s view. The subject is not informed about the speakers and their role in the test. Task. The Bounce-or-not task also contains details that are hidden from the subject. As shown in Fig. 3b, besides the motion patterns presented on the display, for some patterns a barely audible beep sound is introduced at time tc without informing the subject. Thus, task relevant information is communicated to the subject in two ways: openly and subtly. The openly provided information is presented through the visual display, and the subtly introduced information is presented through the audio display.

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Fig. 3. a) An audio display, placed away from the subject’s view, is added to the testing device. b) In some trials, an additional beep sound is introduced to the Bounce-or-not task at time tc .

Procedure. The actual idea is to test whether or not the subject will notice the subtly introduced beep sound among the various other background stimuli coming from the environment and utilise it to improve the task performance. Initially, the subject is tested only on motion patterns, without using the beep sound, until the task performance is stabilised, i.e., three consecutive performance measurements are within certain small interval. Then, without informing the subject, a beep sound is added only when the discs bounce and again the test continue until the performance is stabilised. In the testing device, a t-test is used to compare these two task performances. A statistically significant improvement of the performance after adding the sound leads to the assumption that the subject has used the additional sound information. To confirm this assumption, again without informing the subject, the test continues with a beep sound only when the discs stream through. If this results in significantly below chance level task performance, then the assumption that the subject has used the beep sound information is correct. In this case, the testing device accepts the subject, i.e., classifies the subject as having shown ability to utilise beneficial environmental features without being explicitly instructed to do so. Such subject is considered as having skill acquisition potential. On the other hand, if the subject’s performance doesn’t improve after adding a beep sound when the discs bounce, the conclusion is only that the subject didn’t show the above ability and not that the subject doesn’t possess it. In this case, the testing device rejects the subject. 2.3

Illustrative Example

Test Parameters. The colour of the disc starting from the top right corner was held constant yellow with RGB value of (255/255/0). The colour of the other disc was varied between RGB (250/250/0) and RGB (210/210/0) by

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changing the red and green components in steps of 10, obtaining five levels of task difficulty. The colour distances corresponding to each difficulty level were d = (0.02, 0.06, 0.1, 0.14, 0.18) after normalisation. The significance level of the t-test was set to P = 0.01. One female and two male subjects in their early 20’s participated in the test. The tested subjects were instructed to watch the displayed motion patterns and to answer with a key-press whether the discs had bounced or not. The test consisted of several sessions, each one having 10 blocks of trials. In one block, 60 or 30 trials were performed sequentially, with one trial consisting of displaying a motion pattern and receiving an answer. In each block of trials, the task difficulty was held constant. Each session started with the minimum task difficulty level, gradually increasing it to the maximum level, and decreasing it again to the minimum. The proportion of correct answers was calculated for each block of trials and was shown to the subject at the end of the block. Test Results. Figure 4 shows the task performance of the subject that was accepted by the testing device. The three lines in the graph represent the proportion of correct answers in the three cases of the task: when no beep sound is used (no sound ), with beep sound only when the discs bounce (with sound ), and with beep sound only when the discs stream through (reversed sound ). The points show the mean value of the proportion of correct answers and the bars show the standard deviation. The horizontal axis shows the distance d between the colours of the two discs, which corresponds to the task easiness. Compared to the case without sound, in the case in which similarly to the physical phenomenon there is a beep sound when the discs bounce, the proportion of correct answers significantly (P < 0.01) increased. This suggests that the subject has used the beep sound information even though it was not explicitly mentioned in the task instructions and was introduced without informing the subject. In other words, this is an example of the skill related ability to utilise beneficial environmental features to improve the task performance. In the case in which there is a beep sound only when the discs stream through, for the most difficult level d = 0.02, the proportion of correct answers is significantly (P < 0.003) below the chance level. This result confirms that the performance improvement in the previous case is due to the beep sound information. The test settings are such that if the subject consciously perceives the sound and relies on it, the maximum proportion of correct answers can be achieved. But from Fig 4 we can see that the subject’s performance in the no sound case is not the maximum possible. This and the observed below chance-level performance suggest that the beep sound information may have been used at a level below the conscious experience. Figure 5 shows the task performance of the subjects that were rejected by the testing device. For these two subjects, there are no significant differences between the proportion of correct answers in the two cases: no sound and with sound. Also, there was no significant decrease of the task performance in the reversed sound case. These results suggest that the subjects didn’t use the beep sound information in their decisions.

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Fig. 6. Performance of the accepted (pass) and the rejected (fail) subjects in the no sound and with sound cases for colour distance d = 0.02

We can see from Fig. 4 and Fig. 5 that one of the rejected subjects has shown similar performance level to that of the accepted subject for colour distance d >= 0.06 in the with sound case. If the testing device relied only on the absolute task performance, both subjects should have been accepted. But since we are interested in the subject’s skill acquisition ability, the testing device is based on detecting specific improvements of the task performance. Thus, to be

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accepted by the testing device, the subject has to show significant improvement of the performance in the with sound case compared to no sound case, i.e., to show ability to utilise beneficial environmental features to improve the task performance without being instructed to do so. Figure 6 gives another view of the subjects’ performance in the no sound and with sound cases for colour distance d = 0.02. The horizontal axis shows the number of block of trials and the vertical axis shows the proportion of correct answers in the corresponding block. We can see an improvement of the accepted subject’s performance starting after 10 block of trials, while the performance of the rejected subjects doesn’t improve. The accepted subject was able to utilise the additional sound information to overcome the initial asymptotic task performance. 2.4

Generalised Form of the Device

The device implementation presented above was based on Bounce-or-not task and used visual and audio stimuli. Other implementations can be obtained using different tasks and different stimuli, providing that the following essential characteristics of the device and the task are retained. Generally, the device serves as an interface through which the subject performs a given task. There are two or more task relevant stimuli that are necessary to achieve good performance. In its role as an interface, the device gives us control over the relevant stimuli that are presented to the subject. We should be able to control which stimuli are presented and their intensity level. The device, also, computes appropriate task performance based on the presented stimuli to the subject and the subject’s response. Using the device, we prepare three test cases: normal, enhanced and reversed. In the normal task case, only a part of the relevant stimuli are presented to the subject. In the enhanced task case we add in a subtle manner some or all of the task stimuli that were held out in the normal case. In the reversed task case the stimuli added in the enhanced case are reversed so that they would trigger incorrect responses to the task. The testing procedure is similar to that in the illustrative example. At the beginning, the subject is tested with the normal test case until the performance stabilises. Then, without informing the subject, the test continues with the enhanced case. The performance in these two test cases is compared. If there is statistically significant improvement of the performance in the enhanced case, the test continues with the reversed case to confirm that the observed improvement is due to the use of additional information provided by the subtly introduced task stimuli. A drop in the performance after reversing these stimuli confirms that the subject indeed used them. Because of the observed sudden drop in the subject’s performance, we call this a slump-like effect. This effect is more pronounced when the additionally introduced stimuli is used unconsciously. Finally, the device accepts a subject that is able to improve his task performance by utilising these additional stimuli. The decision for including beep sound in the Bounce-or-not task is based on the effect of sound on visual perception discussed by Sekuler [2] and Shimojo [3].

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Due to our experience with bouncing as a physical phenomenon, where bouncing is often correlated with a sound, we have already acquired the relation between sound and bouncing. This reduces the time requirements of the experiment, since the subjects don’t have to learn this relation but only to notice and use, consciously or not, the subtly introduced sound information.

3

Discussion on the Idea Behind the Proposed Testing Device

This section presents a discussion on the idea behind the proposed testing device in relation to the implicit part of the skill acquisition process, a stepwise character of the performance improvement, and a general difference between humans and machines. 3.1

Implicit Learning

Children learn to speak their mother tongue without any formal understanding of its grammar. They can speak more or less correctly, as if they follow the language rules, while at the same time they are not aware of the rules themselves. This kind of knowledge and its acquisition are subjects of the research on implicit learning [4]. Similar observation can be made in the case of skilled practitioners. They perform very well on given tasks but sometimes have difficulties to state explicitly how they achieve it. This suggests that implicit learning also plays a role in the skill acquisition process. In the proposed testing device, we address an important aspect of the learning process, namely, noticing and utilising relevant information that helps to improve the task performance. Further experiments with testing device based on different tasks are considered to investigate the implicit part of the skill acquisition process. 3.2

Skill Acquisition as Stepwise Performance Improvement

Posner [5] distinguishes “cognitive”, “associative”, and “autonomous” phases in the skill acquisition process. In the initial “cognitive” phase, the individuals acquire understanding of the task, which information is important and which is not, what actions are appropriate, etc. In the second “associative” phase, a coherent strategy and behaviour are formed, that result in acceptable level of performance. In the third “autonomous” phase, the individuals achieve a level in which the skill performance require little or no conscious control. Usually, the initial phases are associated with major performance improvements, while at the last phase, an asymptotic performance level is achieved with little further improvements. The Ericsson’s theory of expertise [6,7,8] explains the role of deliberate practice in avoiding full automaticity and overcoming the asymptotic performance level. The deliberate practice is characterised with active attempts to go

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Fig. 7. Overcoming asymptotic performance

beyond the current performance level and often with the use of specific training situations. This results in refining the existent and forming new mental representations, improving the control over the task performance. Based on the above positions, we view the skill acquisition process, as a repeating cycle of finding new or improved mental representations, adjusting the task related decisions and actions to utilise the new information, rehearse the newly created associations, and look again for another possibility for improvement. This can be illustrated with the curve in Fig. 7, showing a point of overcoming an asymptotic performance with initial accelerated performance improvement and setting for another asymptotic level. The human abilities that facilitate and maintain this process of stepwise improvements are essential for the skill acquisition. We consider individuals as having skill acquisition potential if they are able to overcome such asymptotic performance levels on their own, i.e, without being explicitly instructed how to do so. Based on the idea that the ability to notice and utilise beneficial environmental features among the information coming through our senses is a general factor for task performance improvement, we can use the proposed testing device for testing skill acquisition potential. 3.3

Intelligence in Humans and Devices

If we consider using the proposed testing device to test robots instead of human subjects, an important advantage of humans comes to attention. This advantage is related to the frame problem [9], or more precisely to that, unlike robots, humans are able to overcome this problem. The frame problem arises from the need to be able to distinguish which information is important for achieving a given task and which is not. Even if we consider robots with learning capabilities, currently it is the humans that have to decide which sensory inputs and in what form should be fed to the learning algorithm to achieve good results in acceptable time limits. In that context, the proposed testing device doesn’t attempt to overcome the frame problem but to test for human abilities that allow them to overcome that problem, in this case, the ability to find and utilise task related information that supports better performance.

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Conclusion

In this paper we proposed a device for testing the human ability to utilise beneficial environment features for skill improvement. We presented a device implementation based on Bounce-or-not task, using combinations of visual and audio stimuli. An illustrative example of the testing process shows the device capturing successfully the ability to utilise beneficial environmental features for task performance improvement. The importance of that ability for skill acquisition suggests that the proposed device can be used for testing skill acquisition potential. The presented device implementation gives one possibility to test for skill acquisition potential. Further experiments with adapting the generalised form of the proposed device to different tasks and sensory modalities are necessary to investigate the reliability and the feasibility for practical application. Device variants for both cognitive and motor skills are also considered.

References 1. Ackerman, P.L., Cianciolo, A.T.: Cognitive, perceptual speed, and psychomotor determinants of individual differences during skill acquisition. Journal of Experimental Psychology: Applied 6, 259–290 (2000) 2. Sekuler, R., Sekuler, A.B., Lau, R.: Sound alters visual motion perception. Nature 385, 308 (1997) 3. Watanabe, K., Shimojo, S.: When sound affects vision: Effects of auditory grouping on visual motion perception. Psychological Science 12, 109–116 (2001) 4. Cleeremans, A., Destrebecqz, A., Boyer, M.: Implicit learning: news from the front. Trends in Cognitive Sciences 2(10), 406–416 (1998) 5. Fitts, P.M., Posner, M.I.: Human performance. Brooks/Cole (1967) 6. Ericsson, K.A., Krampe, R.Th., Tesch-R¨ omer, C.: The role of deliberate practice in the acquisition of expert performance. Psychological Review 100(3), 363–406 (1993) 7. Ericsson, K.A., Charness, N.: Expert performance: Its structure and acquisition. American Psychologist 49(8), 725–747 (1994) 8. Ericsson, K.A.: Attaining excellence through deliberate practice: Insights from the study of expert performance. In: Ferrari, M. (ed.) The pursuit of excellence in education, pp. 21–55. Erlbaum, Hillsdale, N.J (2002) 9. McCarthy, J., Hayes, P.: Some philosophical problems from the standpoint of Artificial Intelligence. In: Meltzer, B., Michie, D. (eds.) Machine Intelligence 4, pp. 463–502. Edinburgh University Press (1969)

Author Index

Abar, Sameer 1078 Abdulla, Waleed H. 473 Alpay, S ¸ erafettin 334, 364 Bai, Shih-Ming 155 Bannister, Peter R. 591 Barbosa, Carlos R. Hall 552 Bojduj, Brett 786, 984 Boongasame, Laor 935 Boonjing, Veera 935 Bosse, Tibor 621 Bouziri, Ahlem 454 Chai, Soo H. 145 Chan, Po-Chou 185 Chang, Cheng-Yuan 611 Chang, Chir-Ho 424 Chang, Jen-Chieh 176 Chang, Jing-Rong 735 Chang, Yaotsu 915 Chang, Yau-Zen 464 Chang-Chien, Tsai-Lu 84 Cheah, Wooi Ping 374 Chen, Chen-Yuan 493 Chen, Cheng-Hung 207 Chen, Cheng-Wu 493 Chen, Jiah-Shing 217 Chen, Jr-Chang 1118 Chen, Liang-Yu 217 Chen, Ruey-Maw 655, 715 Chen, Rung-Ching 1148 Chen, Shi-Huang 915 Chen, Shyi-Ming 74, 155, 238, 745 Chen, Stephen 313 Chen, Su-Ping 1148 Chen, Yu-Pin 1052 Chen, Yung-Fu 185, 766 Cheng, Ching-Hsue 735 Cheng, Chung-Chuan 185 Cheng, Shu-Chen 834 Chiou, Yih-Chih 195 Chiu, Chuang-Cheng 541 Chiu, Tzu-Fu 886 Chiu, Yu-Ting 886

Cho, Hyunbo 572 Cho, Jae Hyung 1012 Cho, Sang-Young 992 Cho, Sung-Bae 135, 444, 725, 796 Cho, Tae Ho 64 Cho, Ung-Keun 444 Choi, Bumghi 696 Choi, Hyung Rim 1012 Choi, Sook-Young 374, 521 Chou, Chang-Min 611 Chueh, Chung-Huei 1127 Chung, I-Ling 611 Chung, S.L. 227 Chung, Yoojin 992 Clifton, David A. 591 Conteras, Roxana J. 552 Cooper, Eric W. 1169 Da Cruz, Andr´e V.A. 552 Daskalopulu, Aspassia 963 Din, Der-Rong 1138 Doncescu, Andrei 395 Ejnarsson, Marcus 511 Escobar-Jeria, V´ıctor H.

483

Fan, Pingzhi 303 Figueiredo, Karla 552 Fujimoto, Hideo 1180 Fukuta, Naoki 354 Furukawa, Tomonari 1032 Gerritsen, Charlotte 621 Giannikis, Georgios K. 963 Goda, Shinichi 896 Granmo, Ole-Christoffer 845 Guo, Dongfen 1108 Hanpin, Wang 644 Hao, Pei-Yi 756 Haraguchi, Kazuya 776 Hashem, Khaled 813 Hirose, Yasuo 905 Hong, Chao-Fu 886 Hong, Jin-Hyuk 444

1192

Author Index

Hong, Tzung-Pei 675 Horng, Shi-Jinn 303 Hsieh, Fu-Shiung 1072 Hsieh, Yi-Chih 1002 Hsu, Jiin-Chyr 766 Hsu, Shun-Chin 1118 Huang, Chih-Hui 823 Huang, Jen-Peng 562 Huang, Wen-Chen 84 Huang, Yueh-Min 655, 715 Huang, Yung-Fa 166 Hwang, Chein-Shung 1052 Hwang, Fuh-Hsin 611 Hwang, Keum-Sung 135 I-Kai Wang, Kevin 473 Ichige, Koichi 905 Inoue, Katsumi 395 Ito, Takaichi 806 Ito, Takayuki 354 Iwaya, Yukio 1078 Jeong, Buhwan 572 Jhou, Jhen-Jie 834 Ji, Genlin 23 Jiang, Xiaoyi 766 Jing, Xu 865 Jo, Geun-Sik 876 Jung, Jason J. 876 Jung, Myung-Chul 796 Jung, Sung-won 665 Kaikhah, Khosrow 705 Kamei, Katsuari 1169 Kang, Byoungho 531 Kang, Mi-young 665 Kang, Moo Hong 1012 Kang, S.H. 227 Kang, Tae-Koo 405 Kao, Tzong-Wann 303 Kawahara, Tatsuya 126 Kiewra, Maciej 1061 Kim, Dongwon 405 Kim, Eunjung 1042 Kim, Hyun-Don 280 Kim, Hyun Soo 1012 Kim, Ji-Soo 105 Kim, Kyoung-Yun 374, 521 Kim, Kyung-Joong 135, 796 Kim, S.H. 105

Kim, Sang-Woon 845 Kim, Soo-Hyung 521 Kim, W.P. 105 Kim, Yong-Dae 1042 Kinoshita, Tetsuo 1078, 1088 Kinoshita, Yuichiro 1169 Kitani, Naho 855 Ko, Yaun-Kai 238 Komatani, Kazunori 280, 384 Konno, Susumu 1078 Kr´ ol, Dariusz 260 Kulvatunyou, Boonserm 572 Kuo, Huang-Cheng 562 Kuo, I-Hong 303 Kuo, Li-Ching 176 Kurfess, Franz 984 Kwon, Hyuk-chul 665 Lan, Guo-Cheng 562 Lasota, Tadeusz 260 Lavis, Benjamin 1032 Lee, Chi-Yung 207 Lee, Chin-Ling 823 Lee, Daewon 572 Lee, Hae Young 64 Lee, Hyung-Jae 374, 521 Lee, Je-Hoon 1042 Lee, Ju-Hong 696 Lee, Li-Wei 745 Lee, Minna 992 Lee, Ya-Ting 735 Lee, Yunsik 601 Leung, Ho-fung 935 Li, Chi-Hsiang 766 Li, Meiyin 925 Li, Tieke 1108 Li, Xiaolu 1088 Li, Zheng 415 Liao, Chien-Ping 250 Liao, Shu-Ying 735 Lifu, Wang 644 Lim, Joon S. 145 Lim, Jun-Seok 582 Lim, Sungsoo 725 Lin, Cheng-Jian 207, 823 Lin, Chiu-Hsiang 250 Lin, Chu-Hsing 176 Lin, Chun-Wei 675 Lin, Hsiu-Pi 84 Lin, Hsuan-Hung 766

Author Index Park, Gwi-Tae 405 Park, Nam Kyu 1012 Park, Tae-Su 696 Peng, Yucheng 415 Polyakovsky, Sergey 1098

Lin, Jin-Ling 424 Lin, Mu-Hua 886 Lin, Pei-Chun 270 Lin, Shouxun 925 Lin, Shu-Hao 493 Lin, Tsung-Lieh 303 Lin, Yao-Tang 217 Lin, Yen-Hsiu 756 Liou, Bing-Hong 834 Liu, Jung-Chun 176 Liu, Qun 925 Liu, Rey-Long 44 Liu, Ying 33 Liu, Yongge 865 Lo, Shih-Tang 655, 715 Loh, Han Tong 33 Lu, Wen-Hsiang 675 Luh, Guan-Chun 1127 Lupien, Vincent 313

Qian, Yueliang Qu, Weiguang

Randall, Marcus 974 Rau, Hsin 250 Razzaqi, Sarah 313 Ryu, Kwang Ryel 531

M’Hallah, Rym 454, 1098 Maemura, Takahide 1088 Maeno, Yoshiharu 806 Maki, Atsuto 94 Mart´ın-Bautista, Mar´ıa J. 483 Matsuo, Tokuro 953 Matsuyama, Takashi 94 Merckel, Loic 324 Misu, Teruhisa 126 Mochiyama, Hiromi 1180 Mohammad, Yasser F.O. 115 Motoda, Hiroshi 12 Nagamochi, Hiroshi 776 Nakadai, Kazuhiro 905 Nakano, Mikio 905 Nalepa, Wojciech 260 Nguyen, Ngoc Thanh 1061 Nilsson, Carl Magnus 511 Nishida, Toyoaki 115, 324 Nishino, Masaaki 94 Ogata, Tetsuya 280, 384 Ohsawa, Yukio 806, 896 Okuno, Hiroshi G. 280, 384 Oommen, B. John 813, 845 ¨ urk, Pinar 291 Ozt¨ Pacheco, Marco Aur´elio C. Park, Dong-Chul 601

925 23

552

Salcic, Zoran 473 Samuel, Mathew 845 S´ anchez, Daniel 483 Selim, Hasan 54 Shen, Jine-Lih 493 Shiang, Wei-Jung 250 Someya, Hiroshi 434 Son, H.J. 105 Song, Heejun 405 Sui, Zhifang 23 Sumiya, Ryota 905 ´ Swirski, Konrad 1022 Takeda, Ryu 384 Tan, Tan-Hsu 166 Tanaka, Katsumi 1 Tao, Jia 644 Tarassenko, Lionel 591 Taylor, Dennis 984 Tidemann, Axel 291 Ting, Ping-Ho 166 Topaloglu, Seyda 54 Trawi˜ nski, Bogdan 260 Treur, Jan 621 Truong, T.K. 915 Tsai, Chieh-Yuan 541 Tsai, Chung-Hung 493 Tsai, Meng-Hsiun 185 Tsai, Meng-Ru 195 Tsai, Zhi-Ren 464 Tsujino, Hiroshi 905 Turner, Clark S. 786 Uchiya, Takahiro Uehara, Kuniaki

1088 855

Vellasco, Marley Verikas, Antanas

552 511

1193

1194

Author Index

Vila, Mar´ıa-Amparo 483 Vladimirov, Blagovest 1180 Wang, Chen-Shu 501 Wang, Hui-Yu 74 Wang, Jenhung 270 Wang, Xiangdong 925 Weber, J¨ org 633 Wee, H.M. 227 Wen, Zhao 644 Whangbo, Taeg-Keun 344 Wojdan, Konrad 1022 Wotawa, Franz 633 Wu, Chienwen 685 Wu, Chih-Hung 675 Yamamoto, Shun’ichi 384 Yamamoto, Yoshitaka 395 Yan, Jianfeng 865 Yang, H.J. 105 Yang, Heng-Li 501, 943

Yang, Hsiao-Fang 943 Yang, Hyung-Jeong 374, 521 Yang, P.C. 227 Yang, Sheng-Yuan 1158 Yang, Tai-Ning 1118 Yasumura, Yoshiaki 855 Yavuz, Mahmut 334 Yeh, Ken 493 Yen, Shi-Jim 1118 Yoo, Kwan-Hee 1042 You, Peng-Sheng 1002 You, Younggap 1042 Yu, Shiwen 23 Zhan, Liangliang 415 Zhang, Yang 865 Zhang, Yongchuan 415 Zhong, Yuan-Heng 185 Zhou, Jianzhong 415 Zhou, Junsheng 23