Multi-Agent Rationality: 8th European Workshop on Modelling Autonomous Agents in a Multi-Agent World, MAAMAW'97, Ronneby, Sweden, May 13-16, 1997, Proceedings (Lecture Notes in Computer Science)

Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science Edited by J. G. Carbonell and J. Siekmann

Lecture Notes in Computer Science Edited by G. Goos, J. Hartmanis and J. van Leeuwen

1237

Magnus Boman Walter Van de Velde (Eds.)

Multi-Agent Rationality 8th European Workshop on Modelling Autonomous Agents in a Multi-Agent World, MAAMAW'97 Ronneby, Sweden, May 13-16, 1997 Proceedings

Springer

Series Editors Jaime G. Carbonell, Carnegie Mellon University, Pittsburgh, PA, USA J6rg Siekmann, University of Saarland, Saarbriicken, Germany

Volume Editors Magnus Boman DSV, Stockholm University andThe Royal Institute of Technology Electrum 230, S-16440 Kista, Sweden E-mail: [email protected] Walter Van de Velde Artificial Intelligence Laboratory Vrije Universiteit Brussel Pleinlaan 2, B-1050 Brussels, Belgium E-mail: walter @ arti.vub.ac.be Cataloging-in-Publication Data applied for

Die Deutsche Bibliothek - CIP-Einheitsaufnahme

Multi-agent rationality : proceedings / 8th European Workshop on Modelling Autonomous Agents in a Multi-Agent World, MAAMAW '97, Ronneby, Sweden, May 13 - 16, 1997. MagnusBoman ; Walter van de Vetde (ed.). - Berlin ; Heidelberg ; New York ; Barcelona ; Budapest ; Hong Kong ; London ; Milan ; Paris ; Santa Clara ; Singapore ; Tokyo : Springer, 1997 (Lcetu~ note~ in computer science ; Vol. 1237 : Lecture not~ in artificial intelligence) ISBN 3-540-63077-5

CR Subject Classification (1991): 1.2, C.2.4, D.1.3 ISBN 3-540-63077-5 Springer-Verlag Berlin Heidelberg New York This work is subject to copyright. All rights are reserved, whether the whole or part of the materiat 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 -Verlag. Violations are liable for prosecution under the German Copyright Law. 9 Springer-Verlag Berlin Heidelberg 1997 Printed in Germany Typesetting: Camera ready by author SPIN 10548791 06/3142 - 5 4 3 2 1 0

Printed on acid-free paper

Table of Contents I n v i t e d T a l k s (Abstracts) Market-Aware Agents for a Multiagent World Michael P. Wellman Learning and Adoption Versus Optimization Yuri M. Ermoliev

2

Limits of Strategic Rationality for Agents and M-A Systems Cristiano Castelfranchi

3

Papers Multiagent Coordination in Antiair Defense: A Case Study Sanguk Noh and Piotr J. Gmytrasiewicz

4

A Service-Oriented Negotiation Model between Autonomous Agents Caries Sierra, Peyman Faratin, and Nick R. Jennings

17

Norms as Constraints on Real.Time Autonomous Agent Action Magnus Boman

36

Distributed Belief Revision vs. Belief Revision in a Multi-Agent Environment: First Results of a Simulation Experiment Aldo Franco Dragoni, Paolo Giorgini, and Marco Baffetti

45

Multi-Agent Coordination by Communication of Evaluations Edwin de Jong

63

Causal Reasoning in Multi-Agent Systems B. Chaib-draa

79

The Reorganization of Societies of Autonomous Agents Norbert Glaser and Philippe Morignot

98

Adaptive Selection of Reactive/Deliberate Planning for the Dynamic Environment Satoshi Kurihara, Shigemi Aoyagi, and Rikio Onai

112

Distributed Problem-Solving as Concurrent Theorem Proving Michael Fisher and Michael Wooldridge

128

Commitments Among Autonomous Agents in Information-Rich Environments Munindar P. Singh

141

VI

Making a Case for Mulli-Agent Systems Fredrik Ygge and Hans Akkermans

156

Learning Network Designs for Asynchronous Teams Lars Baerentzen, Patricia Avila, and Sarosh N. Talukdar

177

Building Multi-Agent Systems with CORRELATE Wouter Joosen, Stijn Bijnens, Frank Matthijs, Bert Robben, Johan Van Oeyen, and Pierre Verbaeten

197

Modelling an Agent's Mind and Matter Catholijn M. Jonker and Jan Treur

210

Delegation Conflicts Cristiano Castelfranchi and Rino Falcone

234

Multi-Agent Rationality: Preface Magnus Boman* & Walter Van de Velde** *DECIDE, DSV, StockholmUniversity **AI Lab, Vrije Universiteit, Brussels

Theme The theme of MAAMAW workshops has never been crucial to their success. Nevertheless, it has always played a part in the vision of the near future, as presented by the respective scientific co-chairs. We decided that multi-agent rationality would be a challenging and inspiring theme for MAAMAW'97. The challenge lies in the fact that a transition from studies of individual rationality to studies of group rationality is currently taking place, and it is a rocky road. Artificial agents have been making decisions for some years now. Their guiding principle has been that of maximising their expected utility (PMEU, for short), although one cannot ignore the influence of rival principles. There are indeed good reasons for a utilitarian focus on individual rationality. To many end users agents are merely a metaphor for personalised decision support embodied in personal digital assistants, web crawlers, or intelligent filters. However, the multi-agent systems (MAS) community seems to hold the opinion that PMEU cannot be the operational definition of rationality that Newell sought in his oR-cited AAM address. Wherever there is room for co-operation and co-ordination, social issues must come into play. In our immediate future lie artificial decision makers facing sequences of heterogeneous and complex decision situations, perhaps even in real time. Their rationality might be explicated by their capacity for synthesising results from evaluations that employ different evaluation functions. The extent to which their analyses governs their behaviour will vary, but the representation and modelling of their social context (in terms of their place in the MAS, in their particular coalition, and their alignments) is central. The number of treatises on the dependence of group rationality upon individual rationality is still small, and if the choice of theme for MAAMAW'97 can inspire more activity on this topic, the whole area should benefit. Invited

Speakers

The three speakers who have been invited were selected with particular regard to our workshop theme. Each of them represents an important and influential school of thought on the topic of multi-agent rationality. Abstracts of their talks are included in this volume. Michael P. Wellman (University of Michigan) has for several years been investigating the use of economic principles for studying economically rational agent behaviour, coining the term market-oriented programming in the~process. This interdisciplinary sub-area has produced models for several multi-agent applications, and is currently one of the fastest growing research topics in the field of computer science. Of special interest to the theme is the speaker's positive stance on how successful economic rationality can be in real-life

viii MAS applications.

Yuri Ermoliev (IIASA,Vienna) is one of the founders of the area of stochastic optimization, and in recent years he has used multi-agent systems for simulating decisions under risk. He is perhaps best known for his pioneering work in economics, some of which was done with Brian Arthur, and now widely accepted. Even though this work questioned some of the foundations of economics, the speaker's view on the promises of crossing economics with computer science are not entirely negative. He is more sceptical, however, about the coupling of physics and MAS as a guarantee of rational agent behaviour. Cristiano Castelfranchi (CNR Institute, Rome) should be well known to most readers of this volume. For about ten years he has stressed the importance of studying the social aspects of agent communication and behaviour in MAS. In recent years he has expressed doubts about the hopes that many researchers hold for formal theories, notably game theory, in governing and explaining rational behaviour in MAS. His criticism extends to overconfidence in the concept of economic rationality, and so in a sense completes the picture of the invited speakers' views.

Papers Labelling papers can be difficult, but classifying papers for a scientific meeting can greatly assist the attendees. Unfortunately, it can also create confusion by clustering papers unnaturally, which can negatively affect the composition of sessions. Below therefore, papers are briefly introduced in what is hoped is a logical order, without trying to cluster them. Fifteen papers were chosen from a total of 51 submitted papers, a further fifteen of which were selected for poster presentation (not included here). One of the purposes of the theme was to try to clarify the importance of game theory to formal representation of autonomous agents in multi-agent systems: If there is a gap between the theory and its applications, how can this gap be bridged? In this light, "Multiagent coordination in antiair defense: a case study" must be classified as advocating the classical game-theoretic approach. The work could be classified as planning for autonomous agents, where the latter have a model of the other agents. Noh and Gmytrasiewicz base their studies of optimal behaviour on payoffmatrices and their model extends the matrices by probability distributions, allowing for PMEU to be applied. The paper "A service-oriented negotiation model between autonomous agents" follows a similar path, although the presentation and emphasis of the two papers varies considerably. Where Noh and Gmytrasiewicz focus on planning, Sierra, Faratin, and Jennings choose to focus on negotiations. They provide evidence for the fact that the game-theoretic assumptions can be extended without violating the tractability of the basic questions. This extension is more or less analogous to introducing subjective probability. The authors develop a formal model of negotiation with particular concern for tactics. By weighting the tactics and applying a variant of PMEU, agents are guided through the space of possible actions that ultimately define contracts with other agents. PMEU appears in its classical form in "Norms as constraints on real-time autonomous agent action" by Boman, although probabilities are augmented by credibility and reliability

IX reports concerning other agents in the MAS. The normative advice given by a decision module evaluating subjective agent reports can be overridden by norms, classified here into three types, acting as constraints on actions. Hence, rational behaviour in the group is not determined by PMEU alone, but by PMEU in conjunction with the set of relevant norms using an efficiently implemented algorithm which is part of a more ambitious anytime algorithm realising the evaluation model. Dragoni, Giorgini, and Baffetti also use credibility and reliability assessments in their paper "Distributed belief revision vs. belief revision in a multi-agent environment: first results of a simulation experiment". Instead of computing the group utility of actions, however, the authors let agents hold elections. These elections do not affect the individual agent assessments. Since the authors strive to keep the agent theories consistent, they face computational problems, such as when computing maximally consistent subsets of revised knowledge bases. Another computationally hard problem is the application of Dempster's combination rule (a non-classical rule), which effectively makes simulations the appropriate tool for strategy evaluation. Whether or not domain competence is a necessary condition for optimal behaviour has been debated for over 40 years. In his paper "Multi-agent coordination by communication of evaluations", de ,long investigates the extent to which local experts may direct other agents, whose competence is less dependent on the domain. In spite of the similarity with respect to the aim and the focus on coordination, the paper is very different to the paper by Noh and Gmytrasiewicz. Where those authors use game theory, de Jong uses so-called coordination signals that simulate real-life encounters by scalar numbers. The paper is also decidedly more related to machine learning than to planning. In "Causal reasoning in multi-agent systems", Chaib-draa recommends a relatively coarse scale for utilities, producing a model that can only give partial advice. The author prefers to stress the importance of graphically depicting cognitive maps (here called causal maps) of agent decision situations. It is further argued that the recursive modelling problem, tackled in the paper by Noh and Gmytrasiewicz, is of little practical importance in this context. This can perhaps be explained by the fact that the paper follows the AI tradition of centering around the representation of reasoning and leaving evaluation in the periphery. The same is true for "The reorganization of societies of autonomous agents", in which agents migrate between simple societies composed only of agents. Agents assume roles in their society, and are motivated to migrate by utility values pre-assigned to them as well as to the societies with which they interact. Glaser and Morignot argue that each society then establishes conventions, which encourage certain agents and punish others. Each agent also possesses four kinds of competence, which the agent itself quantifies. Its individual competence suggests its role in a particular society. The paper "Adaptive selection of reactive/deliberate planning for the dynamic environment" mixes reactive and deliberate planning with the purpose of controlling a real-world system for robot vision. Similar attempts at a synthesis between these two fundamentally different approaches were made earlier in the area of Artificial Life. The real-time application described by Kurihara, Aoyagi, and Onai is treated in part with methods developed within parallel programming, and in part with purely multi-agent methods.

The paper "Distributed problem-solving as concurrent theorem proving" by Fisher and Wooldridge shares this connection to parallel processing. In particular, the authors' vectorized approach to theorem proving is close to research usually carried out within concurrent constraint programming. As anyone familiar with different kinds of resolution knows, the operational choices made at runtime between different rules, such as unit resolution and hyperresolution, have a significant impact on computational efficiency. This fact has turned the question into a classic AI problem, crucial to distributed planning, for example, which the authors now relate to MAS techniques. During the last decade, parts of the theory of information systems have been merged successfully with parts of the theory of knowledge-based systems, and later that of agent-based systems. In his paper "Commitments among autonomous agents in information-rich environments', Singh takes this proposed merger further by investigating the connection between the extended transaction model and the organisational aspects of MAS inspired by sociology. Singh focuses on commitments and argues that the information systems community should take organisational structures more seriously, while the MAS community should perhaps take them less seriously. Specifically, the anthropomorphic interpretation of beliefs as mental states is shown to be unnecessary in the context of co-operative information systems. In "Making a case for multi-agent systems", Ygge and Akkermans investigate in depth Huberman and Clearwater's conclusion regarding the appropriateness of a MAS alternative to classical engineering methods. In comparison to the three previous papers, this position paper is more explicit in its connection to universally accepted non-MAS approaches. Impeccable from a methodological standpoint, the paper is a reminder of the careful analysis and the comparison to traditional methods that MAS methods have to withstand. An approach to distributed problem-solving with a simple form of learning is explained in "Learning network designs for asynchronous teams" by Baerentzen, Avila, and Talukdar. The very difficult problem of using historical information for solving new problem instances is attacked here by representing the problem using a form ofprobabilistic automata. The problem is further complicated by the fact that (only partly autonomous) agents make observations of earlier processes of problem solving. A full programming language environment for multi-agent applications is introduced by Joosen, Bijnens, Matthijs, Robben, Van Oeyen, and Verbaeten in "Building multi-agent systems with CORRELATE". Based on concurrent OOP, the language was developed mainly for agent-oriented programming. The long-term goal of the research project is to provide a platform for several such languages. Techniques for representing the dynamics of models include dynamic logic, cybernetics, and conceptual modelling. The representation of state in conceptual models is traditionally augmented with a temporal transition relation that captures the dynamics of the universe of discourse. In "Modelling an agent's mind and matter", Jonker and Treur extend this with a complete set of temporal rules. The authors connect to MAS by representing the lifespan between the birth and death of multiple agent objects. What is unique about their approach is that the authors break up the traditional duality that in conceptual modelling separates the object level from the meta level, and that in logic forms the basis for the division into syntax

• and semantics. The mind of the agent is instead seen as materialised, and its physical features modelled separately from the model of the physical world. Castelfranchi and Falcone cover many topics already mentioned in their paper "Delegation conflicts". They use the mind metaphor as did Jonker and Treur. They discuss the trade-offbetween autonomy and obedience as did Boman. They also discuss roles, as did Glaser and Morignot. The authors claim that an analytical theory of delegation and adoption is central to MAS, and to cooperation in particular. Delegation is explained in terms of adopting another agent's plan, and such adoption is argued to be rational also from the point of view of an artificial agent. The paper pays special attention to the possible conflicts resulting from such delegation. We note with pleasure (and not without surprise) that most of the papers are closely related to the theme, in spite of the fact that no measures were taken in the reviewing process to secure this. This indicates that problems associated with multi-agent rationality lie at the heart of MAS.

Acknowledgements We would like to thank those who were involved in the previous MAAMAW (documented in "Agents Breaking Away", edited by Van de Velde and Pertain, Volume 1038 of this series) for sharing their experiences with us. Thanks are due to Hel~ne Karlsson who assisted us considerably by keeping tabs on authors and papers. We also thank our sponsors Sydkraft, ABB Network Partner, and the City of Ronneby. Last but not least, MAAMAW'97 could not have happened without the exceptional effort put in by the organisational chair in Ronneby, Staffan Hiigg, and his staff and student volunteers. The program committee consisted of the following 26 researchers who, sometimes assisted by helpful associates, secured that each submitted paper was given at least three reviews. Magnus Boman John Campbell Cristiano Castelfranchi Helder Coelho Anne Collinot Yves Demazeau Aldo Dragoni Love Ekenberg Jacques Ferber Francisco Garijo Marie-Pierre Gleizes Rune Gustavsson Nick Jennings Wouter Joosen George Kiss

(Stockholm Univ/RIT, Sweden) (University College, London, UK) (Ist di Psicologia del CNR, Rome, Italy) (INESC, Technical Univ, Lisbon, Portugal) (LAFORIA-IBP, Paris, France) (LEIBNIZ/IMAG, Grenoble, France) (Univ di Ancona, Italy) (IIASA, Vienna, Austria) (LIRMM, Montpellier, France) (Telefonica, Madrid, Spain) (SMI, Toulouse, France) (IDE, Karlskrona/Ronneby, Sweden) (Queen Mary & Westfield Coil, London, UK) (Katholieke Univ, Leuven, Belgium) (Open Univ, Milton Keynes, UK)

• Judith Masthoff Jean-Pierre Mtiller JOrgen MOiler Eugenio Oliveira John Perram Jeffrey Rosenschein Donald Steiner Kurt Sundermeyer Jan Treur Walter Van de Velde Peter Wavish

(Philips Research, Eindhoven, Netherlands) (IliA, Neuchatel, Switzerland) (Univ Bremen, Germany) (Univ do Porto, Portugal) (Odense Univ, Denmark) (Hebrew Univ, Jerusalem, Israel) (Siemens AG, Munich, Germany) (Daimler Benz AG, Berlin, Germany) (Free Univ, Amsterdam, Netherlands) (Vrije Univ Brussel, Belgium) (Philips Research, Redhill, UK)

Market-Aware Agents for a Multiagent World Michael P. Wellman University of Michigan Ann Arbor, MI USA [email protected] http://ai.eecs.umich.edu/people/wellman/

Abstract. The title of this aptly named workshop envisions a world populated by numerous (presumably artificial) agents, acting and interacting autonomously, producing behaviors of complexity beyond our means to predict--hence the need for the modeling effort called for in MAAMAW's first "M". For the past several years, my research group has been exploring the idea of constructing engineerable multiagent worlds based on economic princples. In this "market-oriented programming" approach, we solve multiagent decision problems by (1) casting them in terms of assigning resources to production and consumption activities of the constituent agents, and (2) running the agents within a computational market price system to determine an equilibrium allocation. To date, we have tested this approach with applications to simple problems in transportation planning, distributed engineering design, and network information services. Current work is developing more complex models in these domains, as well as investigating further applications in allocation of computational resources, and provision of distributed information services in a digital library. In this talk, I provide an overview of the approach, including some underlying economic theory and some overlying computational issues. Examples from models we have developed illustrate fundamental concepts of the methodology, including: competitive vs. strategic behavior, intertemporal allocation through futures markets, and representing uncertainty through contingent goods. I will also discuss some new infrastructure for market-oriented programming: The Michigan Intemet AuctionBot is a configurable auction server implementing marketbased negotiation over the World-Wide Web. Users create auctions by filling in web forms specifying their desired auction characteristics, and the AuctionBot takes over: accepting bids, calculating prices and matches according to the specified auction rules, and notifying bidders of the results. The AuctionBot is now operating, at: http://anction.eecs.umich.edu/

Learning and Adoption Versus Optimization Y. M. Ermoliev IIASA A-2361 Laxenburg ermoliev @iiasa.ac.at

Abstract. The aim of the talk is to discuss the role of stochastic optimization techniques in designing learning and adaptive processes. Neural and Bayesian networks, path-dependent adaptive urn's processes, automation learning problems and agent based models are considered. A unified general framework of stochastic optimization is proposed enabling us to derive various known and many other adaptive procedures for such different classes of models. We emphasize similarities with natural evolutionary processes, but at the same time we show that this similarity may be misleading when we deal with man-made systems. The "particles" (economic agents, enterprises, countries) of such systems do not follow strong laws like the laws in mechanics and physics (for instance, gravity law). Economic "particles" have flexibility to choose different behavioral patterns (policies, decisions). The uncertainty is a key issue in the modeling of anthropogenic systems and the main methodological challenge is to address related uncertainties explicitly within overall risk-based decision making processes. Purely myopic, trial and error approaches may be expensive, time consuming and even dangerous because of the irreversible nature of decisions. The decisive feature of man-made systems is their ability to anticipate and affect possible future outcomes. Approaches facilitating our ability for making decisions in the presence of uncertainties and related risks are discussed.

Limits of Strategic Rationality for Agents and M-A Systems Cristiano Castelfranchi National Research Council - Institute of Psychology Division of "AI, Cognitive Modelling, and Interaction" PSCS-Social Simulation Project Viale Marx, 15 - 00137 Roma - ITALY tel +39 6 860 90 518 / 82 92 626 fax +39 6 82 47 37 E-mail: cds @pscs2.irmkant.rm.cnr.it

Abstract. While AI adoption of the Game-Theoretic paradigm is found motivated, it is shown to suffer from basic limits for modelling autonomous agents and MA systems. After a brief re-statement of Game-Theory's role for DAI and MAS (e.g. the introduction of formal prototypical social situations ("games"); the use of formal and sound notions, a self-interested view of autonomous agents, etc.;) a number of criticisms, that have an impact on modelling intelligent social/individualaction, have been examined: the economicist interpretation of rationality; its instrumentalistconception, which leaves implicit the ends of agents' choices; the consequent multiple equilibria allowed by the theory; the context-unboundednessof rationality (here, some contributions for a more eterarchic, context-bounded, architecture of rational agent are made, and a goal-based strategy, as distinct from a strictly utilitarian principle of decisionmaking, is proposed); its troubles with Multi-Agent Systems. Finally, some limits inherent to the notion of "incentive engineering" are pointed out.

Multiagent C o o r d i n a t i o n in Antiair Defense: A Case Study* Sanguk N o h and Piotr J. Gmytrasiewicz Department of Computer Science and Engineering University of Texas at Arlington Arlington, TX 76019, Box 19015 {noh, piotr}@cse.uta.edu Office: (817)272-3399, 272-3334, Fax: (817)272-3784

Abstract. This research addresses rational decision-making and coordination among antiair units whose mission is to defend a specified territory from a number of attacking missiles. The automated units have to decide which missiles to attempt to intercept, given the characteristics of the threat, and given the other units' anticipated actions, in their attempt to minimize the expected overall damages to the defended territory. Thus, an automated defense unit needs to model the other agents, either human or automated, that control the other defense batteries. For the purpose of this case study, we assume that the units cannot communicate among themselves, say, due to an imposed radio silence. We use the Recursive Modeling Method (RMM), which enables an agent to select his rational action by examining the expected utility of his alternative behaviors, and to coordinate with other agents by modeling their decision-making in a distributed multiagent environment. We describe how decision-making using RMM is applied to the antiair defense domain and show experimental results that compare the performance of coordinating teams consisting of RMM agents, human agents, and mixed RMM and human teams.

1

Introduction

This paper describes rational decision-making and rational coordination among the antiair defense units facing a missile attack. The task of automated defense units is to defend a specified territory and to coordinate their attempts to intercept the attacking missiles, given the characteristics of the threat, and given what they can expect of the other defense units. Our approach to this coordinated decision-making problem is based on the assumption that the task of each of the defense units is to minimize the overall damages to the attacked territory. Under a realistic threat situation, friendly defense units cannot expect to have an advanced knowledge of the character of the incoming attack. It is, therefore, crucial that each of the defense units make * This research has been sponsored by the Office of Naval Research Artificial Intelligence Program under contract N00014-95-1-0775, and by a research initiation grant from the CSE Department of the University of Texas at Arlington.

a decision as to which incoming threat to intercept with an available interceptor by analyzing all potential threats acquired from the radar. In such cases, coordination requires an agent to recognize the current status, and to model the actions of the other agents to decide on his own next behavior. Since in any realistic combat situation the integrity of the defense team cannot be guaranteed, and even the very existence of the other friendly units cannot be counted on, relying on pre-established coordination protocols can be suboptimal or even dangerous. Therefore, our approach is that each unit is to independently decide on and execute his action, and that coordination among the units is to emerge on-the-fly as the result of the units' individual rational actions. We begin by formulating the antiair defense as a decision-theoretic problem from the point of view of an individual defense unit. As we mentioned, the objective is to minimize damages. Since the classical notion of a symbolic goal doesn't provide a sufficient basis for choice of action in uncertain situations [13], we need the attributes quantifying the quality of choices in the design of decision-making procedures. First, each attacking missile has its threat value. We compute the missile's threat considering such attributes as the altitude of the missile and the size of its warhead. Further, the defense units should consider the probability with which their interceptors would be effective against each of the hostile missiles. Based on these attributes combined, each unit has to determine the optimal action from his probabilistic decision model. For the purpose of coordinated decision-making in a multiagent environment, our research uses the Recursive Modeling Method (RMM), previously reported in [2, 3]. RMM enables an agent to model the other agents and to rationally coordinate with them even if no protocol or overall plan can be established explicitly in advance. Using RMM as a decision-making tool, an agent rationally selects his action under uncertainty guided by the principle of expected utility maximization. We expect RMM to be appropriate to the antiair defense domain, since the coordinating units have to be able to react to threats in previously unforeseen circumstances. These can include the changing nature of the attack, other defense units being shot at and possibly incapacitated, communication lines broken down, sudden need for complete radio silence, and so on. In these unexpected conditions, relying on globally consistent view of the situation, achieved by communication or by pre-established protocols, is unrealistic or likely to lock the agents into suboptimal forms of behavior. Further, by being competent decision makers and able to rationally model the action of other agents, RMM agents can effectively interact with human-controlled units, in spite of a lack of a predetermined protocol that a human would have to learn and follow. In the following sections, this paper explores the overall objective of the antiair defense scenario. To compute the expected utilities of alternative action, we elaborate on a set of attributes describing the relevant features of the problem domain. Then, we explain how RMM leads an agent to the subjective decisiontheoretic optimality with a concrete example of a coordination in our domain, and we discuss the experimental results. In conclusion, we summarize our results and further research issues.

2

Antiair Defense Environment

Let us consider a situation depicted in Fig. 1. This scenario has six incoming missiles and two defending units in a 20 by 20 grid world. Each of the defense units independently decides to launch interceptors against the incoming missiles in the absence of communication. The incoming missiles keep going straight top-down and attack the overall ground site on which the units are located.

Missile k

Interceptor

/~

Defense

Unit

Fig. 1. The antiair defense scenario.

2.1

Attribute Analysis

One of the major problems encountered in decision making process is how to model preferences and utilities. For the purpose of formalizing the decisionmaking problem of minimizing damages, we first consider the attributes that influence the units' decision-making. Each missile has its intrinsic threat value. In our model, threat is evaluated by considering the altitude of a missile and its warhead size. Intuitively, a defense battery should give priority to the missile which is closer to the ground and bigger than the others. An explosion of a missile close to the ground results in a more powerful the blast, which increases the damages. Further, the measure of damage is proportional to the size of a missile's warhead. We calculate the missile threat, MT, using the following formula:

MT,~ = W. x 1/An where 9 W,~: the warhead size of missile n

(1)

9 An: the altitude of missile n A factor that does not enter into the missile threat calculation, but one that should be considered when an intercept decision is made, is the probability that an interceptor would be successful against a given missile. This probability is assumed to depend on the angle between a missile's direction of motion and the battery's line-of-sight. This probability is maximized when this angle is 0, as follows: P ( H I T i j ) = e -~'~'j

(2)

where " 7ij: the angle between battery i's line-of-sight and missile j's direction of motion. Thus, "~ij t a n - l a - t a n - l ~ such that 0 _< Vii _< 90 9 a: the slope of the missile j's direction of motion 9 fl: the slope of the line-of-sight with which battery i aims at missile j 9 #: an interceptor-specific constant (assumed here as 0.01) :

We will use the values of missile threat and the hit probability to develop the decision-theoretic assessment of the agent's alternative plans of action in an antiair defense environment.

2.2

Planning and Execution Cycle

In the domain in Fig. 1, the defense units, {Battery1, Battery2}, is a set of planning and executing agents. The targets, {MissileA , MissileB, MissileC, MissileD, MissileE, MissileF), have no plans of their own, and are assumed not to make any decisions. The ordered actions (Scan-Area, Select-Target, Launch-Interceptor) available to agents are repeatedly used to achieve the subgoal of intercepting one of the attacking missiles during the overall plan-action cycle. As we mentioned, there is no notion of a symbolic goal in this planning. Instead, the goal of minimizing the damages is represented as a quality measure assigned to plans, which is then used to coordinate plans among multiple agents. In this case study, our work addresses the rationally coordinated target selection, i.e., the Select-Target step, providing the best defense strategy that can be implemented by independent defense units.

3

Decision-Theoretic

Agent

To be rational in decision-theoretic sense, the agents follow the principle of maximum expected utility (PMEU) [10]. In this section, we will show how PMEU can be implemented in this case study using the Recursive Modeling Method (RMM). RMM [2, 3] will be used to model the other agent, and to select the most appropriate missile to intercept by a given defense battery.

3.1

An Example

Scenario

Our a p p r o a c h is to take the agent-oriented perspective. In the examples scenario (Fig. 1), we view the decision-making t h r o u g h the eyes of an individual defense unit, B a t t e r y l , and his r a d a r - a c q u i r e d data. 1 Fig. 2 depicts the information acquired in the example scenario by B a t t e r y l for the missiles A t h r o u g h F. In Fig. 1, the left top corner of the screen is (0,0), x is pointing right, and y is pointing down. Applying formula (1) and (2) to the acquired data, B a t t e r y 1 can c o m p u t e the relevant a t t r i b u t e s of altitude, w a r h e a d size, and the angle t h a t determines the hit probability. B a t t e r y l also generates the expected hit probabilities for B a t t e r y 2 , assuming his hypothetical intercepting actions. T h e results are s u m m a r i z e d in Fig. 2.

Data Acquisition

[ Attributes )

Missile(warhead,position) A: (470,(3,3)) B: (410,(5,6)) C: (350,(9,2)) D: (370,(12,5)) E: (420,(16.3)) F: (450,(18,6))

Location(position) Batteryl:((7,20)) Battery2:((13.20))

MT MTA: (27.65) MTs :(29.29) MTc :(19.44)

(1) & (2) ~

P(HIT) P(HITtA):(0.88) P(HITIR):(0.92) P(HIT,c):(0.94) P(HITID):(0.83) P(HITIE):(0.76) P(HIT,F):(0.68)

MTD: (24.67) MTE : (24.71) MT~ : (32.14) P(HIT2A):(0.74) P(HIT2B):(0.74) P(HIT2c):(0.88) P(HIT2D):(0.96) P(HIT2E):(0.90) P(HIT2F):(0.82)

Fig. 2. Radar data acquired by Batteryl for the missiles A through F.

3.2

Generation

of the Payoff Matrix

Commonly, a decision p r o b l e m is represented by a version of belief network [6]. Poh and Horvitz [7] use an influence d i a g r a m t h a t includes a decision variable, with values ranging over the possible decisions, chance variables, t h a t represent the uncertainty of the domain, and a utility node. In our work, we rely on the payoff m a t r i x representation, used in g a m e theory. Payoff matrices, while different from belief nets, can be seen to faithfully s u m m a r i z e the information contained in belief nets by listing the expected payoffs (obtained from the utility node) of possible decisions, depending on the p a r a m e t e r s describing the domain (chance nodes). T h e expected payoffs, corresponding to b a t t e r i e s ' a t t e m p t i n g to intercept the respective missiles, can be expressed as a c o m b i n a t i o n of the t h r e a t of the 1 Battery2 acquires the information about environment from his point of view. Batteryl and Battery2 independently maintain their knowledge bases.

missiles and the probability of their interception. For example, if the Battery1 is faced with n missiles at some state, and he targets a missile j, the resulting threat will be reduced by the missile threat MTj multiplied by the probability of successful interception P(HITlj). If both batteries target missiles at the same time, the reduction of threat, and therefore the total payoff, is equal to the sum of the threats that each of them removes. 3.3

Modeling Other Agent - Recursive Model Structure

In order to solve his decision-making situation, described by the payoff matrix above, B a t t e r y l needs to hypothesize the likely actions of the Battery2. In the Recursive Modeling Method, the actions of the other rational agents are anticipated using a model of their decision-making situation. If it were known that the Battery2 is a rational decision-maker, then Battery2's decision-making situation could be modeled as a payoff matrix as well. In our case study, we considered a more realistic situation in which it is not known for sure that the Battery2 is rationally maximizing the payoff as well. For example, it could be that Battery2 has been damaged or otherwise incapacitated, in which case there is no information as to what action he would undertake. Thus, there are two alternative models that B a t t e r y l can use to model Battery2; one has the form of the payoff matrix, and the other one contains no information about Battery2's action. We call the latter model the No-info model. In RMM, each of the alternative models is assigned a probability indicating the likelihood of its correctness. Further, in case the Battery2 is not incapacitated, it is likely that Battery2 is modeling B a t t e r y l as well, in his own attempt to coordinate with B a t t e r y l . This leads to the nesting of models in RMM. Since, in our scenario, Battery2 also may be uncertain whether B a t t e r y l is not damaged, there are two alternative models on this level of modeling as well. One of the models Battery2 can have of B a t t e r y l is that of a rational maximizer, while the other one is unknown and again labeled as a No-info model. The resulting hierarchy of models, which we call the recursive model structure, terminates with a No-in]o model when the agent (in this case Battery2) runs out of modeling information. Fig. 3 is the B a t t e r y l ' s model structure of depth three for the example scenario in Fig. 2. To summarize, level 1 in the recursive model structure represents the way that B a t t e r y l observes the situation to make his own decision, shown as B a t t e r y l ' s payoff matrix. Level 2 depicts the models B a t t e r y l has of Battery2's situation, and level 3 contains the models that B a t t e r y l anticipates Battery2 may be using to model Batteryl. The recursive modeling could continue into deeper levels, but in this case we assumed that the batteries have no further information. In other words, we are examining the reasoning of B a t t e r y l in the particular case, when equipped with a finite amount of information about Battery2, and nested to the third level of modeling. 2 2 We do not elaborate on the important issues of learning and belief revision here. Thus, we analyze decision-making given a pre-existing state of knowledge, but we do

10

Battery2

Level 1:

Batteryl

A B C D

A 26.8 47.4 38.6 41.0

B 46.0 28.7 40.0 42.3

C 41.4 44.2 19.3 37.7

D 48.0 50.7 42.0 24-~

E 46.6 49.4 40.6 42.9

F 50.6 53.4 44.7 46.9

E 39.1 40.4 35.9 42.4 24.1 45.1 F 42.3 43.7 39.1 45.7 44.3 30.3

--.. 1, we distinguish the following families of tactics: . Relative Tit-For-Tat The agent reproduces, in percentage terms, the behaviour that its opponent performed 5 _> 1 steps ago.

30

I xo_~ [n+l [j] =

tn- 26 9

min(max(x,~2~+2[j l ~ [3] x ta_b ..... [31,"'min~), m a x , ) n > 26 min~ + ~.?(max~ - m i n ~ ) min~ + (1 - n ; ) ( m a x ~ - rain;)

n < 2~, Vj~ decr.

n < 26, Vja incr.

Depending on the value of the quotient between two consecutive counter offers, the agent exhibits a range of different behaviours: m i r r o r if the quotient is greater than 1, r e t a l i a t o r y if it is lower t h a n 1, and a type of time independent b o u l w a r e if it is exactly 1. 2. R a n d o m A b s o l u t e T i t - F o r - T a t The same as before but in absolute terms. It means t h a t if the other agent decreases its offer by s then the next response should be increased by the same s Moreover, we add a component t h a t modifies t h a t behaviour by increasing or decreasing (depending on the value of p a r a m e t e r s) the value of the answer by a random amount. (This is introduced as it can enable the agents to escape from local minima.) M is the m a x i m u m amount by which an agent can change its imitative behaviour. rain ( max

~n-1 9 (Xa.-..-,b [3]

xt,~+~[j] = ~-'~

A,_

t~--26 (Xb__+ a

9 [3]

-

-

tn 26+2 Xb----, a

+(-1)SR(M),min;),max~) mi~; + ~;(ma~ - m~,~) rnin; + (1 ~ 2 ) ( m a x ; - m i n ; )

9 [ 3 ] ) ~-

n > 2~ n < 2~, 5 o deer. n 1 of its opponents history when determining its new offer. When 7 = 1 we have the relative Tit-For-Tat tactic with/~ = 1.

min(max( xt"+'[J] =

5

t rL 2 9

tT, 1[J],mi~) m a x ; ) n > 2"~ ,.x ~ ~+~b] Xa-.b

rnin~ + a ~ rnin~ + (1 - t~;)(maz; - rnin;)

n E 27, Vj~ decr. n < 2% ~ a incr.

Negotiation strategies

The aim of agent a's negotiation strategy is to determine the best course of action which will result in an agreement on a contract x t h a t maximises its scoring function V ~. In practical terms, this equates to how to prepare a new counter offer. In our model we consider t h a t the agent has a representation of its mental state containing information about its beliefs, its knowledge of the environment (time, resources, etc.), and any other attitudes (desires, goals, obligations, intentions, etc.) the agent designer considers appropriate. The mental state of agent

31 a at time t is noted as MS~a. We denote the set of all possible mental states for agent a as M Sa. When agent a receives an offer from agent b it becomes the last element in the current negotiation thread between both agents. If the offer is unsatisfactory, agent a generates a counter offer. As discussed earlier, different combinations of tactics can be used to generate counter offers for particular issues. An agent's strategy determines which combination of tactics should be used at any one instant. Hence, the following definition: D e f i n i t i o n 3. Given a negotiation thread between agents a and b at time over domain X = Xl x ... X X p , with x t" l. and a finite set ~ b = { " ' , xt,, b---,aJ, m tactics n T a = {TiIzi : M S a ~ X}ie[1,m], a w e i g h t e d c o u n t e r p r o p o s a l any lineal combination of the tactics that generates the value at time tn+l the thread. T h a t is, for issue j

Xa__+b[Jj -'- "[jlTl(iSta'~+l)[j] H- ")'j2T2(iStan+l)[j] --[-... T "~jrnTmllVlo a such that for all issues j,

EiE[1,m] ~/ji = 1 and

tn of is in

][j]

x~+~bt~+l--__{ . . . '~b--*a'~a--*bI~'tn tn+l 1

Given a set of tactics, different types of negotiation behaviour can be obtained by weighting the tactics in a different way. T h a t is, by changing the matrix F particular to each negotiation thread:

711 ~12 ''- ~/lm / [~/21 ~22 ~2m

t

/

\~pl ~/p2

~/pm

An example of when this weighted combination may be useful is when modelling a s m o o t h transition from a behaviour based on a single tactic (e.g. Boulware, because the agent has plenty ot time to reach an agreement) to another one (e.g. Conceder, because the time is running out). Smoothness is obtained by changing the weight affecting the tactics progressively (e.g. from 1 to 0 and from 0 to 1 in the example). We model many-parties negotiations by means of a set of interacting negotiation threads. The way this is done is by making a negotiation thread influence the selection of which matrix F is to be used in other negotiation threads. Thus, D e f i n i t i o n 4. Given a, b E A g e n t s , t E T i m e , a's mental state M S~, and/"Z-,b, a N e g o t i a t i o n S t r a t e g y , is any function f of the following type:

Fat+l --,b

= f(F~--+b, M S t )

11 This definition uses the natural extension of tactics to the multi-dimensional space of issues' values.

32

A simplistic example of the application of our model would be to have a matrix F built up of 0s and ls and having F a---*b t+l = F a---*b t for all t. This would correspond to using a fixed single tactic for each issue at every instant in the negotiation.

6

Convergence results

Convergence in negotiation is achieved when the scoring value of the received offer is greater t h a n the scoring value of the counter offer the agent intended to respond with. T h a t is, D e f i n i t i o n 5. A Negotiation thread x ~t~b = { ' " , x t~ b--*~} converges at time t~+l

iff V%x~L~) --> v ~ ~, '~+~ a-*b] With this definition in mind, we have obtained some preliminary results on convergence for a single variable, single tactic, bilateral negotiation. Wider convergence criteria will be forthcoming as future work. The second proposition (6.2) is particularly interesting because it allows an agent using a time-dependent tactic to know if the negotiation will converge with an agent playing relative TitFor-Tat. Knowing if an opponent is playing Tit-For-Tat can easily be guessed by using a strategy t h a t makes some initial random offers and then examines the responses. Notice, however, t h a t convergence cannot be guaranteed in general. For example, two agents using a Tit-For-Tat tactic might stay negotating forever if no limitation on time is established.

If two agents a and b negotiate values for an issue j over the value regions [min;,max;] , [m~nj, 9 b raaxj], b satisfying [rnin~, max;] A [min~, m a x b] ~ O, then the following properties hold:

Proposition6.

1. If a and b follow a time-dependent tactic with Vja decreasing (resp. inereasb lug), Vjb increasing (resp. decreasing) and tanaz = tma x then the negotiation for issue j converges. 2. If a uses a time-dependent tactic with Vja increasing (resp. decreasing) and b uses a relative Tit-For-Tat tactic with Vjb decreasing (resp. increasing), and a starts the negotiation thread, then the negotiation converges if Xta~b[jlX~a[j] < (rain;) 2 (resp. if Xa_,b[J]Xb__,a[j ,2 ] > (maxD2) Proof. (1) We prove it for ~ a decreasing, the other case is similar. We have [min~, max;] N [min b, max b] 7~ O, then m a x ; > m i n b. W h e n time runs out the a(t) functions of both agents become 1, t h a t is a~(t~ax) = a j (bt m abx ) ---- 1 and then their acceptance values will become rain; + ~ ; ( t ) ( m a x ; - m i n ; ) = m a x ; and min~ + (1 - ajb(t))(maxjb- rain b) = minb. So, b will make the offer tbLa~

.

Xb--,~ [2] = rain b at the deadline. But given t h a t a uses a monotonically decreasing function and m a x ; > min~ we have Vja(max;) > Vj~(minb). In other words,

33

by using the assumption tma ,~t~ t a ~ [J])" So a x = tbma~ we have ~,j/ ' a (~b--,a [J]) > ]"j] ' a (~Xa--*b the negotiation converges. (2) Again we prove it for V3b increasing, the other case is similar. Without loss of generality assume that the thread is: t , - 1

X a.-.b

{Xtal_~b '

t2 , tn-l l Xb--. a 9 .. , X a--. b

and that ~ = 1. By using the definition of relative Tit-For-Tat, it can be seen that: tn-3

r .1

tn-3

t n - - 5 r .1

x ~ a [ j ] = xa-~b[3J t.-z Xa-~b~J] Xa-~b[3] x t ~ , [ j ] = b t3] a t ' ' " Xb-"':' [J] -t"-'[J] ~Xa-~b z -.b [3] za-.b in--3

try--5

9

xo-,b[J] '" ,.1 : t.._: t.-3,-:: " " ~ b~,:,,L3J= t._1,-.1 b~o,[3J Xa--,b[J] X~-~bt3J Za-~bt3] Z~-~bt3J The thread converges if Vjb

t~-i

.

> Vjb(x L [j]), but given that Vjb is

t~-i monotonically decreasing, this happens only if Xtb~[j] < X~_.b [3]. Then, by subtl " ~ in-1 r , ; 1 that is tl 9 t2 9 in-1 9 2 stituting we get ~ z ~ a [ j ] Xa__,b[jj, Xa~b[3]Xb__,~[3 ] < (X~__,b [j]) . a~b

tJJ

t 9 = min~ (by Vja beBut when time approaches t~nax we have h9m t - - , t ~ X a--*b[J] tl . t2 .] < (min~) 2 the negotiation ing increasing). So, at the limit, if x~__+b[3]Xb__,a[3 converges.

7

Related

work

Research in negotiation models has been pursued in different fields of knowledge: game theory, social sciences and artificial intelligence. Each field has concentrated on different aspects of negotiation, making the assumptions that were pertinent for the goal of their study. In game theory, researchers have been interested in mechanism design: the definition of protocols that limit the possible tactics (or strategies) that can be used by players. For instance they are interested in defining protocols that give no benefit to agents t h a t mis-represent or hide information [9]. In this work disclosure of information is acceptable, because by doing so it will benefit the agent in finding an optimal solution for itself. Contrary to our model, and as we discussed in Section 2, this is an inappropriate assumption from the point of view of real applications. As has been argued elsewhere [10], these and other assumptions limit the applicability of game theory models to solve real problems. Our interests lie in invertigating the process of negotiation among agents and not only on the outcome. Hence, our study, and those forthcoming, are much more in the experimental line of [4]. Although we do not concentrate on learning, some similarities can be found with the formalism by Zeng and Sycara [10]. We have not concentrated however on the description of negotiation protocols that has been an important focus of attention for the community of distributed artificial intelligence (see [7] for extensive references).

34

8

D i s c u s s i o n a n d future work

The next stage in the development of our model is to undertake an experimental evaluation of the tactics and strategies described herein. We believe adequate strategies have to be developed in accordance with the desired properties and characteristics of the domain at hand. These strategies then need to be tested in repeated games over a range of typical scenarios to determine which are the most successful. Some initial modeling of concrete strategies has been made considering several variables in the mental state of an agent: (i) an approximation of the first and second derivatives of the other agent's behaviour, (ii) the relation between both negotiating agents (e.g. members of the same company, boss/employee, ...), and (iii) the time remaining to reach an agreement (in this case time is playing a role at both strategic and tactic levels). This model is being used in the real modeling of the domain presented in Section 2. Currently there are two versions of the model implemented in CLIPS and PROLOG. The initial results on convergence, although simple, encourage us to make a more complete analysis of the types of negotiation situations that are likely to occur. We have identified many research opportunities in extending the model. For instance, fuzzy control techniques could be used to relate a qualitative estimate of the first and second derivatives of the opponent and a qualitative value for the/~ to be used in a tactic; we imagine a rule like: If the agent concedes quite a lot (first derivative) and the agent concession ratio (second derivative) increases then Beta is Medium. Genetic algorithms could also be used to determine experimentally which weighted combinations of tactics survive better in the line of [4]. Moreover, genetic algorithms may help to determine which negotiating agents show the desired behaviour by using the strategies as the genetic code. Finally, case-based reasoning could be used to model strategies. The case memory could be used by the agent to determine which past combinations of tactics worked best in similar circumstances.

9

Acknowledgements

This project has received the support of the Spanish Research project SMASH (CICYT number, TIC96-1038-C04001) and the D T I / E P S R C Intelligent Systems Integration Programme (ISIP) project A D E P T .

References 1. R. Axelrod. The Evolution of Cooperation. Basic Books, Inc., Publishers, New York, USA., 1984. 2. B. Crabtree and N. (eds.). The Practical Application of Intelligent Agents and Multi-Agent Technology. London, UK., 1996. 3. D. G.Pruitt. Negotiation Behavior. Academic Press, 1981.

35 4. A. Ito and H. Yano. The emergence of cooperation in a society of autonomous agents - the prisoner's dilemma gamme under the disclosure of contract histories. In V. Lesser, editor, Proceedings of the First International Conference on MultiAgent Systems, pages 201-208, San Francisco, USA, 1995. AAAI Press/The MIT Press. 5. N. R. Jennings, P. Faratin, M. J. Johnson, T. J. Norman, P. O'Brien, and M. E. Wiegand. Agent-based business process management. Int Journal of Cooperative Information Systems, 5(2-3):105-130, 1996. 6. S. Krans, J. Wilkenfeld, and G. Zlotkin. Multiagent negotiation under time constraints. Artificial Intelligence Journal, 75(2):297-345, 1995. 7. H. Mueller. Negotiation principles. In G. M. P. O'Hare and N. R. Jennings, editors, Foundations of Distributed Artificial Intelligence, Sixth-Generation Computer Technology Series, pages 211-229, New York, 1996. John Wiley. 8. H. Raiffa. The Art and Science of Negotiation. Harvard University Press, Cambridge, USA, 1982. 9. J. S.Rosenschein and G. Zlotkin. Rules of Encounter. The MIT Press, Cambridge, USA, 1994. 10. D. Zeng and K. Sycara. How can an agent learn to negotiate. In J. Mueller, M. Wooldridge, and N. Jennings, editors, Intelligent Agents III. Agent Theories, Architectures, and Languages, number 1193 in LNAI, pages 233-244. Springer Verlag, 1997.

Norms as Constraints on Real-Time Autonomous Agent Action* Magnus Boman The DECIDE Research Group Department of Computer and Systems Sciences Stockholm University and the Royal Institute of Technology Electrum 230, SE-164 40 Kista, SWEDEN Phone: +46 8 16 1678 Fax: +46 8 703 9025 e-mail: [email protected] WWW: http://www.dsv.su.se/DECIDE & The ISES Project University of Karlskrona/Rormeby

Abstract. We describe a general model of agent action, and ways of constraining action using norms. The agents under study are supersoft agents: autonomous artificial agents programmed to collect, formalise, and actively use qualitative data. A consistent, interruptible, and pre-emptive anytime algorithm that also has measurable quality and diminishing returns is discussed. The algorithm controls procedures for decision making already implemented. The model thus has computational meaning and strives to characterise real-time decision making in agents acting under uncertainty in imprecise and partially described environments.

1. Introduction True agent autonomy is sometimes described as being incompatible with the adoption of social norms by the agent. Two extremes are the fully obedient agent that never violates a social norm and the anarchic agent that ignores, is unaware of, or even deliberately chooses to violate social norms. In this view, the obedient agent is seen as being fully dependent and the anarchic agent as being fully autonomous, see Fig-1. Full u

\ \ \ \ \ \

o

\ N

None

Autonomy Full

F i g - l : The trade-off between obedience and autonomy. t The author would like to thank Love Ekenberg, Mats Danielson, Harko Verhagen, and the participants of the ICMAS'96 Workshop of Norms, Obligations, and Conventions for discussions in relation to this paper. This work was in part supported by NFR.

37 There are several reasons for questioning the truth in this purported trade-off. First, the social norms employed might not be very restrictive. In many environments, an agent could act as if unaware of the norms without actually violating them. The likelihood of this scenario seems to increase with the complexity of the agents in the multi-agent system (MAS). Simple utilitarian agents are less likely to escape restrictions on behaviour, becanse their behaviour can be concisely described and predicted with relative ease. Hence, norms are easier to specify in such a way that they keep agents in control. By contrast, more sophisticated and social agents will inescapably affect social norms via their actions--the special case being an MAS in which social norms emerge, with the set of norms being empty at the outset. A second reason, more general than that special case, is that agents sometimes act upon incomplete information. Moreover, there might be uncertainties involved that pertain to utilities, probabilities, or some other relevant metric in the domain at hand. Being forced to act in an incomplete and uncertain environment, the agent is prone to errors and ways of reducing the number of erroneous decisions made would be welcome. Modelling agents only partially aware of social norms, the norms themselves being imperfect in that they are partial and sometimes inapplicable, is an important problem. A third reason is that it is often meaningful to talk about the existence of social norms, even though they are violated occasionally. Violations may not be a display of anarchic behaviour: the deviator might in fact honour the norm, but nevertheless have good reasons for violating it. An agent might violate a social norm because the punishment is relatively low. A utility maximiser might, for instance, neutralise a physical threat in an act of self defence. These reasons not only serve as evidence for the falsity of the trade-off in Fig-1, but are interesting in their own right. In the following section, we will further motivate the introduction of a model of autonomous agent action, as well as of ways of constraining action using norms. Section 3 discusses the model itself. Special attention is given to norms, since the agents under study are part of an MAS. Section 4 discusses evaluation aspects of the model. The final section offers conclusions.

2. Background One way of studying rational agents that repeatedly act upon the result of evaluating decision situations is to think of their means of evaluation as external to the agent. Just as a human agent might pick up a calculator for computing the product of two large numbers, an artificial agent might seek assistance in the form of decision support, almost regardless of the level of sophistication of the (internal) reasoning capabilities ascribed to the agent. As a case in point: we have just initiated a small project for making a simple version of the socalled Delta Decision Tool [9] available on the Internet. Mobile Internet agents can in the future seek advice from a decision module by interacting with a Java program on a WWW page, and await advice before moving on in the network. As designers of the page, we can have no control over the agents that choose to use the page, e.g., to what extent they accept the advice given as normative.

38 We make the following two provisos, more concise motivations for which are available in [7] and [13] respectively.

Proviso 1: Agents act in accordance with advice obtained from their individual decision module, with which they can communicate. Proviso 2: The decision module contains algorithms for efficiently evaluating supersoft decision data concerning probability, utility, credibility, and reliability. The first proviso makes our presentation clearer, because every change of preference (or belief revision, or assessment adjustment) of the agent is thought of as adequately represented in the decision module. This gives us freedom from analysing the entire spectrum of reasoning capabilities that an agent might have, and its importance to the use of the decision module. The communication requirement presents only a lower bound on agent reasoning by stating that the agent must be able to present its decision situation to the decision module. This entails that the agent can represent the information in an ordinary decision tree. That the decision module is seen as customised is inessential: It is a metaphor for a situation where all agents utilise an oracle, but where the computations of the oracle depend on the individual agent input. In other words, the oracle acts as a decision tree evaluator, and the size of the oracle is the only thing that makes it inconvenient for the agent to carry it around in its rucksack. The proviso also lets us separate the important problem of agents failing to obey social norms from the other problems discussed in this paper. Finally, the proviso makes explicit that no nonconsequentialist decision bias affects the decision chosen [3]. In the eyes of a consequentialist (e.g., a person in favour of expected utility theory), artificial agents are closer to the perfect decision maker than human agents can ever hope to be. The second proviso requires more explanation. Supersoft decision theory (SSD) is a variant of classical decision theory in which assessments are represented by vague and informal statements, such as "The outcome o is quite probable" and "The outcome o is most undesirable." The purpose of SSD is to investigate the applicability of the classical theory to real-life decision situations [ 19]. The obvious reason for decision analysts to be interested in SSD is that most human decision makers prefer vague and informal assessments to the use of precise numbers, as in "The outcome o is larger than 0.55"; not to mention "The probability of the outcome o is 0.32", the precise meaning of which is almost impossible to grasp for a human [17]. If the agents under study are supersoft agents: autonomous artificial agents programmed to collect, formalise, and actively use qualitative data, then these agents could merit from using SSD tools for evaluation. Supersoft agents need not know the true state of affairs, but can describe their uncertainty by a probability distribution. In such decisions with risk, the agent typically wants a formal evaluation to result in a presentation of the action optimal with respect to its assessments, together with measures indicating whether the optimal action is much better than any other action, using some kind of distance measure. The basic requirement for normative use of such measures is that (at least) probability and utility assessments have been made, and that these can be evaluated by an evaluation function, e.g., the principle of maximising the expected utility (PMEU). Given such a function, one might contemplate its use as a choice rule generator. In fact, several people have

39 equated rationality with the use of a set of choice rule generators containing only the PMEU (see, e.g., [14]) but there are good reasons not to accept this equation [5]. While the PMEU can be seen as constituting the core of rational agent behaviour, it is not always sufficient in its own right [6]. The PMEU can never be the operational definition of rationality that Newell sought in his oft-cited AAAI address [16],[20]. In particular, the agent may want to exclude particular actions that are in some respect too risky regardless of whether they are affirmed (or dismissed) by the PMEU. This can be done by setting security levels, an idea introduced in [22] that has been applied to MAS [10], [12]. In [7], our recommendation of using security levels to extend the PMEU stood fast but with the notion of security level and policy implementation given an important new interpretation as a norm for cooperative action (cf., e.g., [8]). This idea is combined here with notions developed in [13] to give the general model for constraining actions presented in the following section. 3. A M o d e l f o r A c t i o n C o n s t r a i n t s The model in Fig-2 below is general in that it makes relatively few assumptions about the agent architecture, language, sensors, and communication protocol. This is not to say that these matters are unimportant. Some choices of agent language would be incompatible with the model, for example. The ambition is to let the model admit a unifying view, and we therefore encourage that it be complemented by additional components as appropriate. Not even the concept of goal is necessary: Agents are seen as fully autonomous and will always maximise their absolutely local and subjective utility. Bootstrapping does not present a problem, since no restrictions apply to neither sense data, nor communication data. The only requirement is that the contents of the four bases conform to Proviso 2 above. The concept of agent credibility as used here in imprecise and uncertain domains (cf. [3]) was defined in [10], and that of reliability in [13]. We refer the reader to the latter paper for a formalisation of the four different bases in the decision module, and focus here on intuitive aspects of the representation. Before taking an action, an agent might have used its means of communicating with other agents in the MAS, its sensors, and the computational power of the decision module. If there are no norms present in the MAS, the four bases in the decision module are non-linear systems of equations representing (typically subjectively assessed) supersoft data about 9 probabilities of outcomes of different consequences of actions 9 utilities of outcomes of different consequences of actions 9 credibilities of the reports of other agents on different assessments 9 reliabilities of other agents on different agent ability assessments The preferences of the agents can be stated as intervals of quantitative measures or by partial orderings--the formal details available in our earlier papers are not repeated here. Credibility values are used for weighting the importance of relevant assessments made by other agents in the MAS (see [10] for details on how to formalise this). Reliability values mirror the reliability of another agent as it in turn assesses the credibility of a third agent (see [13]). All bases except the utility base are normalised. Note that an MAS without norms is a social structure where group utility is irrelevant to the individual agent.

40 The presence of norms can manifest itself in three ways, each representing a different level of abstraction: 1 Through skewing of the equations in the four bases. 2 By filtering normative advice before it is received by the agent. 3 By disqualifying certain actions by referring to their negative impact on the global utility in the MAS.

Sens e }, where si is the subset of I made of all the information items given by S i. The information set is the main output of the belief-function formalism. Figure 2 presents the I/O mechanism applied to the case in the previous example. Let us see now how the mechanism works. Remember that E denotes the set of the atomic propositions of L. The power set of E, ~ = 2 - , is called frame of discernment. Each element o~ of fl is a "possible world"or an "interpretation" for L (the one in which all the propositional letters in ~0 are true and the others are false). Given a set

49 of sentences s c I (i.e., a conjunction of sentences), [s] denotes the interpretations which are a model for all the sentences in s.

Source Information U

b

w

a

W T

Credibility Information

Credibility

Good

7981 b .597 a .113 a c a ~ - - ( b )

D-8

c

.597 c

Theory of Evidence

a --9 -~(b)

1"4351b c a 395 a ~

-~(b)

Source Reliabil~lNN " U 0.9 W 0.8 T 0.7

Source

O

New Reliability U .798

r Bayesian TM O lC~176

W .597 T .395

Fig. 1. Basic I/O of the belief-function mechanism The key assumption with this multi-source version of the belief function framework is that a reliable source cannot give false information, while an unreliable source can give correct information; the hypothesis that "S. is reliable" is compatible only with [si], while the hypothesis that "S.~is unreliable" Is compatible with the entire set ~ . Each S i gives an evidence for f~ and generates the following basic probability

assignment (bpa) m. over the elements X of 2f~: 1

mi ( X )=

Ri 0

ifX =~ otherwise

All these bpas will be then combined through the Dempster Rule of Combination:

:c Xln...nXn=X

m(X)=ml(X)|174

~

m I ( X I ) " ... " mn( Xn)

Xln...nXnr

From the combined bpa m, the credibility of a set of sentences s is given by:

Bel(s)= ~ m ( X ) X~Is]

In figure 1, we see another output of the mechanism, obtained through Bayesian Conditioning: the set { ..... <Sn, NRn>}, where NR i is the new reliability of S i. Following Shafer and Srivastava, we defined the "a priori" reliability of a source as the probability that the source is reliable. These degrees of probability are

50

"translated" into belief-function values on the given pieces of information. However, we may also want to estimate the sources' "a posteriori" degree of reliability from the cross-examination of their evidences. To be congruent with the "a priori" reliability, also the "a posteriori" reliability must be a probability value, not a belieffunction one. This is the reason why we adopt the Bayesian Conditioning instead of the Theory of Evidence to calculate it. Let us see in detail how it works here. Let us consider the hypothesis that only the sources belonging to ~ c S are reliable. If the sources are independent, then the probability of this hypothesis is:

II

S~O

Si~

We could calculate this "combined reliability" for any subset of S. It holds that R(O)= 1. Possibly, the sources belonging to a certain 9 cannot all be considered q~2 s

reliable because they gave contradictory information, i.e., a set of information items s such that [s]=O. In this case, the combined reliabilities of the remaining subsets of S are subjected to the Bayesian Conditioning so that they sum up again to "1"; i.e., we divide each of them by "1- R(O)". In the case where there are more subsets of S, say 9 l . . . . . O1, containing sources which cannot all be considered reliable, then: R(O)=R(OI)+ ... +R(O0 We define the revised reliability NRi of a source Si as the sum of the conditioned combined reliabilities of the "surviving" subsets of S containing Si. An important feature of this way to recalculate the sources' reliability is that if Si is involved in contradictions, then NRii IGj]=IG Although the "best-out" method is easy to implement, it is also very rough since it discriminates the goods by confronting only two assumptions. The lexicographic method could be justified in some particular application domains (e.g. diagnosys). The inclusion-based method yields a partial ordering Performance ofuniversity

> Performance of university (b)

Fig. 2. (a) Administrators' causal map (CMA); (b) Administrators' perception of staff's causal maps (CMsA)

c o n s t r u c t e d by one or m a n y agents, in order to p e r f o r m a causality-based analysis where choices are m a d e in terms of the consequences of believed courses of actions and can be explained in terms of antecedent circumstances. In fact, we can use causal maps at different levels to represent the subjective views of agents [5]. Precisely, first-order CMs show the views of individuals (or groups of individuals) such as I and J, second-order CMs show w h a t agent I thinks agent Y is thinking and vice versa. Third order m a p s show w h a t agent I thinks agent Y thinks agent X is thinking and vice versa. Similarly, higher order CMs can be constructed, b u t the reflections on reflections rapidly diminish like mirrors facing each other. Of course, higher order causal m a p s are by no means the only m e t h o d of depicting such structures. However, the m e t h o d proposed in this section is relevant in those cases where some sort of causal belief structures a p p e a r to be helpful in thinking a b o u t inter-agent interaction. As illustrative examples, consider the m a p s in Fig. 2 and 3, which represent the first and second-order perception of a situation involving a typical interaction between administrators and the staff in a university. Notice t h a t for the sake of simplicity and readability of causal maps, we have only considered here the three

85 basic relationships (% 0, _)1. In these conditions, Fig. 2(a) indicates the point of view of administrators and expresses that the global income of the university depends both on the number of students and upon supplementary sources of funds such as those provided by advertising and marketing campaigns. In Fig. 2(b), administrators believe that the staff has a bounded vision of the overall situation and, particularly, administrators believe that the staff does not understand the importance of advertising and marketing campaigns for improving the number of students. Staff recruimaent

Staff - - + > Teachingand recruitment researchquality

Teaching and research facilities

Global 9 income~

+ > ..... womng facilities

Precafious~ staff

selectionbased on scholar records

l-

Precarious < staff

+

Global income

Partial

Partial income

income

T+

§

selectionbased

> Number ofstudents

on scholar records

> Number ofstudents +

> Failures -

~. Performance

"

~Failures

of university

(a)

Ped0rmance ofuniversity

(b)

Fig. 3. (a) Staff's causal map (CMs); (b) Staff's perception of Administrators' causal maps (CMAs)

The members of the university staff have also their point of view on the overall situation, as indicated in Fig. 3(a). Their causal map CMs emphasizes the working facilities (of the staff) and the recruitment of other staff in order to improve teaching and research quality. Notice that this quality has an indirect positive effect on the global income. Their causal map also shows that the global income promotes precarious jobs that, in turn, negatively influence global 1 0 represents here the "unrelated" and the "neutral".

86 income. Thus, the staff's causal map specifies what is important for the members of staff: their working conditions and the recruitment of precarious staff which has a negative indirect effect on the recruitment of the permanent staff. Finally, causal map CMAs of Fig. 3(b) denotes the staff's perception of administrators. In this cognitive map, the staff believes that administrators don't see the importance of adequate working conditions of the staff and the worst thing is that administrators try to replace permanent staff by precarious staff, with few advantages, in order to improve global income. To summarize, subjective views represented by causal maps can be used by agents to reason on others. This reasoning can bear on (1) predicting what others can do; (2) explaining what others have done; (3) trying to demonstrate to others the importance of some area of causal relationships between concepts, etc. All these aspects allow agents to coordinate their activities and to develop more coherent interactions between them. Now we describe the reasoning process, based on subjective views, through the previous examples. To facilitate the representation of causal maps, we code the concept variables as follows: 1: Recruitment of staff, 2: Staff's wages, 3: Operation costs, 4: Wages, 5: Global income, 6: Partial income, 7: Advertising and marketing campaigns, 8: Number of students, 9: Selection based on school records, 10: Performance of university, 11: Teaching and research facilities, 12: Teaching and research quality, 13: Working facilities, 14: Precarious staff, 15: Failure (of students). In this case, CMA, CMsA, CMs, and CMA8 can be easily transformed as square adjacency matrices of size 15 x 15, VA, VSA, Vs and VAS. In these matrices, each element {ij} takes a value { + , 0 , - } as expressed in causal maps CMA, CMsA, etc. This matrix representation of CMs allows administrators and staff to construct new matrices A (merging the information from VA and VSA) and S (merging the information from Vs and VAS). In matrix A (resp. S), the notation Ix, y] in entry i, j expresses that the causal relation between C.i and Cj is x in the causal map CMA (resp. CMs) and y in the causal map CMsA (resp. CMAs). Thus, for example, the notation [ + , - ] in A expresses that the relationship between Ci and Cj is + in causal map CMA and - in causal map CMsA. Entries consisting of 0,0 are left blank. 1

A =

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

2

3

4

5

+,0 § +,0

-,0

6

7

8

9

10

11

-,0 +,0

+,+

+,+ +,0 +,+

+i+

12

13

14

15

87 With A and S, each party can compare his causal relations with those of the other. When comes the time of negotiation between both parties (administrators and staff) to renew their "social contract", matrix A permits administrators to see on what causal links they think they agree with the members of staff (i.e., [+, +] or [ - , - ] ) and on what other causal links they think they disagree with them (i.e, [+, 0] or [-, 0]). In the latter case, administrators try to convince the members of staff to alter their CMs so that the causal links [+,0] and [-,0] become [+, +] and [-, -]. On the other hand, the staff defends its point of view which is based on matrix S. Members of the staff try to convince administrators to change their beliefs so that causal links [0, +] and [0,-], as reflected in S, become [+, +] and [ - , - ] . 1

2

3

4

5

6

7

8

9

10

11

1 (

S=

2 3 4 5 6 7 8 9 10 11 12 13 14

15 i,

12

13

14

+,0

+,+

15

+,0

+,0 +,+ +,+ +,+

+,0 0,--

We now examine the case where possible actions are considered by agents (prediction) and suppose that in our context of university, administrators want to build a new building for services (including restaurants, banks, bars, libraries, movies, theater, etc.) to raise money. Administrators can view this action (through their causal map, CMA) as a new contribution for improving the global income of the university, whereas the university staff can consider this idea (through their subjective view on the administrators' causal map, CMAs) as a way to recruit more precarious staff. This case is a conflict and it can be solved by a negotiation between administrators and staff. To achieve this, administrators must try to alter CMA8 by persuading, with arguments, the staff that: (1) the university community needs to have such a new building, and (2) the staff's belief about the recruitment of more precarious staff is a misperception. The staff can counteract to demonstrate that it is just the opposite or that administrators want to make money, etc. Now suppose that administrators and staff do not agree on a satisfying settlement at the time of discussions about their social contract. In this situation, both parties might agree on appealing to a mediator from another university to find a solution to this impasse. We assume that they agree on a mediator who knows very well the situation (for example, the chosen person is a former professor who has been working in administration for ten years). In these conditions, the chosen mediator knows the concepts which are relevant to this situation and, consequently, can construct a grid from those concepts that she communicates to

88

both parties in order to collect their beliefs about causal links and therefore their CMs. After this collection, the mediator constructs A and S and analyzes them through a new matrix M to identify potential conflicts and potential positive relationships. 1

M

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

2

3

4

5

6

7

8

9

10

11

UA L/A ~A

12

13

14

US

(P

15

US

UA SA A UA A A A

A A

Us OB

UAS

The matrix M includes A and S and is based on Bryant's representation [6]. In this representation, the notation [+, 0;+, 0] indicates that the causal relationship between concepts Ci and Cj is + in the causal map CMA, neutral in CMsA, + in CMs and neutral in CMAs. In these conditions, [+, + ; + , + ] and [-, - ; - , -] express a general agreement (noted A) about a causal link. Similarly, [+, 0;0, 0] and [-, 0;0, 0] (noted L/A) or [0, 0;+, 0] and [0, 0;-, 0] (noted Us) express an unilateral view of administrators or staff. In the same way, notations such as [0, +;0,0] and [0,-;0,0] (noted UsA) or [0,0;-, 0] and [0,0;+,0] (noted L/A8) specify an unilateral view on another agent. Other causal links include: (1) [+, +;+, 0], [-, - ; - , 0], [-, 0;-, - ] or [+, 0;+, +];

(2) [+, +;0,0], [0,0;+,+], [-,-;0,0] or [0,0;-,-]; (3)[+,0;+,0], [-,0;-,0], [0, -;0, -] or [0, +;0, +]. In (1), both parties see the same causal relationship and one side believes that the other side shares the same perception (noted SA or $B). In (2), one participant holds a point of view and believes the other does too (noted (9A or OB). Finally, in (3), both parties see causal relations in the same way, but neither believes that the other shares this perception (noted S). The matrix M allows the mediator to study: (1) areas of consensus between agents (such as, for instance, the negative relationship between the selection based on scholar records and the number of accepted students at the university, the negative relationship between teaching and research facilities, and students' failures, etc.); (2) unilateral views of administrators represented by L/A; (3) unilateral views of the university staff, Us. The problem of how the mediator reasons on these aspects to solve conflicts between both parties will be investigated in the future. Notice that the emerging potential for cooperation can also be studied by analyzing the agents' cognitive maps. This suggests that cognitive map analysis can be used to explore the following: (1) How do the agent's characteristics (e.g., its rationality, its sincerity, its role, etc.) affect its cognitive map? (2) How to efficiently focus on patterns of similarities and differences between agents, with

89 respect to both their causal beliefs and their roles? (3) How to separately analyze cognitive maps in terms of complexity, density, imbalance and inconsistencies, relative to some salient goals? The objective behind this analysis is to locate groupings of goals that could result in a coalition of agents on the issue of establishment of a cooperation [14]. 2.3

Causal Maps as a Tool for Qualitative Decision Making in Multiagent Environments

Causal maps can also help an agent or a group of agents considered as a whole to make a decision. Given a cognitive map with one or more decision variables and a utility variable, which decision should be taken and which should be rejected? To achieve this, the concerned agent should calculate the total effect of each decision on the utility variable. Those decisions that have a positive total effect on utility should be chosen, and decisions that have a negative total effect should be rejected. Decisions with a nonnegative total effect on utility should not be rejected, decisions with a nonpositive total effect should not be accepted. No advice can be given about decisions with an universal, a non-zero, or an ambivalent total effect on utility. To solve the decision-making problem, an agent generally needs to analyze its cognitive map or the cognitive map of its organization if this map is available. To illustrate the decision-making process in the context of multiagent environments, consider, for example, the causal map of a professor (considered as an agent) P1 shown in Fig. 4. This professor has to choose between two courses D1 and D2 (D1 and D2 are decisions variables). ~rthermore, P1 works with a colleague P2 in the same research group (this group is called here G12) and shares with him some students. Pl's causal map, shown in Fig. 4, includes the following beliefs. D1 favors the theoretical knowledge of G12's students. Greater theoretical knowledge gives a greater motivation to students. Greater motivation of students gives a better quality of research for group G12, which gives, in turn, a greater utility of G12. P2 gives a course C1 that improves, as D1, the theoretical knowledge of Gl~'s students. This course, however, has the disadvantage to be very hard and this makes G12's students lose their motivation. Finally, the second decision variable D2 is an easy course that decreases the workload of P1. Obviously, decreasing P1 's workload increases her utility. In this case, how can Pt make her choice between the two courses D1 and D2? Notice that in the context of our example, P1 should reason about other agents (i.e., P2 and G12) to make her decision. Under some circumstances, she can also collaborate with them to develop her decision. In this sense, the decision-making process considered here is a multiagent process. To run this process, it might be useful to convert the causal map being analyzed to the form of a valency matrix V. With the valency matrix, P1 can calculate indirect paths of length 2 (i.e. V2), 3 (i.e. V3), etc., and the total effect matrix Vt (see Definition 11 below). In fact, Vt tells P1 how the decision variables D1 and D2 affect her utility and G12's utility. The formal model that we propose in the next section allows to calculate direct and indirect effects and consequently allows agents to make decisions.

90 DI(P1)

Theoretical know. of students

Research quality of G12

+

Utility of G12

Utility of P1

/_ (P2)

Resources from science faculty

~lents motivation

+

Workload

:~ of P1

-

Rosce from CS department

Fig. 4. An Illustrative Example for Decision-Making in a Multiagent Environment

2.4

C a u s a l m a p s as a T o o l for R e p r e s e n t i n g a D y n a m i c W h o l i s t i c Approach of an Organization of Agents

Weick [28] suggested to change the prevalent static view of an organization to a dynamic view which is sustained by change. Precisely, he proposed that organization and change were two sides of the same social phenomena. His reasoning was that an organization is a process of co-evolution of agents' perceptions, cognitions and actions. In this context, Weick proposed a theory of organization and change based on the graphs of loops in evolving social systems. Recently, additional investigation guided by this approach [3,4] tried to articulate how causal maps provide a way to identify the loops that produce and control an organization. In multiagent systems, the study of an organization of agents has generally focused on some structural models such as [2]: (1) centralized and hierarchical organizations, (2) organizations as authority structure, (3) market-like organizations, (4) organizations as communities with rules of behavior. All these structures missed dynamic aspects and influences that exist in an organization of agents. Generally, dynamic aspects and influences evolve through paths that close on themselves and form loops. We have realized that such loops are important for an organization of agents for two main reasons: i) a change in an organization is the result of deviation amplifying loops, ii) the stability of an organization is the result of deviation countering loops [4]. As an example, consider

9] the organization that binds researchers, grant agencies and qualified personnel in any (science and engineering) department. The causal map representing this organization is shown in Fig. 5. The meaning of this CM is clear and we shall explain it no more.

ion of est researchers+ "~ Researcher satisfaction Research ... quall~y ~

+

Sal .ieP satisfaction

"~.....~+

~

-

> Personnal Qualification

\

+

/

/

>- Quan.tity of quahfied

+ /

_ Resource~

--firesearch

Fig. 5. An organization of agents as loops

In this causal map, concepts link together to form loops, some of which are numbered (1) to (7). Loops (1), (4)-(7) are deviation-amplifying loops. Change in the organization is the result of such loops, because any initial increase (or decrease) in any concept loops back to that concept as an additional increase (or decrease) which, in turn, leads to more increase (or decrease). Thus, in loop (5), an increase in research quality improves researcher satisfaction. Increase in satisfaction of researchers allows, in turn, to improve the retention of the best researchers. Finally, the improvement of retention of the best researchers improves research quality. Loops (2) and (3) are deviation-countering loops [4]. The stability of the organization is the result of such loops. In the case of loop (2), for instance, an increase of resources for research can lead to an increase of salaries which, in turn, reduces the resources allowed to research. If this reduction is not enough to compensate the initial increase of resources, then a residual increase of salaries takes place which, in turn, reduces the resources, and so on, until a balance between the

92 initial increase of resources and salaries is reached. Thus, deviation-countering loops are useful for stabilizing the growth generated in an organization. To summarize, we can conceptualize an organization of agents as a "whole" composed of loops of influences. This is a wholistic approach in which the "whole" constrains the concepts and the relationships between them. By achieving this, we obtain a dynamic system in which deviation-amplifying loops are responsible for change and deviation-countering loops are responsible for stability of the organization. Using these loops, an individual strategist can direct strategic change in the desired direction. This can be done by (1) choosing and changing a loop or (2) choosing and changing a set of loops [4]. In the next section, we provide rules that allow to make inferences in the context of causal maps. In this way, we can determine the type of loop in an organization of agents. 2.5

Discussion

We have clearly shown how and why causal maps are useful in multiagent environments. The formal aspect which is presented in the next section, provides a formal procedure for those maps. After presenting the formal model, we will explain how to use it by means of the previous examples. It is important, however, to point out that the causal map approach adopted here is generally applicable to an environment that is not rapidly changing. In other words, the causal maps considered here are relatively stable. It is also convenient to point out that the approach adopted here seems inappropriate to open environments where agents enter and leave the group dynamically. The main reason is that it is very difficult to update causal maps and to assure coherence between concepts. There are also some other assumptions about CMs that must be considered [23]. The most important are (1) pairwise influence and (2) superposition. The first assumption requires relations in a C M to be binary relations. The second means that the result of applying together C1 and C2 is the same as applying C1 and C2 sequentially. Finally, it is important to point out that CMs can be derived from different sources, including documents, questionnaires, grids, and interaction and communication between agents.

3

Formal

Aspects

For the sake of understanding and using the cognitive maps in multi-agent environments, here are some definitions that should be remembered. A more rigorous version of these definitions will be given in the next section. D e f i n i t i o n 1. A cognitive map C M := (C, A) is a directed graph which represents an agent's assertions about its beliefs with respect to its environment. The components of this graph are the points (C) and the arrows (A) between those points (points and arrows are defined below).

93 Definition 2. A point represents a concept variable, which may be a goal option of any agent. It can also represent the utility of any agent or the utility of its group or organization, or any other concept that may take on different values in the context of multi-agent reasoning.

3. An arrow represents causal relations between concepts. It represents a causal assertion of how one concept variable affects another. As stated in the Introduction, causal links are labelled by elements of the set {+, - , 0, $ , @, 4-, a and ?}. Definition

Definition 4. A path from variable Vl to variable vn is a collection of distinct points, Vl, v2,..., vn, together with the arrows vlv2, v2v3,..., Vn-lVn. A path is trivial if it consists of a single point. Definition 5. A cycle is a nontrivial path together with an arrow from the terminal to the initial point of the path. Definition

6. Definition 6: An acyclic CM is a graph that has no cycle.

7. An indirect effect is the result of combining direct effects of relationships that are in a sequence. For instance, from vi +---+vj +~ Vk, there is Definition

an indirect effect vi +~ v~. Usually, this operation is called "multiplication" (*) and its rules are: +*y=y 0*y=0

ifyER, ify 9

- * - = +,

(1) (2) (3)

a 9 y = a if y 9 n - { 0 } , (4)

9 distributes over U x*y=y.x, ifx, y 9

(5) (6)

where R is the set of all the four types of causal relations, i.e., R = {+, - , 0, ?}. These six rules seem to be reasonable. For example, rule (2) says that, if vi has no effect on vj (x = 0), it is natural that vi has no effect on Vk (x * y = O) no matter the effect of vj on Vk (y 9 R). Finally, rule (5) says that * distributes over U, as for instance, - 9 0 = { - } * {0 U - } = ( { - } * {0}) U ( { - } * { - } ) = {0} u {+} = . . 8. The total effect of vi on variable vj is the sum of the indirect effects of all the paths and cycles from vi to vj. The rules governing the sum (D are the following: Definition

01y = y if y e R,

(7)

a[y=a x[x=x

(8) (9)

+1-

ify 9 if y 9

= ?,

xly = ylx x, y e R. I distributes over U

(10)

(11) (12)

94 Here also R is the set of all the four types of causal relations, i.e., R = { + , - , 0, ?}. Similarly, these rules are also reasonable.

D e f i n i t i o n 9. The valency m a t r i x V of a C M is a square matrix of size n, where n is the number of concepts in the corresponding cognitive map. Each element vii can take on the values {+, - , 0, | O, • a and ?} to characterize the relationship between elements i and j of C M . Evidently, i and j of V correspond to two concepts of the C M , and vii characterizes the relationships between these two concepts. D e f i n i t i o n 10. The total effect m a t r i x V t is the matrix which has as its i j th entry the total effect of vi on vj. This matrix can be calculated from the direct effects matrix with the operations of "addition" and "multiplication" defined by 2:

( v I w ) , j -- ~ l w ~ j , (13) ( v , w ) i j = (v/1 9 Wlj){(vi2 9 w 2 j ) l ... I(Yin * W n j ) . (14) Notice that operations * and I, between elements of matrices, obey to rules (1)(9) defined above. In these conditions, we have V 1 = V; V n = V * V n - 1 ; and Vt :

V ] V 2 I V 3 ... I V n - 1 .

The previous rules which determine indirect and direct effect can be used by autonomous agents for reasoning about coordination between agents in realworld applications as for instance the example of Section 2.1. Such rules are also useful in the case of reasoning about subjective views (see Section 2.2). In this case precisely, agents can use those rules to determine (1) areas of consensus and (2) unilateral views. Finally, the previous formal model can be also used to make a decision, particularly in a distributed environment as shown in Section 2.3. As explained in this section, the decision maker should calculate the total effect of each decision on the utility variable. Those decisions that have a positive total effect on utility should be chosen, and decisions that have a negative effect should be rejected. Advice on other total effects can be based on heuristics. Adopting this procedure for the example of Section 2.3 gives the following matrix of size 2 • 2 (keeping only the relevant entries) involving two decision concepts (DC), D1 and D2, and two utilities considered as value concepts (VC), namely, Utility of G12 and Utility of P1. DC\VC

D1 D2

Utility of G12 Utility of P1 + +

Thus, P1 perceives (1) decision D1 as having a positive effect on Utility of G12 and a negative effect on her utility; (2) decision D~ as having a negative 2 Vik is the ik th element of the matrix V.

95 effect on Utility of G12 and a positive effect on her utility. In these conditions, it depends on how P1 and P2 want to cooperate and how do they rank Utility of G12 and Utility of P1. If we assume, for example, that Utility of G12 is more important than utility of P1, then decision D1 would be preferred. Conversely, D2 would be the preferred decision if utility of P1 is more important than utility of G12. Finally, our formal model can also be used on the example about the wholistic view of an organization (see Section 2.4). This allows the identification of the type of loops (deviation-amplifying or deviation countering) and the development of a strategic plan to change a wholistic system by changing its loops. More precisely, if Vi+ = + (or ~), then there exists at least one deviation-amplifying loop through node i. Similarly, if Vi+ = - (or @), then there exists at least one deviation-countering loop through i. Finally, if Vi+ = d: (or any relation containing +, such as ?), then node i belongs to both an amplifying and a countering loop. Strategic changes to a wholistic system can be made by changing a loop or a set of loops [4]. Of course, the loop to be changed should be a weak loop which is loosely coupled to the system. Changing a loop (from deviationamplifying to deviation-countering, or vice-versa), can be done by (1) adding, removing, or replacing a node; (2) changing the label of a link. Finally, note that changing a set of loops is more subtle and requires strategies that avoid conflicts between agents. The difficult problem of changes in wholistic systems is an issue of our future work.

4

Conclusion and future Work

We have shown that causal reasoning in multi-agent is an important tropic which allows autonomous agents to reason about interrelationships among a set of individual and social concepts. We have also explained how such qualitative process of reasoning is important for coordination, negotiation and cooperation. Finally, we have proposed some formal aspects and particularly some intuitive inference rules that allow agents to reason from causes to effects. There are many directions in which the proposal work presented in this paper can be extended. (1) negotiation and mediation between agents in the context of reasoning about subjective views, (2) knowledge available to or necessary to agents in the case of nested causal maps [10], (3) reasoning in and on the wholistic approach, (4) reasoning on social laws particularly for qualitative decision making and coordination. Another option is to study "fuzzy relations" between agents' concepts [16,30,31]. Our approach might be extended in this direction to take into account many degrees and vague degrees of influences between agents such as: none, very little, sometimes, a lot, usually, more or less, etc.

Acknowledgments This work was supported in part by the Natural Sciences and Engineering Research Council of Canada under grants OGP-0121634.

96

References 1. R. Axelrod, (Ed.). Structure of Decision: The Cognitive Maps of Political Elites. Princeton University Press, 1976. 2. A. H. Bond and L. Gasser, (Eds). Readings in Distributed Artificial Intelligence. Morgan-Kaufmann, San Mateo, CA, 1988. 3. M. G. Bougon. Uncovering Cognitive Maps: The Self-Q Technique. Privately printed handbook, Penn State Univ., 1986. 4. M. G. Bougon and J. M. Komocar. Directing strategic change: a dynamic wholistic approach. In A. S. Huff, (Ed.), Mapping Strategic Thought. Wiley and Sons, 1990, pp. 135-163. 5. J. W. Bryant. Hypermaps: a representation of perception in conflicts. Omega, 11, pp. 575-586, 1983. 6. J. W. Bryant. Modelling alternative realities in conflict and negotiation. J. Opl. Res. Soc., 35, 11, pp. 985-993, 1984. 7. D. M. Buede and D. Ferrell. Convergence in problem solving: A prelude to quantitative analysis. IEEE Trans. Syst., Man, Cybern., vol. 23, 1993, pp. 746-765. 8. K. S. Decker. T/~MS: a framework for environment centered analysis and design of coordination mechanisms. In G. M. P. O'Hare and N. Jennings, (Eds), Foundations of Distributed Artificial Intelligence, Wiley InterScience, 1996, pp. 429-448. 9. E. H. Durfee. Planning in distributed artificial intelligence. In G. M. P. O'Hare and N. Jennings, (Eds), Foundations o] Distributed Artificial Intelligence, Wiley InterScience, 1996, pp. 231-246. 10. E. H. Durfee. Blissful ignorance: knowing just enough to coordinate well. Proc. of the First Int. Conf. on Multi-Agent Systems, MIT Press, San-Francisco, USA, 1995, pp. 406-413. 11. C. J. Eden, and D. Sims. Thinking in organizations. Macmillan, London, 1979. 12. R. Fagin, J. Y. Halpern, Y. Moses, and M. Y. Vardi. Reasoning About Knowledge, MIT Press, 1995. 13. P. J. Gmytrasiewics and E. H. Durfee. A rigourous, operational formalization In Proc. of the First Int. Con]. on Multi-Agent Systems, 1995, pp. 125-132 14. J. Hart. Comparative cognition. In [1]. 15. G. A. Kelly. The Psychology of Personal Constructs. New: Norton, 1955. 16. B. Kosko, Neural Networks and Fuzzy Systems. Prentice Hall. 1992. 17. B. Laasri, S. Lander, and V. Lesser. A generic model for intelligent negotiating agents. Int. J. Intell. Coop. In]. Syst. 1(1), 1992, pp. 291-318. 18. V. R. Lesser and D. D. Corkill. Distributed problem solving. In S. C. Shapiro, (Ed.), Encyc. o] Arti/. Intel., Wiley, New York,1987, pp. 245-251. 19. J. C. C. McKinsey. Postulates for the calculus of binary relations. Journ. Symbolic Logic, 5, 1940, pp. 85-97. 20. B. Moulin and B. Chalb-draa. An overview of distributed artificial intelligence. In G. M. P. O'Hare and N. Jennings, (Eds), Foundations of Distributed Artificial Intelligence, Wiley InterScience, 1996, pp. 3-55. 21. H. J. Miitler. Negotiation Principles. In G.M.P. O'Hare and N. Jennings, (Ed.), Foundations o] Distributed Artificial Intelligence, Wiley InterScience, 1996, pp. 211-230. 22. K. Nakumara, S. Iwai, and T. Sawaragi. Decision support using causation knowledge base. IEEE Trans. Syst., Man, Cybern. vol. SMC-12, 1982, pp. 765-777. 23. K. S. Park and S. H. Kim. Fuzzy cognitive maps considering time relationships. Int. J. Human-Computer Studies, 42, 1995, pp. 157-168.

97 24. L. L. Ross and R. I. Hall. Influence diagrams and organizational power. Admin. Sci. Q., vol. 25, 1980, pp. 57-71. 25. R. G. Smith. The contract net protocol: high-Level communication and control in a distributed problem solver. IEEE Trans. on Computers, C-29(12), 1980, pp. 1104-1113. 26. T. Smithin and D. Sims. Ubi Caritas?--Modeling beliefs about charities. Eur. J. Opl Res., vol. 10, 1982, pp. 273-243. 27. K. R. Sycara. Multiagent compromise via negotiation. In L.Gasser and M. N. Huhns, (Eds), Distributed Artificial Intelligence, vol. 2., Morgan Kaufmann, Los Altos, CA/Pitman, London, 1989, pp. 119-137. 28. K. E. Weick, The social Psychology of Organizing, Reading, MA: Addison Wesly, 1969. 29. M. P. Wellman. Inference in cognitive maps. Mathematics and Computers in Simulation, 36, 1994, pp. 1-12. 30. W. R. Zhang, S. S. Chen, and R. S. King. A cognitive map based approach to the coordination of distributed cooperative agents. IEEE Trans. Syst., Man, Cybern., vol. 22(1), 1992, pp. 103-114. 31. W.R. Zhang. NPN fuzzy sets and NPN qualitative algebra: a computational framework for bipolar cognitive modeling and multiagent analysis. IEEE Trans. Syst., Man, Cybern., vol. 26(4), 1996, pp. 561-574.

The Reorganization of Societies of A u t o n o m o u s Agents Norbert Glaser 1 and Philippe Morignot 2 i C R I N / C N R S - I N R I A Lorraine, BP 239, F-54506 Vandoeuvre-l~s-Nancy Fax: +33 (0) 3 83.27.83.19 Tel: +33 (0) 3 83.59.30.83 gmail: [email protected] 2 ICS-FORTH Science and Technology Park of Crete Vassilika Vouton, P.O.Box 1385 GR 711 10 Heraklion, Crete, Greece Fax: +30 81 39 16 01 Tel.: +30 81 39 16 00 [email protected]

A b s t r a c t . This paper investigates the skills of autonomous agents to reorganize their society as answer to environmental changes. The reorganization of an agent society can be motivated by the desire to reduce conflicts within inter-agent cooperation and to increase the efficiency in achieving goals. Our interest is centered on situations where new agents want to join an existing agent society which has established conventions for agent cooperation. Joining an agent society means that the society can draw benefits from the interaction with the new members having competencies which are complementary to the society ones; both, the society and the new members, need to agree on a new convention. We introduce a method for reorganization based on the principle of punishment : a society punishes or favors the behaviors of its new members.

Keywords:

1

A u t o n o m o u s agents m o d e l i n g , r e o r g a n i z a t i o n , a d a p t a t i o n , reinforcement

Introduction

In the l i t e r a t u r e a b o u t m u l t i - a g e n t s y s t e m s ( M A S ) , people t a l k on one side a b o u t a single society of agents, on the o t h e r side p e o p l e s t u d y t h e b e h a v i o r of one agent; the l i t e r a t u r e a b o u t artificial intelligence h a n d l e s this p o i n t , even if M A S does not. These studies a n d m o d e l s do not cover t h e real world which is c o m p o s e d of m a n y societies of agents where agents can m i g r a t e a m o n g t h e m . A g e n t s b e l o n g i n g to a society are said to have a c o m m o n c u l t u r a l backg r o u n d ; the society is itself coherent a n d its agents are o b s e r v e d to respect social n o r m s [20]. Nevertheless, w h a t is coherent in one society m i g h t n o t e be a c c e p t e d in a n o t h e r one; a n d all seems to be right. We d r a w f r o m h i s t o r y t h a t

99 some societies grow, others disappear by lack of adaptation to new environmental conditions. This means at a more abstract level that societies split from large old confederations, or they build new confederations for synergy enhancement. In this paper we propose a scheme for the reorganization of a society of agents due to the migration of agents among societies. More precisely, we focus on the simplest event that reveals the existence of other societies to a given society: the integration of a new agent, coming from another society, into a given society. We propose a simple model for a society expressed by a set of roles which are assigned to each agent. A role has a double interpretation : it reflects the competencies that an agent has and that the agent should provide to the other agents. The process of identifying individual roles can be seen as the agreement on a convention between agents. Change of conventions among agents means dynamically modifying the structure of the society in order to maintain a level of coherence. In a coherent society, the agents are able to combine their actions towards some unifying goal. We speak of deliberative agents which are aware of their roles. We propose furthermore a model for an autonomous agent that encapsulates reactive, cognitive, cooperative and social competencies. Autonomy is one of the fundamental characteristics of an agent [10]. Autonomous agents need to cooperate when they cannot achieve global goals individually, or when individual goals can be collectively achieved at lower costs [1]. Conflicts may arise during agents' cooperation; they are generally solved by negotiation and coordination strategies [6,17,24]. Nevertheless, social competencies are useful for agents to reduce occurring conflicts through the reorganization of their society. The acceptance of an agent by a society depends on the fact that the utility of the society increases. The society disposes a mechanism of punishment and favoring to make agents agree on roles. On the other hand, an agent joins only a society if its own utility increases, too. The paper is organized as follows. First, we present a model for agents, one for societies and one to explain how agents are part of a society. Then, we present a mechanism to analyze the integration of one agent into a society. Third, an example is presented. Finally, we briefly sum up our contribution.

2 2.1

Static M o d e l s An Individual Agent

An individual agent has four basic competencies rising from reactive, cognitive to cooperative and social ones. Figure 1 illustrates how the four basic competencies of an agent can be encapsulated into an agent model composed of four distinct layers [13,12]. Layered agent models are a very useful approach since we can express specific requirements for each layer during design and run time; it is also a very common approach for the design of multi-agent systems [23]. At the reactive level, an agent performs basic actions depending on its perception and received events. At the cognitive level, an agent can perform individual

100

/

-~--,-/

//

/

/

/ / / / / Layer / . 9 protocols . no conflict update ~c~176176 n ~ g ~ I rUo~::te//~ Cooperation tiafionmethods / /.,,/I 9 / / L a y-e/ r / planningmathods/~------'~'tntaragent / /jointp*an / / / I conflict ~. / / Cognitive / ." " / / 9 update Social

Laye;S.,...,.ct,v~ //f

.e..,v:

.av,oo// lav"

I o~,To~.

..... /

/

Layer

Fig. 1. Competencies of an Agent

action plans to achieve some non-trivial goals. It can exhibit expert behavior during the execution of these plans; however, it cannot handle conflicts which may arise with other agents. The agent can be qualified as a specialist [7]. At the cooperation level, an agent has protocols for the cooperation with other agents, it can build joint plans and solve conflicts; a cooperative agent is said to have a static role in a multi-agent society. Finally, at the social level an agent has full social behavior : it can agree with other agents on the assignment of roles; social agents are said to have the competence to reorganize within their society. In defining social knowledge, we refer the reader to [5]. D e f i n i t i o n ( A g e n t ) . An agent al is a quadruple having reactive, cognitive, cooperative and social competencies, C ~ = { B H Vi , P S Mi , C P Mi , S C Mi }. B H V~ represents a set of behaviors, P S M i a set of methods for problem-solving, C P M i a set of methods for agent cooperation, and S C M i a set of methods for the reorganization of agent societies. We associate each competence cj E C a' with a confidence value c f i ( c j ) expressing how much the agent ai believes in this competence. D e f i n i t i o n ( W o r l d ) . All defined agents represent the world W -- { a l , . . . , an} without considering their grouping into societies. The available competencies in the world are the union of all individual competencies, CB,P,C,S = ~J C ~ aiEW

2.2

Agent Society

We consider a society as a grouping of agents having common interests. An interest can be the desire or need to share resources and competencies. Within a society agents have different roles reflecting the competencies necessary for accomplishing it. This competence-based approach comprises deliberative agents which are aware of the roles they are playing and of those they want to play. Comparing artificial agents to real-world ones, we recognize that an agent is found to be part of various societies, i.e. a company, an association and a

101

family, each of them more or less organized. This has to be reflected by different types of roles which are given by the structure of a society. Moreover, we need to consider mimic agents and societies influenced by chaotic and unintended effects. The macroscopic-microscopic model of Haken [14] could be a promising and elegant solution. Definition (Society). A society S is a set of roles which identify the positions which individual agents ai E W can play, S = { r l , . . . , rn}. The set A represents the agents being part of the society. A example for a society which offers three possible roles to agents is S = {rmanager,rexpert, roperator }- Figure 2 illustrates such a society. The interpretation is the following : the manager distributes tasks to one or several experts for solution and to one or several operators for execution. The manager needs cooperation methods, the expert problem-solving methods, and the operator reactive behaviors. We assume that the definition of the different roles belonging to a society are part of the social knowledge of an agent.

organisation = {manager, expert, operator}

9" " " '

committed roles

""'",

desired roles

expected roles

|

%%'~"""~176 ......................... 9~176176176176176176176 Fig. 2. Different types of roles

Definition (Role). A role rj is defined by the competencies associated to it, rj = {ck I ck ~ CB,p,c,s}. Each individual agent is able to play different roles in a society depending on its individual competencies C a~. An agent having reactive behaviors and cooperation methods can fulfill the role of the manager and of an operator. On the other hand, the society to which the agent belongs can demand that the agent has to play the role of a manager. This example underlines that we need to distinguish between three types of roles. Definition ( D e s i r e d Role). The desired role r~ is the role that the agent wants to play. The role has a benefit for the agent. The set of desired roles of an agent is R~'. Definition ( E x p e c t e d Role). The expected role re is the role that the society expects the agent to play. The expected role is that role that is of benefit for the society. The set of expected roles of a society is Res.

102

D e f i n i t i o n ( C o m m i t t e d R o l e ) . The committed role rc is the role that an agent has committed to play within in the society. The set of c o m m i t t e d roles of an agent is R~'. We assume that each single agent has, as part of its social knowledge, the information about societies, i.e. types of structures for a society and how to build them. Fiske [9] introduces four basic relational frames or structures, i.e. communal sharing, equality matching, authority ranking and market pricing. We view them as basic frames from which societies can be derived. An agent completes this knowledge in interacting with other agents which can have different knowledge about structures. More complex roles can emerge through role-combination from the initial role set; agents are also found to cooperate to fulfill a role together. Moreover, the recombination of the agents' competencies can make new roles emerge. Haken's model could be a promising approach for the emergence of roles and structures within an agent society : simple changes at a microscopic level representing the competeneies, for example, would make roles emerge at a macroscopic level.

2.3

Convention between Agents and Society

We introduce the notion of a convention to describe how agents form a society. In fact, a convention is some sort of social norm which allows agents to increase the utility of a society and also their own one. At a generic level, our notion of convention is close to the understanding of [20] about cultural models which

have powerful influence in structuring actors' interaction with the environment. The environment is in our case the world of all agents. The cultural background may be expressed by the type of utility function each agent has. We explain this point later oil. We define a convention as a distribution of roles between agents. A convention can be an implicit agreement on a distribution of roles of which the agents become aware after some time. In a primitive society agents are found to build a convention just in behaving in a similar manner. Moreover, in a XXX society, for example, a convention may emerge from the initial random activity of the agents. We consider in this paper the case of established societies where conventions have been stated by some of its members. D e f i n i t i o n ( C o n v e n t i o n ) . A convention is represented by the set of c o m m i t t e d roles on which the agents have agreed between each other, Cs = {(ai, rj) [ ai E

W A r j 6 R~'} C W x S. This definition supposes that an agent needs to have background knowledge about possible structures of societies. This claim is reasonable as we have discussed in the previous section. Taking the example with S = {rmanager,re~:pert,roperato,, } illustrated in Figure 2, a possible convention is Cs = {(al, rrnanager), (a2, re,pert), (a3, roperator)}. A convention describes the structure of a society, or in other words its organization. The roles assigned to the agents by a convention cannot be seen as

103

independent labels for the agents but they are related to each other. For example, [19] identified an authority and communication relationship within an organization composed of functional entities having three possible roles, i.e. coordinator of actions, executer of actions and provider of expertise. How do agents now agree on a convention to form an organisation ? This question highlights that we need something more, something to express the motivation of a society to accept/refuse an agent and the motivation of an agent to accept/refuse a role. A very useful means is the definition of utility functions. 3

Dynamic

3.1

Model

The Reorganization of a Society

A typical example which requires the reorganization of a society is the situation where one agent wants to join an existing society. Several questions arise : does the new member progressively adapt itself so as to internalize the organisation of the society it lives now in ? Or does the society adapt itself so as to reflect the internal organisation of the agents it is composed of ? In the latter case there needs to be a common part in the agents that tend to group to form a society. - The first question requires a mechanism where the society does not modify itself and rejects the candidate agent until it matches the already established convention. The members of the society punish and favors the candidate agent until it has adapted to the convention. - The second question requires a mechanism where the society accepts a modification of its convention so that the candidate agent can be integrated into the society. The candidate agent rewards the members of the society for having accepted to do that. In both situations, it is the candidate agent which performs the action to derive an (expected) role from the society, i.e. from the convention of the society; it is not the society which proposes a role to the candidate agent. The candidate agent acts as a social and autonomous entity [3] having its own goals; it is nevertheless liable to influencing. The creation of a new convention includes the possibility that roles are redistributed among the remaining agents. D e f i n i t i o n ( R e o r g a n i z a t i o n ) . The reorganization within an agent society S happens through the modification of the established convention Cs and the creation of a new convention C s, for new society S'. There are two cases : 1. Agent ai leaves the society, C S, = Cs \ {(ai, rj)}. t

2. Agent ai joins the society, C s, = Cs U {(ai, rj)} A rj E R~ . If the society were to modify itself due to the loss of an agent, we believe this phenomenon would be represented by reversing the model for agent's inclusion that we present here. However this points is left for future research.

104

.................... s ................................. Cs

I

puoishmen avo.og i

...--"

Fig. 3. Principles of Reorganization

The principle of our reorganization method is illustrated in Figure 3. Agent a4 wants to join the agent society A = {al, ae, a3] having organized as S = {rl, r2] with rl is played by agent a2 and r2 by agents al, 3. i.e., the agents have established the convention Cs = {(al, r j , (a~, rl), (a3, r2)}. The society either favors the integration of the new agent, i.e., it accepts the desired roles R~ 4, or its rejects it until the agent has accepted the expected roles from the society R~s. 3.2

Utility Functions

We have already mentioned that the agent and the society need some additional information which motivates their reorganization. This information is introduced through utility functions assigned to agents and the society. Utility functions were found to be a simple but elegeant model for choosing among courses of actions (plans) for an agent [8,26]. The utility function of an agent may take many variables into account, among others, its life time, its resource production and consumption, and including the degree of satisfaction and usefullness of an agent's goals. If goals lead to alternative courses of actions, an agent pursues a goal that increases its utility. On the society level the utility function may reflect the quality of the convention convention established between the agents including basic knowledge about basic social knowledge about structures of societies. D e f i n i t i o n ( U t i l i t y of a S o c i e t y ) . The utility of a society depends on the utility of its convention//(Cs), on the cost for reorganization Cr and on the absolute utility of a society Us.

U(S) :: Us + U(Os) - G

(I)

105

This definition distinguishes the intrinsic utility of the society and the cost of any change, hence enabling to model more or less conservative society. We give a simple definition of Cr; Constraint Propagation Techniques (see for example [29]) constitute one direction to perform this evaluation on a network of symbolic roleconnected agents. We give below two examples of definition of Cr and 1A(Cs).

Cr .U(Cs) :=

grolechanges

Nrole,

~ CM(ai,rj) (ai,rj)ECs

(2)

(3)

C.h/t(ai, rj) expresses the commitment of the agent ai to role rj. Definition ( U t i l i t y of an A g e n t ) . The utility of agent depends on the roles it desires, the roles it has committed to and the confidence it has its roles. It is defined by the utility of a role it.

Ll(ai, rj) := D(ai, rj) * CiT(ai, rj)

(4)

with :D(ai, rj) is the desire of agent ai to role rj. CgV(ai, rj) is the confidence of agent ai in role rj. These two definitions allow us to clarify when an agent integrates into a society and when a society accepts that. P r i n c i p l e 1. A society chooses to integrate an agent that increases the society's utility. P r i n c i p l e 2. An agent chooses to integrate into a society that increases the agent's utility. An agent integrates in a society through the establishment of a new convention between the members of the society and the new candidate. We express this by the first equation. E q u a t i o n 1. A candidate agent ai E W integrates into a society if it matches the expected role. A simple threshold mechanism between the expected role and the agent's competencies is used :

CJ:(ai,re) = ~

cf~(ck) >>se

(5)

CkEre

A society draws benefits from the integration of a candidate agent; on the other hand, an agent draws benefits from integrating into a society. This is expressed by the following two equations.

106

E q u a t i o n 2. The expected role to which the candidate agent has committed augments the utility of the society. 3rj C S CI Re8, so that U(S) 1".

E q u a t i o n 3. The desired role to which the candidate agent has committed and which was accepted by the society augments the utility of the agent. 3rj E S A Rag' , so that U(ai, rj) ~.

(7)

Given this mechanism for a society to produce an expected role for an incoming agent, an optimal policy can be drawn for a candidate agent. If the agent is able to (1) simulate the society's utility and (2) simulate the function that produces the expected role given an agent's competencies, it can plan the expected role that it will be given. Thus an optimal policy for this agent is to find how the society's utility increases most and proposes the most relevant competencies. This learning mechanism by which an agent learns an internal model of the target society requires for the agent a large amount of knowledge on this society. The agent is little likely to acquire enough of it, since it is not a member of this society. We would assume that cooperation with a small number of members of the society would improve this learning phase. 3.3

T h e M e c h a n i s m s of Integration

At the beginning of this Section we have presented two situations for the integration of an agent into a society. Briefly, the first one concerned the adaptation of the candidate agent through punishment/favoring; the second one the adaptation of the society. These mechanisms are illustrated below. In speaking of society, we have its member agents in mind. M e c h a n i s m for I n t e g r a t i o n 1 ( A u d i t i v e a g e n t ) . The candidate agent is an auditive one which tries to derive the best fitting role for its integration into the society. 1. 2. 3. 4. 5.

The The The The The

agent analyzes the society's utility. agent analyzes the society's convention. agent tries to find a modified convention increasing the society's utility. agent proposes this role for itself. society reward the kind character of the agent.

M e c h a n i s m for Integration 2 (Selfish agent). The candidate agent is a selfish one which insists on its desired roles during the integration into a society. 1. The society analyzes its utility with the desired roles of the candidate agent.

107

2. 3. 4. 5.

The The The The

society punishes/favors the desired roles of the agent. agent makes modifications to its desired roles. agent reapplies if its utility has increased during these modifications. society rewards the agent.

Punishment means that the society tells the agent that it has no utility for integrating into it; the other way round, favoring means that the society says to the agent that it has an utility for integrating into it. Both can be realized by reinforcement learning as Q-learning. We refer the reader to [30] for a description of Q-learning and to [18] for an overview about learning techniques. The use of such a learning method requires the definition of reward functions. R e w a r d F u n c t i o n s . We reward the following situations during the integration of an agent. -

Positive feedback on a society if an agent joins. Negative feedback on a society if an agent leaves. Positive feedback on an agent if it accepts to join a society. Negative feedback on an agent if it decides to leave a society.

The previous feedbacks have a positive or a negative influence on the utility of the society and of the candidate agent.

4

Proposed

Experiments

The experiments can be realized within the C o M o M A S environment which is a knowledge engineering and simulation environment for the development of multi-agent systems [12]. The C o M o M A S approach proposes a set of conceptual models at an implementation-independent level. This work extends the COMMONKADS approach [28] for the development of multi-agent systems. The knowledge engineering module produces descriptions of the conceptual models in the Conceptual Modeling Language (CML) [27] which has been extended. The simulation module integrates an updated and extended version of MICE, an agent simulation environment introduced by [21]. We have conceived within this environment a layered-agent architecture [13] encapsulating specific competencies at distinct layers. We are studying several scenarios of various complexity in which existing societies reorganize through the integration of new agents. Take the following scenario as example. S c e n a r i o . Given a startup company composed of one manager and several programmers, A = {al,a2, a3, a4} with the convention Cs = {(el, rraanager), (a2,3,4, rprogrammer)}. Now, a candidate agent a5 wants to enter the company.

108

-

P a r t A. The company has a small size, the expected role is a programmer, r~ = Vp~og. . . . . . . . A candidate agent qualifying for a programmer has no problems to get accepted; on the other hand, a higher qualified candidate agent is refused since the society does not recognize the need to pay a high salary. Nevertheless, the higher qualified agent is employed if it accepts the role of a programmer. P a r t B. Having reached a certain size, the company needs to reorganize in order to maintain its efficiency; it favors now the candidature of higher qualified agents having manager skills, r~ = rmanager.

We illustrate below the principles in applying our approach to model this scenario. It can be implemented within the C o M o M A S environment as a set of social agents using our layered agent model [13]. The society is given by Cs = {(al, r . . . . g~), (a2,3,4, rprograrnmer)} with the expected thresholds for the roles, Sm~,~ag~ = 0.7 and Sp~og~ . . . . = 0.4. The society has the utility //(S) given by the utility of the convention l l ( C s ) = ~ (ai, rj) e Csg]td(ai, rj) + Us since no reorganization has yet occured. The utility of the society depends on the commitment of the individual agents. The candidate agent has the desired roles R~ 5 = {rma,~g~, r p r o g ~ m , ~ } with an equal confidence in both roles, g ~ ( a s , r , ~ a g ~ ) = 0.8 and CN(as, rp~og. . . . . . . ) = 0.8. The agent has a greater desire to be manager than to be programmer, T~(as, r m ~ g ~ ) = 0.9 and 7)(a5, rprog~m,,,r = 0.4. The role rj increasing the utility of the agent U(as, rj) is the role of the manager, //(as, rm~,~g~) = 0.72. Given the expected threshold values by the society for admitted roles, the candidate agent can play both roles, rm~n~g~ and rprogramme r . - In part A, the society refuses the desired role of the candidate agent, r d -~ rmanager based on its social knowledge that a small society with two managers has a lower utility than a society with one manager; this is modeled by the absolute utility values Us. The refusal is expressed by a negative feedback to the desired role of the agent. - In part B, the society has increased considerably through the employment of a huge number of programmers. Agent al has decreased its commitments to play role r,,~a~ag~r and the utility of the society U(S) has decreased despite the rewards from the integration of new agents. The society favors the role of a manager rrnanager. 5

Related

work

Working on the reorganization within an agent society requires the definition of social attitudes and social knowledge of individual agents, the investigation

109

how agents build societies and how they maintain them. Noteworthy is the work of Conte and Castelfrancchi [5], Rao and Georgeff [25], Hobbes [16] and Gauthier [11], and Etzioni [8]. The design and implementation of social agents can be supported by an appropriate conceptual model; this aspect is addressed, among others in [2,12]. The definition of social commitment and belief as introduced by [5] makes us think to integrate them as sources of knowledge for the determination of roles by agents. This is in particular interesting for an agent society to determine if its members fulfill the expected roles. In fact, we have based the utility of a convention between agents on their individual commitments. The definition of belief and desire by [25] illustrates a similar understanding of the social knowledge agents have. Instead of funding the definition on the objectives and goals of an agent, we use the more abstract notion of a role; this expresses that an agent having competencies is part or wants to be part of a society. The definition of an agent society by a set of roles, derived from the agents' competencies, allows us to model the dynamic behavior of such a society. In fact, a society evolves over time, i.e. roles disappear and new ones appear. Close to our notion of conventions are the network of contracts proposed by Hobbes [16] which are in his view the only way to avoid belligerent attitudes among humans. The notion of mutual commitments [11] expresses a similar understanding. Our work is complementary to [4] which studies the conditions, the situations and the mechanisms for the emergence of organizations between agents. The notion of an organisation used in that paper is close to our notion of a convention. Autonomous agents are required to be able to modify their local knowledge in a such a way that they can agree with other agents to build a society. [22] presents a way to structure an agent so that it can easily adapt itself to new environments. Thereby an environment can be a society, since the agent's structure includes social and cooperative aspects. [15] proposes an architecture for adaptive agents. Aspects of this architecture are comparable to our layered architecture [13]. Interesting is also the work of [8] who is a successful example for having introduced economical concepts into artificial intelligence, actually planning and the design of agents. His propositions on utility functions could allow us to integrate new elements into the definition of the utility of a society, for example, to model th choice of an agents among various societies, with each requiring various plans for integration.

6

Conclusion

The rapid evolution of the world in which societies exist makes it necessary for them to adapt. We consider the reorganization of a society as a response to an evolution of their living conditions. In particular, we consider the situation where an individual agent intends to be a member of a society. This paper presents a new approach for the reorganization of a society of autonomous agents. We define a society as a collection of agents having agreed

110

on a distributed of roles through a convention. A candidate agent having reactive, cognitive, cooperative and social competencies applies for membership. Our method is based on the principle of punishment and favoring : the society of agents favors those roles of the candidate agent which augments the society's utility and punishes the others. The candidate agent is rewarded after its integration through the growth of its own utility function. A realistic scenario illustrates the application of this society model and of this reorganization method. The scenario can be straightforwardly validated within the C o M o M A S multi-agent simulation environment which we have conceived. Future work will be centered on the extension of the current method for reorganization which considers the integration of an agent into a society. The splitting and merging of societies at a more general level is an interesting and necessary topic. We are analytically characterizing our model in full decision-theoretical framework. We are also experimentally studying the use of our method within different existing agent architectures available at our laboratories. In particular, the application of social reorganization to software agents collaborating via the Internet seems promising.

7

Acknowledgments

This project was partly funded from the European Community. The first author was funded through a doctoral fellowship within the H u m a n Capital and Mobility program, contract ERB4001GT930339, and has received the qualification MarieCurie fellow delivered by the European Union. The second author is currently supported by an E R C I M fellowship.

References 1. M. Adler, E. Durfee, M. Huhns, W. Punch, and E. Simoundis. AAAI workshop on cooperation among heterogeneous intelligent agent. A I Magazine , 13(2):39-42, 1992. 2. T. Bouron and A. Collinot. SAM: a model to design computational social agents. In Proc. 10 th ECAI, Wien, Austria, 1992. 3. C. Castelfranchi. Guaranties for autonomy in cognitive agent architectures. In ECAI-94 workshop on ATAL, pages 56-70, Amsterdam, The Netherlands, 1994. LNAI Series, 890. 4. V. Chevrier, R. Foisel, N. Glaser, and the research group MARCIA. Autoorganisation: Emergence de structures. In Journdes du PRC IA sur les SMA, 1995. CRIN report 95-R-290. 5. R. Conte and C. Casteffranchi. Cognitive and Social Action. UCL Press, 1995. 6. K.S. Decker and V.R. Lesser. Designing a family of coordination algorithms. In Proc. I ~t [CMAS, pages 73-80, San Francisco, California, 1995. 7. J. Erceau and J. Ferber. L'intelligence artificielle distribute. La Recherche, 22:750758, 1991.

111

8. O. Etzioni. Embedding Decision-Analytic Control in a Learning Architecture. Artificial Intelligence, 49:129-159, 1991. 9. A.P. Fiske. The four elementary forms os sociality : Framework for a unified theory of social relations. Psychological Review, 99:689-723, 1992. 10. S. Franklin and A. Graesser. Is it an agent, or just a program?: A taxonomy for autonomous agents. In ECAI-96 workshop on ATAL, pages 21-36, Budapest, Hungary, 1996. LNAI Series, 1193. 11. D.P. Gauthier. The Logic of Leviathan. Oxford University Press, 1969. 12. N. Glaser. Contribution to Knowledge Acquisition and Modelling in a Multi-Agent Framework - The CoMoMAS Approach. PhD thesis, Universit6 Henri Poincar6, Nancy I, 1996. 13. N. Glaser, V. Chevrier, and J.-P. Haton. Multi-agent modeling for autonomous but cooperative robots. In Proc. 1 ~t DIMAS, pages 175-182, Cracow, Poland, 1995. 14. H. Haken. Synergetics. An Introduction. Springer Verlag, Berlin, 1990. 15. B. Hayes-Roth. An architecture for adaptive intelligent systems. Artificial Intelligence: Special Issue on Agents and Interactivity, 72:329-365, 1995. 16. T. Hobbes. Leviathan. Oxford University Press, 1948. 17. N.R. Jennings. Towards a cooperation knowledge level for collaborative problemsolving. In Proc. 10 th ECAL pages 224-228, Wien, Austria, 1992. 18. L. Kaebling. Learning in Embedded Systems. MIT, 1993. 19. E. LeStrugeon, C. Kolski, R. Mandian, and M. Tendjaoni. Intelligent agents. In Second International Conference on the Design of Cooperative Systems, pages 331344, Juan-les-Pius, France, 1996. 20. G. Mantovani. Social context in HCI: A new framework for mental models, cooperation, and communication. Cognitive Science, 20:237-269, 1996. 21. T.A. Montgomery and E.H. Durfee. Using MICE to study intelligent dynamic coordination. In Proc. 2 ,~a IEEE Conf. on Tools for AI, pages 438-444, 1990. 22. Ph. Morignot and B. Hayes-Roth. Why does an agent act: Adaptable motivations for goal selection and generation. In M. Freed and M. Cox, editors, A A A I Spring Symposium, Representing Mental States and Mechanisms, pages 97-101. Stanford, 1995. 23. J.P. Miiller. The Design of Intelligent Agents : A Layered Approach, volume 1177 of Lecture Notes in Computer Science. Springer Verlag, 1996. 24. E. Oliveira, F. Mouta, and A.P. Rocha. Negotiation and conflict resolution within a community of cooperative agents. In Internat. Symposium on Autonomous Decentralized Systems, Kawasaki, Japan, 1993. 25. A.S. Rao and M.P. Georgeff. BDI agents: From theory to practice. In Proc. 1 st ICMAS, pages 312-319, San Francisco, California, 1995. 26. S. Russell. Rationality and intelligence. In Proc. 1J th IJCAI, Montr6al, Canada, 1995. 27. G. Schreiber, B.J. Wielinga, J.M. Akkermans, W. Van de Velde, and A. Anjewierden. CML: The CommonKADS conceptual modelling language. In Proc. 8 th European Knowledge Acquisition Workshop, pages 1-25, Hoegaarden, Belgium, 1994. LNAI Series, 867. 28. G. Schreiber, B.J. Wielinga, R. de Hoog, H. Akkermans, and W. Van de Velde. Commonkads: A comprehensive methodology for KBS development. IEEE Expert, 9(6):28-37, 1994. 29. P. Tsang. Foundation of Constraint Satisfaction. Academic Press, 1994. 30. C.J.C.H. Watkins and P. Dayan. Q-learning. Machine Learning, 8:279-292, 1992.

Adaptive Selection of Reactive/Deliberate P l a n n i n g for t h e D y n a m i c E n v i r o n m e n t - A Proposal and Evaluation of MRR-planning Satoshi KURIHARA, Shigemi AOYAGI, and Rikio ONAI NTT Basic Research Labs. 3-1 Morinosato-Wakamiya, Atsugi, Kanagawa, 243-01 JAPAN E-mail:kurihara@square. brl. ntt. co.jp Abstract

This paper proposes and evaluates a methodology for multi-agent realtime reactive planning. In addition to the feature of conventional real-time reactive planning, which can react in a dynamic environment, our planning can perform deliberate planning when, for example, the robot has enough time to plan its next action. The proposed planning features three kinds of agents: a behavior agent that controls simple behavior, a planning agent that makes plans to achieve its goals, and a behavior selection agent that intermediates between behavior agents and planning agents. They coordinate a plan in an emergent way for the planning system as a whole. We confirmed the effectiveness of our planning by means of a simulation. Furthermore, we implemented an active vision system, which is the first stage of building the real-world agent, and used it to verify the real-world effectiveness of our planning.

1

Introduction

In this paper, we propose "multi-agent real-time reactive planning" (hereafter called MRR-planning) that can flexibly r e s p o n d t o dynamic environmental changes in order to implement a real-world application. In a dynamic environment, goals may change dynamically and the amount of planning time available may vary. To respond reactively to environmental changes, it is essential to incorporate the features of real-time reactive planning [1] [7]. Furthermore, to undertake deliberate planning properly, it is essential to incorporate the features of classical planning [6]. Our real-world application is called the active vision system (AVS) (mentioned in Section 5) that is designed to detect, trace, and if possible identify moving objects by using image and sound data. This system is mounted in an autonomous robot so the robot can chase an object. Relatively simple short-term goals as well as complicated, deliberate long-term goals are given dynamically.

113

For example, when a detected object moves quickly, the AVS must frequently control the video camera mechanically to trace the object. Reactive plavnlng is therefore used to trace the moving object reactively. However, when the detected object moves slowly, the AVS has enough time to identify the object by using deliberate planning. To identify the object, several kinds of high-load image processing are necessary. Moreover, the time available for planning varies according to the speed of the object. The system thus requires both reactive and deliberate planning. We propose an adaptive planning that can select reactive/deliberate planning efficiently in a dynamic environment. Important issues that must be addressed in order to implement the planning are (1) how the planning generates both reactive and deliberate planning results within a restricted time and (2) how the most appropriate plan is selected from among the results of the two kinds of planning. Several behavior-based approaches to perform reactive planning have been proposed [3] [S]. In those approaches a number of planning agents reactively generate individual plans by applying a "conditioned reflex response." These agents coordinate each other and decide which plan is the best depending on the environment. It is quite difficult, however to perform deliberate planning by using these approaches. Yamada [14] proposed an efficient form of deliberate planning using the probability of changing goals, but we cannot apply this planning because it is quite difficult to calculate all probabilities of changing goals. We therefore use the multi-agent framework: each agent generates a plan reactively or deliberately and also identifies the plan that should be selected. The most efficient feature of the multi-agent framework is that the system is established in a bottom-up fashion and the complex control of the system is established by coordinating agents. If we establish the above p]anning by applying central control, however only one central module would control all the reactive/deliberate planning. Reactiveness would thus be lost. In MP~-planning there are two kinds of planning agents; one performs reactive planning and the other performs deliberate planning. MRR-planning basically applies the framework of real-time reactive planning based on the behaviorbased approach. Moreover, efficient selection of reactive/deliberate planning is achieved by using a spreading activation model and a threshold/maximum mechanism. For example, reactive planning is selected when the robot faces an emergency situation and deliberate planning is selected when the robot has enough time to plan its next action. The most important feature of MRR-planning is that the effect of conventional close coordinations of agents is achieved through their non-close coordination. Therefore, in MP~t-planning, even if there are a lot of planning agents, their coordination costs remain low so that reactiveness is not lost. Section 2 explains RTT (Real-Time TileWorld) which is a simulator used to

114

9 Obstacle ~ Tile I[~ Energysupplybase mHole (score) 0 Enemyrobot 9 Playerrobot

Figure 1: RTT (Real-Time TileWorld) evaluate MRR-planning, and Section 3 explains MRR-planning. Section 4 offers a comparative evaluation using RTT. Section 5 explains the active vision system, which is a real-world application, and Sections 6 and 7 summarize future issues and the overall results of the research.

2

RTT: Real-Time TileWorld

We used an extented version of TileWorld [10] for evaluating the proposed MRRplanning. TileWorld was originally introduced to simulate an environment where environmental changes take place dynamically, and we added components in order to expand the number of possible dynamic environmental changes. As Figure 1 shows, RTT includes a player robot, tiles, obstacles, and holes (all of which exist in the conventional setup) as well as the newly added enemy robots and energy supply bases. Two enemy robots try to capture the player robot. The player robot has an internal store of energy, which decreases as the player robot moves. A large amount of energy is taken when the player robot is captured by the enemy robots, but the player robot can replenish its energy at the energy supply bases. When the player robot's energy store reaches zero, the game is over. The player robot has three goals to be achieved by reactive planning: escaping from the enemy robots, acquiring energy, and holding or dropping a tile which suddenly appears very close to the player robot. Furthermore, it has one goal to be achieved by deliberate planning: finding the route most suitable to getting a high score and moving for a long time without being captured by the enemy robots. To perform deliberate planning, the player robot uses the locations of tiles, the locations and scores of holes, the energy stores of the player robot, and the distances between the player robot and the enemy robots. Moreover, the player robot cannot predict behaviors of the enemy robots precisely and thus

115

Real World

~

delberale P-agecl ~,-~ oldemand1om~cutplm

ac~rai0n

Planning for deciding the next behavior ~ae~r~10m

P-agent1: planning for escaping from enemy robots. P-agent2: planning for acquiring energy. P-agent3: planning for scodng reactively. P-agont4: planning for scodng deliberately.

J

B-agent1: go up.

B-agent5: hold tile. B-agant2: go dram. B-agent6: embed hole. B-agont3: go left. B-agent7: acquire energy.

B-agent4: go right.

Figure 2: MRP~-planning (for the player robot in P~TT) may have to give up the plan obtained by deliberate planning. The player robot therefore needs adaptive planning which can efficiently select reactive/deliberate planning.

3 3.1

MRR-planning Behaviors

of Agents

MR-R-planning consists of three kinds of agents: planning agents (P-agents), behavior selection agents (BS-agents), and behavior agents (B-agents) (see left side of Figure 2). The algorithm for MRR-planning consists of two parts: a cycle of performing a planning by P-agents and spreading activation energy to B-agents, which are necessary for P-agents to execute their plans, and a cycle of selecting a B-agent depending on the activation energy and actuating a B-agent to carry out an action in the real world. 3.1.1

T h e b e h a v i o r o f P-agents

A P-agent that performs reactive planning (called a reactive P-agent) plans, for example, which direction the robot moves in the next step during one planning cycle (called a one-step plan) as in the conventional real-time reactive planning [8] and executes the one-step plan immediately. The goal is achieved by repeatedly planning and executing one-step plans. Each planning time is very short so that the robot can move reactively, but the total plan towards the goal may not be optimum.

116

A P-agent that performs deliberate planning (called a deliberate P-agent) plans a consecutive series of plans to achieve its goal during one planning cycle (called a total-steps plan) as in classical planning (similar to STRIPS [6]). An optimum plan is therefore always obtained, but this kind of P-agent cannot guarantee reactiveness. As mentioned above, MRR-planning provides reactiveness basically by applying the framework of conventional real-time reactive planning, so that even if a deliberate P-agent plans a total-steps plan during one planniug cycle, it executes the one-step components of that plan in turn. The deliberate P-agent may, however have to give up the total-steps plan because of a dynamic change of environment, and it may have to re-perform deliberate planning from the beginning. This is necessary for the MRR-planning to be reactive. To execute the plan, a P-agent must request a B-agent to, for example, control the actuator. The P-agent therefore spreads the activation-energy to B-agents which achieve the P-agent's plan. 3.1.2

How to set the activation-energy?

The activation-energy is a positive real number and is calculated depending on the level of demand that must be achieved by the plan. Currently, reactive P-agents use such a measure that if the level of demand increases, the activationenergy increases according to a quadratic function (until the activation-energy exceeds the threshold). And deliberate P-agents use such a measure that if the level of demand increases, the activation-energy increases according to a linear function whose slope is small (in this function the activation-energy does not exceed the threshold) (see right side of Figure 2). Moreover, we can change the characteristic of MRR-planning by changing a relation of both the above measures: for example, if the measure of reactive P-agents is higher than that of deliberate P-agents, the characteristic of MRR-planning becomes reactive. And if the measure of deliberate P-agents is higher than that of reactive P-agents, the character of MRR-planning becomes deliberate (see right side of Figure 2). In current MRR-planning, these measures and several parameters for the spreading activation model must be set by hand. 3.1.3

T h e b e h a v i o r o f a BS-agent

The BS-agent determines which B-agent is to be activated. This determination is based on the planning of P-agents using a spreading activation model and a threshold/maximum mechanism. The B-agent is in charge of simple behavior that it can execute by itself, and it does not have a mechanism enabling it to perform planning independently. The BS-agent selects the most suitable B-agent depending on only activationenergy, which means that the BS-agent does not consider the content of a P-

117

2-[5]

P-agent

BS-agent

Figure 3: Algorithms of the P-agent and BS-agent

agent's plan. Synchronization between P-agents and BS-agents is therefore not necessary. The behavior of B-agents, however makes the state of the robots and the environment change, so P-agents reflect the changes to their planning by using information from sensors and B-agents (see left side of Figure 3). In the next section, we explain the cycle of the BS-agent. Up to now we have been explaining that the P-agent spreads the activation-energy to B-agents, but the P-agent actually spreads the activation-energy to a slot corresponding to the B-agent in the BS-agent (see right side of Figure 2).

3.2

Selection of

reactive/deliberate planning

The BS-agent makes possible efficient selection of reactive/deliberate planning. The algorithm for this is as follows (see right side of Figure 3).

[,4] In the BS-agent, there are several slots each of which corresponds to a Bagent. When a P-agent finishes its planning it spreads activation-energy to a slot corresponding to a target B-agent. [B] It is likely that several P-agents spread activation-energy to the same slot. In such a case, activation-energy is added. [0] The BS-agent repeats the following loop until a B-agent is selected and activated. [1] Set all slots at NULL. [2] Execute the following two processes (CUR1, CUR2) concurrently. CURl-[3] Check each slot in turn until activation-energy is spread to all slots. CURI-[~] If activation-energy has been spread to all slots, the B-agent whose slot has the largest activation-energy is activated by the BS-agent. This means that one (or several) of the P-agents which spread activation-energy to this Bagent achieved its demand of planning deliberately. CURl-[5] Suspend CUR2 and go back to [0] CUR2-[3] Check each slot in turn until the slot value becomes larger than the threshold.

118

Deliberate situation

[activation-energy]

UP

~

THRESHOLD:J200]

player [obot~,,,~A*(3)[90] ~,L_

(4)[70]

(2)[50] DOWN

p-a~m~: (1). (2)

.T~

T P-agera2: (a), (4)

MAX: RIGHT[190] < THRESHOLD B-agent:RIGHT is tbctiv6ted

Reactive situation UP

~ (3)[90]

THRESHOLD:[200] P-agent1: (1), (2)

T] ~ I (4)[70 p-aoe~:(3).(4) LE(1)[2101 I1~ RTGHT P-~e.t4:sfill~p~anningl T (2)[190]

LEFT[210] > THRESHOLD

B-agent:LEFTis activated

DOWN Figure 4: Selection of reactive/deliberate planning

CUR2-[4] If one of the slot values has became larger than the threshold, the BS-agent immediately activates the B-agent corresponding to that slot. This means that one (or several) of the P-agents which spread activation-energy to this B-agent, achieved its demand of planning reactively. CUR2-[5] Suspend CUR1 and go back to [0] We can describe the features of MRR-planning as follows. B - a g e n t s Even though the amount of activation-energy spread to the slot of a B-agent is small, that B-agent can be activated if the total activation-energy becomes the largest. Thus, the maximum mechanism is selected and the demand of the P-agent, which spreads the small activation-energy, can be achieved (by [B], CURI-[,~]) (see Figure upper part of 4). However, in an emergency situation, for example, a reactive P-agent spreads high activation-energy, so the total activation-energy in the slot of a B-agent becomes higher than the threshold. In this case the threshold mechanism is selected (by CUR2-[~]) (see lower part of Figure 4). All P-agents have equal conditions and, in MRR-planning, restraint and admission control, caused by the hierarchy relation of agents, are not necessary against the subsumption architecture [3]. In MRR-planning, agents do not carry out close information exchanges, so even when the number of agents increases the possibility of impairing the real-time characteristic remains low. Selection

of

119 player robot 1 1

enemy robot 11 enemy robot2 1 1 1 2

2 3

2 3

TICKS 15, 20, 25 15, 20, 25

15, 20, 25 15, 20, 25

Player robot can move one step during one scheduling time. "Enemy robot 1 = 2" means that enemy robot 1 can move one step during two scheduling times. At this point, "1 TICK = 10 rag' Table 1: Environmental setup in RTT Selection of r e a c t i v e / d e l i b e r a t e p l a n n i n g When there is enough time for the robot to plan its next behavior, the amount of activation-energy spread from the P-agent to the B-agent is small, and the total activation-energy does not exceed the threshold. Therefore it is possible to select a B-agent after all P-agents have completed their planning (by CURI-[~]). This means that in this situation a deliberate P-agent can finish its planning and spread activation-energy stably. Even in this situation, however reactive P-agents can repeatedly plan and spread activation-energy while deliberate P-agents continue their planning; therefore, reactiveness is maintained (by [.4]'). However, when there is not enough time for the robot to plan its next behavior, the activation-energy f~om the P-agent is high and exceeds the threshold. In this case, a B-agent with this activation-energy is activated immediately so that MR/t-planning can perform reactive planning (by CUR2-[.~]). S u p e r v i s i n g B-agents The B$-agent supervises the aggregation of B-agents, which constantly form competitive relationships (however, competition can take place at the B$-agent level).

4

Evaluation

In RTT, to evaluate the performance of MRR-planning, the movements of the player robot and the two enemy robots are controlled by a common scheduler, and the player robot can perform a one-step planning and its execution during one scheduling time. If the player robot can finish planning early in one scheduling time, the player robot must wait the remaining time. The simulation was made with many environmental setups (see Table 1). In the reactive environmental setup, the enemy robots could move as fast as the player robot; and in the deliberate environmental setups, the player robot could move much faster than the enemy robots. Figure 5 shows the results of movement of P-agents. This graph indicates the alterations of P-agents that request activation of certain B-agents. This result enabled us to confirm that

120 P-agent1 P-agent2 P-agent3 P-agent4

1

i

l

L 20

30

,10

~SO

l

L 7@

60

Reactive environmental setup (Probot=

], Erobot7

= 7, E r o b o t 2

[ 80

Number of steps

= !)

P-agent1 P-agent2 P-agent3 P-agent4 0

10

20

30

40

50

60

70

Deliberate environmental setup (Probot=

l , Erobot l =3, Erobot2

Number of s~ps

=3)

Figure 5: P-agent movement sequence MRR-planning could select reactive/deliberate planning effectively in a dynamic environment. To evaluate MRR-planning using RTT, we prepared a planning that used the conventional reactive planning as a target of comparison. This planning is based on classical planning and, as in the case of IRMA mentioned in Ref. [10], reactiveness was enhanced by preparing in advance several plans to meet the environmental changes. We implemented four P-agents: the first agent performed reactive planning to run away from the enemy robots; the second agent performed reactive planning, depending on the remaining energy to go to the energy supply base; the third agent performed reactive planning to respond reactively to the appearance of tiles and holes; and the fourth agent performed deliberate planning to get higher scores and move for long time. The activation-energy was based on two distances: that between the player robot and the enemy robots, and that between the player robot and the energy supply base. The following seven B-agents were prepared: an agent to capture the tile; an agent to lower the tile; an agent to embed the tile in the hole; and agents in charge of movement in four directions (up, down, left, and right). Our evaluation was based on (1) the obtained scores and (2) the number of steps the player robot was able to move. The obtained scores were averaged to produce final results (Figure 6). Scores Under the deliberate environmental setups, MRR-planning obtained scores as high as those obtained by the the conventional reactive planning. This indicates that in the deliberate environmental setups there were no major differences between the performances of MRR-planning and the conventional planning. Under the reactive environmental setups, however the scores obtained

121 Number of steps

P~n~scored

800 +ml &Si ,,,,y

m,

700

"-'2..r

5o

+

\/

I

_

.

.

.

.

.

I N I ~ - -

2~----

-

Figure 6: Points scored and number of operational steps by the conventional reactive planning decreased rapidly, while MRR-planning achieved higher scores in all reactive environmental setups. This result enabled us to confirm that MRR-planning can select reactive/deliberate planning effectively in a dynamic environment (left side of Figure 3). N u m b e r o f s t e p s To evaluate the number of steps, we set up a rule such that the player robot succeeded in its mission if it could move up to 1,000 steps. Under reactive environmental setups, conventional reactive planning required more planning time for one movement than MRR-planning required. Therefore, the energy of the player robot in the conventional reactive planning became fully depleted and the game ended immediately. Under MRR-planning, in contrast, the player robot was able to maintain stable operations in all environmental setups. This result confirmed that MRR-planning could adapt to dynamic environmental changes more flexibly(right side of Figure 3). The results of these evaluations confirm that MRR-planning can perform not only reactive planning but also deliberate planning and can achieve goals in a stable manner regardless of dynamic environmental changes.

5

Application

to

Real-world

Systems

Making a comparative evaluation using RTT enabled us to verify the effectiveness of MRR-planning at the simulation level. We then verified its effectiveness with applications designed for the real world by putting MRR-planning to actual use.

5.1

What

is t h e A c t i v e

Vision

System?

The active vision system (AVS) we have implemented is designed to detect moving objects in the real world and collect valuable image and sound data autonomously.

122

ActiveCIinlmll-I

AcUveCamem~l

i m0)

SGI Indigo2

801 Indy

Figure 7: Flow diagram of the AVS

Figure 8: Photograph of the AVS

So to speak, AVS is an "adaptive moving object detecting system." In the movie "Jurassic Park," there was the following line: "dinosaurs detect only moving objects." The purpose of the AVS is similar to that of the vision system of dinosaurs. The following section explains the functions achieved by AVS. The AVS consists of two ActiveCameras and one pair of stereo microphones (see Figures 7 and 8). The two cameras in the system have different functions. 9 Functions of ActiveCamera-I (narrowing the focus) -Detect moving objects and make the camera follow them (pan/tilt functions). -Zoom in to magnify the movement of small objects (zoom function); the captured object is small. -Judge whether the detected object is human. If so, the camera continues monitoring; if not, it does not follow the movement of the object. Functions of ActiveCamera-II (always focusing on a wide range) -Detect moving objects and make the camera follow them (no zoom functions). -Follow up in response to sound information (pan function only). 9 Functions relating to the coordinative operations of the two cameras -Supplement for the limited scope of each ActiveCamera. 9

The ActiveCamera incorporates a lift that can be computer-controlled via the RS-232C and includes pan and tilt functions. It also incorporates a zoom function, that is computer-controlled. One workstation is used to control one camera unit, and interaction between workstations is achieved via a UNIX socket by a communication function. The present AVS itself cannot move like a robot, so we are planning to load it into a robot. Although many studies have been carried out to make systems similar to the AVS [5] they tended to focus on each mechanism (for example, vision processing and sound processing) rather than mechanisms for coordination and integration.

123

Figure 9: AVS agent network Our purpose is to coordinate and integrate these mechanisms so that for each processing we can use suitable existing technology which has real-time characteristics. Reactive planning in the AVS is used for controlling an ActiveCamera when the degree of object movement is high, and deliberate planning is used for judging whether the detected object is human (which decision requires a long time).

5.2

Implementing

P-agents

Figure 9 explains each agent implemented in AVS. We implemented two sets of Mp~D~-plannings to control each ActiveCamera.

5.2.1

Moving Object Detection Agent (P-agent1, P-agent6)

This P-agent performs reactive planning to control the llft to make sure that the moving object comes to the center of the output frame. The P-agent first gets the interframe differential data by using image data from the real world. Then the center of gravity of the differential data is calculated and activation-energy is spread to the target B-agent so that the center of gravity shifts to the frame center. 5.2.2

A g e n t for Enlarging a n d R e d u c i n g M o v i n g O b j e c t s (P-agent2)

This P-agent zooms in/out using the differential data in such a way that moving objects always appear on the screen in an appropriate size. The P-agent calculates the decentralized value of the differential data and when the decentralized value is higher the camera zooms out, and when the decentralized value is lower

124

Figure 10: Image Processed it zooms in. Then the P-agent performs planning, depending on the decentralized value, to control the lift so t h a t the moving object comes to the center of the output frame.

5.2.3

Human Detection Agent

(P-agent3)

Whether the moving object is human can be determined by observing the color of its surface. At first this P-agent cuts out the smallest rectangle that contains a section experiencing movement by using the decentralized value. This rectangle is then divided into several segments, and we determine whether the sub-area contains a color corresponding to that of skin. Lastly, if there are black sub-areas on these sub-areas containing skin color, we think that this moving object may be a human. Moreover, the P-agent performs planning to control the lift so that the moving object comes to the center of the o u t p u t frame depending on the locations of these sub-areas. The processed image is shown on the right-hand side of Figure 10. Here, the sub-area of the face section is assessed as possessing a skin color. 5.2.4

M o v e m e n t T r a c e A g e n t (P-agent4, P-agent7)

This P-agent carries out planning so that both ActiveCameras are made to operate in a manner reflecting their individual movements. When ActiveCamera-I can detect nothing for a long time, for example, ActiveCamera-II makes planning to control ActiveCamera-I to move and zoom in the same way as ActiveCamera-II.

5.2.5

Sound Detection Agent (P-agent5)

First, only the sound emitted more than a certain power is filtered. Next, the dynamic measure [2] is used so that a voice signal that is mixed with other noise can be detected with high accuracy. More specifically: (1) when image information is lacking so that the camera is unable to detect moving objects, the camera can react to incidental noise; but (2) when the camera does detect moving objects, it does not react to accidental noise other than a human voice.

125

P-agent3

----

l

9

B

I

,

P-agent2

__

•

,

Am

:,-

Rapid

u I-

movement

P-agent1 Idle

9

B/

--"

P-agent4

- m.--..~.a

P-agent7

9

movement

V

9

P--agent6

- -

Idle

i

i

No movement

I 9

i

detected

P--agent5 0

200

400

e o o

Number

800 of steps

1000 Time

(3'35")

Figure 11: P-agent movement sequence

5.3

Behaviors of P-agents in the AVS

Figure 11 shows the results of the movement of P-agents. This graph indicates the alterations of P-agents that request activation of certain B-agents. Idle means that no B-agent is currently activated. P-agentl and P-agent2 need reactive planning and P-agent3 and P-agent4 need deliberate planning for ActiveCamera-I. And P-agent6 needs reactive planning and P-agent5 and P-agent7 need deliberate planning for ActiveCamera-II. When the movement of the object was rapid, P-agentl controlled ActiveCameraI without waiting for the results of planning performed by P-agent3, and when the movement of the object was slow, P-agent3 could perform its planning and decide that the detected object was human. When ActiveCamera-I could not detect the moving object, P-agent7 in ActiveCamera-II controlled ActiveCameraI. When ActiveCamera-II did not detect the movement of the object, P-agent5 performed planning to detect sound. To evaluate the robustness of MR_R-planning, we tested the AVS when the function of P-agent2 turned off. Figure 12 shows the results of movement of P-agents. The function of P-agent2 turned off at 500 steps so that the P-agent could not plan after 500 steps. Nevertheless, the AVS could work stably and P-agent3 could determine whether the detected object was human.

6

Future

Issues

We are currently planning to load the AVS on the B14 autonomous moving robot made by Applied AI Systems, Inc. To control the robot, a lot of P-agents, BS-agents, and B-agents are necessary to integrate the information from the AVS and the many sensors of the robot. Therefore, verification of the algorithm of MRR-planning becomes necessary because in these situations the loads of BS-agents become large.

126

P--agent3

-7,

P--agent2

:I- ""

"I

P--agent1 Idle

-

P--agent4 o

200

460

6 0 0

6 0 0

N u m b e r of steps

1 0 0 0

Time

(3"35")

Figure 12: Verify the robustness of MRR-planning Mechanisms for automatically learning two kinds of parameters are necessary: a parameter for the P-agent to calculate the activation-energy, and a parameter for the BS-agent to determine which B-agent is to be activated. At present, we plan to use the evolutionary approach. Moreover, if MRR-planning can store a history of the behaviors of agents, MRR-planning can re-use this history as experience to deal with new situations (similar to K-line). Lastly, more deliberate studies for the deliberate planning are necessary. The deliberate P-agent in the AVS can always reset its planning when the reactive P-agent's demand is achieved for each step. In other situations, however the deliberate P-agent may be able to or must continue its planning regardless of the selection of the reactive P-agents. In this case, it may happen that the planning of the reactive P-agents influences the deliberate P-agent which then must change its planning policy according to the influence of the reactive P-agents (so-called utility problem). A mechanism to resolve this problem is necessary.

7

Conclusion

This paper proposed a multi-agent real-time reactive planning (MRR-planning), the main features of which are that (1) reactive or deliberate planning is selected efficiently in a dynamic environment and (2) the selection mechanism is established by non-close coordinations of agents. In a simulation, we obtained results that were satisfactory in comparison with those of a planning that uses conventional reactive planning. We also implemented the active vision system, which is the first step in building a realworld agent, and used it to verify the real-world effectiveness of MRR-planning. We plan to focus our future study on building the real-world agent described in Section 4.1. We are planning to load the AVS on an autonomous moving robot, have the robot move around in the environment we live in, and let it interact with us. Finally, we are continuing research on building and retrieving a multimedia database [12], [15]. And, for this database, we are currently planning to use the autonomous robot as an intelligent tool for acquiring data from the real world.

127

Acknowledgements W e would like to thank to our manager, Dr. Kenichiro Ishii, and the researchers of the Semantic Computing Research Group.

References [1] P.E. Agre and D. Chapman: What Are Plans For?, Designing Autonomous Agents, pp. 17--49. Bradford-MIT, 1990. [2] Aaron E. Rosenberg and Frank K. Soong: Recent Research in Automatic Speaker Recognition, Advances in Speech Signal Processing, in Sadaaki Furui and M. Mohan Sondhi, pp. 701-738. Dekker, 1989. [3] R.A. Brooks: Intelligence Without Reason, Proceedings oflJCAI-91, pp. 569-595, 1991. [4] Mark Boddy and Thomas Dean: Solving Time-Dependent Planning Problems, Proceedings of IJCAI-8g, pp. 979-984, 1989. [5] Christel M, Stevens S, Kanade T, Mauldin M, Reddy R, and Wactlar H: Techniques for the Creation and Exploration of Digital Video Libraries, Multimedia Tools and Applications (B. Furth, ed.), Vol. 2, ch. 17, Kluwer Academic, 1995. [6] R.E. Fikes and N.J. Nilsson: STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving, Artificial Intelligence, vo]. 2, pp. 189-208, 1971. [7] Michael P. Georgeff and Amy L. Lansky: Reactive Reasoning and Planning, Proceedings of AAAI, pp. 677'-682, 1987. [8] Pattie Maes: The Agent Network Architecture (ANA), SIGART Bulletin, vol. 2, no. 4, pp. 115-120, 1991. [10] Martha E. Pollack and Marc Ringuette: Introducing the TileWorld, Experimentally Evaluating Agent Architectures, Proceedings of AAAI, 1990. [12] Toshihiro Takada, Mitsukazu Washisaka, Shigemi Aoyagi, and Rikio Onai: Twoway Linkage System between Video and Transcribed Text for Protocol Analysis,

Proceedings of the International Conference on Multi Media Japan'96 (MMJ'96), 1996. [14] Seiji Yamada: Controlling Deliberation with Success Probability in a Dynamic Environment, Proceedings of AIPS'96, pp. 251-258, 1996. [15] Mitsukazu Washisaka, Toshihiro Takada, Shigemi Aoyagi, and Rikio Onai: Video/Text Linkage System Assisted by a Concept Dictionary and Image Recognition, Proceedings of the ICMCS'96, 1996.

Distributed Problem-Solving as Concurrent Theorem Proving* Michael Fisher 1 and Michael Wooldridge 2 1 Department of Computing, Manchester Metropolitan University, Manchester M1 5GD, United Kingdom M. F i s h e r @ d o c . m m u . a c . u k

2 Zuno Ltd, International House, Ealing Broadway Centre London W5 5DB, United Kingdom mj w @ d l i b , c o m

Abstract. Our principal aim in this paper is to demonstrate that distributed prob-

lem solving may fruitfully be viewed as concurrent theorem proving. We begin by introducing a novel agent-based approach to concurrent theorem proving, and then describe Concurrent METATEM, a multi-agent programming language whose model of computation is closely related to that used within the theorem proving approach. An extended case study is then presented, wherein we demonstrate how a multi-agent planning system can be implemented within the agentbased theorem proving framework. We then show how extensions and refinements of the planning system can easily be accommodated within this framework. We conclude with a detailed discussion of related work, from both the multi-agent systems community and the (concurrent) theorem proving community.

1

Introduction

Problem solving is a fundamental issue in AI and, along with game-playing, is perhaps the oldest research topic in the discipline. The view of problem solving as theorem proving has a long and influential history in AI, going back at least to the work of Green [ 12]. This deductive view of problem solving has been particularly influential and useful in AI planning research. Distributed problem solving (DPS), wherein a group of decentralised semi-autonomous agents cooperate to solve problems, is perhaps the paradigm example of multi-agent activity, and is certainly the most-studied process in distributed AI [5]. However, while many logic-based approaches to distributed AI have been described in the literature, we are aware of no work that has explicitly proposed viewing distributed problem solving as concurrent, agent-based theorem proving. This is perhaps due to the lack of an appropriate agent-based computational model for concurrent theorem proving. The main aim of this paper is to show how distributed problem solving may usefully be treated as concurrent theorem proving. To this end, we utilise a recently developed general framework for agent-based theorem proving, and demonstrate how this * This work was partially supported by EPSRC under grant GR/J48979.

129

framework may easily be implemented in a multi-agent programming language. As an example of distributed problem solving, we consider distributed planning, representing both the basic system and various extensions and refinements within our framework. The remainder of this paper is structured as follows. In w we present a general framework for concurrent theorem proving [10], and show how it can be used as the basis for distributed problem solving. In w we introduce Concurrent METATEM [8], a multi-agent programming language whose computational model is closely related to the agent-based theorem proving framework, making the language well-suited to implementing the technique. In w we present an extended case study, in which we show how a range of planning techniques may be implemented using the concurrent theorem proving technique, and finally, in w we discuss related work and provide concluding remarks.

2

A Framework for Concurrent Theorem Proving

In this section, we introduce a novel, agent-based approach to theorem proving. In the interests of brevity, we only give an outline of the technique here. A fuller description, together with a number of technical results associated with the technique, is given in [10]. The basic idea behind the approach is easily illustrated by means of a simple example. Consider the following set of propositional Horn clauses 3: 1. p 2. -~p V q V - ~ r 3. -~p V -~q V -~r 4. -~p V r

Using classical resolution, it is easy to derive the empty clause from this set. In our framework, theorem proving proceeds by allocating each clause i E { 1 , . . . , 4} to an agent, Agi. The agents we consider are self contained reasoning systems, encapsulating both data and behaviour, which are able to execute independently of each other and communicate via broadcast message-passing [ 19]. In our theorem-proving context, these agents behave as follows: -

-

any agent representing a clause containing just a positive literal should pass that information (via broadcast message-passing) to all other agents; upon receipt of a message, agents add this message to their current set of clauses, and repeatedly apply classical resolution in order to transform these clauses, broadcasting any new literals generated or contradictions produced.

Now consider the behaviour of the agents in the simple example given above, when we apply this behaviour. As the agents first begin executing, agent Agl, containing only the proposition p, broadcasts the message p to all other agents. Once p has been received, each agent transforms its clauses by applying classical (in this case, unit) resolution, and the configuration becomes: a Note that, although this example is both propositional and a set of Horn clauses, the technique is more general, being complete for full (non-Horn) first-order logic.

130

(Agl) (Ag2) (Ag3) (Ag4)

: : : :

p q V ~r ~q V ~ r r

Agent Ag4 then broadcasts r as new information. After this message reaches the other agents, they update their clauses, and the configuration becomes:

(Agl) (Ag2) (Agz) (Ag4)

: : : :

p q -~q r

Finally, agent A92 then broadcasts q, and upon receipt of this message, Ag3 generates a contradiction, which it then broadcasts. Since the empty clause has been derived, theorem proving is now at an end. Note that the theorem-proving activity is not dependent upon the order in which messages are sent. If the empty clause can be derived from the initial clauses, then it will be, as long as all messages sent are guaranteed to (eventually) arrive. Also note that since broadcast message-passing is used as the basic communication mechanism, other agents are able to view (and utilize) the intermediate deductions produced by each agent. Hence, global deductions are carried out collectively by the set of agents. While many variations of theorem-proving utilising concurrency have been developed [ 13], few use such a model of computation. Those that are related are discussed in w

2.1

Generality and Correctness

Despite its simplicity, this approach is just as powerful as classical resolution: communication patterns in the concurrent system match the proof steps in a sequential resolution refutation. In the case of Horn clauses, the messages between agents correspond to positive literals while in the case of full classical logic, the messages themselves correspond to Horn clauses. In [10], we prove that the technique is refutation complete for full classical logics, and in addition show how it can be extended to first-order classical logic. The main result from [10] can be stated as follows. Theorem 1 (Correctness [10]). If a set of clauses, A is distributed amongst a set of agents as described above, then a false message will eventually be generated by at least one of the agents if and only if, A is unsatisfiable. Agents may also represent and exchange messages about both the heuristics currently being employed and the organisation of the agent society. Such meta-level information can be used improve the efficiency of the theorem proving process, and allows cooperative, competitive, or opportunistic problem solving structures to be implemented and investigated [ 10].

131

2.2

Efficiency and I m p l e m e n t a t i o n

One potential criticism of the technique described above is the use of broadcast message passing, which is often regarded as too demanding of communication bandwidth to be used in practice. However, in spite of the use of broadcast, the system need not be flooded with messages. Not only is it possible to structure agents so that related information only occurs within one agent, but also, by grouping agents containing related parts of the problem-solving capability together, the number of messages generated can be greatly reduced [10]. Branching in the search space is replaced by additional broadcast messages. Thus, in architectures where broadcast is prohibitively expensive, the technique may prove to be inefficient. However, most contemporary architectures provide efficient multicast mechanisms (indeed, many distributed operating systems are based upon this mechanism: see, for example, [3]).

3

Implementing the Framework

Having outlined the general model of concurrent theorem proving, we now describe the high-level programming language in which problem-solving applications will be represented. A Concurrent METATEM system [8, 9] consists of a set of concurrently executing agents, which communicate through asynchronous broadcast message-passing. The internal computation mechanism for an agent is provided by the execution of temporal logic formulae [2]. We begin by giving a brief overview of temporal logic, followed by an outline of the execution mechanism for temporal formulae. Temporal logic can be seen as classical logic extended with modal operators for representing temporal aspects of logical formulae. The temporal logic we use is based on a linear, discrete model of time. Thus, time is modeled as an infinite sequence of discrete states, with an identified starting point, called 'the beginning of time'. Classical formulae are used to represent constraints within individual states, while temporal formulae represent constraints between states. As formulae are interpreted at particular states in this sequence, operators which refer to both the past and future are required. The future-time temporal operators used in this paper are as follows: the sometime in the future o p e r a t o r - - ~qa is true now if ~ is true sometime in the future; and the always in the future operator - - D ~ is true now if qo is true always in the future. Similarly, connectives are provided to enable formulae to refer to the past. The only past-time temporal operators needed for the examples in this paper are as follows: the sometime in the past operator - - ~ ~p is true now if qo was true in the past; the beginning of time o p e r a t o r - - start is only true at the beginning of time; and the strong last-time operator - - OqD is true if there was a last moment in time and, at that moment, ~p was true 4. Concurrent METATEM uses a set of 'rules', couched in temporal logic, to represent agent's intended behaviour. These rules are of the form: 'past and present formula' =~ 'present or future formula' 4 A number of other operators are provided in Concurrent METATEM,though as they are not required for this paper, they will not be mentioned here; see [2, 9].

132

Consider the following rules, forming a fragment of an example Concurrent METATEM program. start ~ achieves(a) Ogoal(X) ~ ~planned(X) Otopgoal( Y) ~ subgoal( Y) V fact(Y) Here, both ' X ' and ' Y' represent universally quantified variables. Thus, we can see that achieves(a) is made true at the beginning of time and whenever goal(X) is true in the last moment in time, a commitment to eventually make planned(X) true is given. Similarly, whenever top9oal(Y) is true in the last moment in time, then either subgoal(Y) or fact(Y) must be made true. An agent's program rules are applied at every moment in time (i.e., at every step of the execution) and thus execution in a Concurrent METATEM agent can be distinguished from the logic programming approach in that refutation is not involved in the computation process and the model for the formula contained within the agent is constructed by following the temporal rulesforwards in time. Once the agent has commenced execution, it continually follows a cycle of reading incoming messages, collecting together the rules that 'fire' (i.e., whose left-hand sides are satisfied by the current history), and executing one of the disjuncts represented by the conjunction of right-hand sides of 'fired' rules. Each agent contains an interface describing both the messages that the agent will recognise and those it may send. For example, the interface

top (9oal, achieves)[planned, subgoal] : defines top to be the name of an agent in which {9oal, achieves } is the set of messages the agent will accept, and {planned, subgoal} defines the set of messages the agent can send. For a more detailed description of the execution mechanism underlying Concurrent METATEM, see [2, 9].

4

A Case Study: Distributed

Planning

In order to illustrate our approach, we show how planning problems can be represented within our model. We begin with an overview of AI planning; our presentation is relatively standard, and is based on [14]. First, we assume a fixed set of actions Ae = { c q , . . . , c~,~}, representing the effectoric capabilities of the agent for which we are developing a plan. A descriptor for an action c~ E Ac is a triple (Pa,D,, A~), where5:

- Pa C s is a set of sentences of first-order logic that characterise the pre-condition of c~; - D , _C s is a set of sentences of first-order logic that characterise those facts made false by the performance of a (the delete list); 5 We assume a standard first-order logic s

with logical consequence relation ' ~ ' .

133

-

A,~ C_ s

is a set of sentences of first-order logic that characterise those facts made

true by the performance of a (the add list). A planning problem (over Ac) is then determined by a triple (,4, O, "y), where: "4 _C/2o is a set of sentences of first-order logic that characterise the initial state of the world; - O : {(P~, D~, As) I a E Ac} is an indexed set of operator descriptors, one for each available action c~; and - 3' _C/Z0 is a set of sentences representing the goal to be achieved. -

A plan 7ris a sequence of actions 7r = (a 1 , . - . , an). With respect to a planning problem {A, 0 , 7 ) , a plan 7r = (cq,...,c~n) determines a sequence of n + 1 world models ,40, A 1 , - - . , An where: ,40 = "4

and

"4i = (,4/--1 k D~,)U A~,

for I < i < n.

A (linear) plan 7r = ( a l , . . . , an) is said to be acceptable with respect to the problem (A, O, "y) if, and only if, "4/-1 ~ P,~,, for all 1 < i < n (i.e., if the pre-condition of every action is satisfied in the corresponding world model). A plan 7r = ( a l , . . . , an) is correct with respect to {'4, O, 7) if, and only if, it is acceptable and An ~ 7 (i.e., if the goal is achieved in the final world state generated by the plan). The planning problem can then be stated as follows: Given a planning problem (,4, O, 7), find a correct plan

for (,4, O, 7). We will now demonstrate how the planning problem can be solved using the general concurrent theorem proving paradigm we described in w More precisely, in w we show how a Concurrent METATEM system can be generated to solve the planning problem in a top-down (goal-driven) manner. We then prove correctness of the approach. In w we give an alternative method for deriving a Concurrent METATEM system, that will generate a solution to the planning problem in a data-driven (bottom-up) fashion, while in w we consider refinements of the two approaches. 4.1

Goal-Driven

(Top-down)

Planning

In this section, we demonstrate how, from a planning problem (A, O, 7), a Concurrent METATEM system that will solve the problem in a top-down, goal-driven fashion can be systematically derived. We begin with a discussion of the various predicates that will be used, and an overview of the derived system structure. We use just four domain predicates (see Table 1). The predicate top-goal(...) represents the fact that its argument (a set of s sentences) is the top-level goal of the system. The unary predicate goal(...) is used to represent sub-goals; its argument is also a set of s sentences. The achvs(...) predicate takes two arguments, the first of which is a set of s sentences, the second of which is a plan; achvs(A', 7r) represents the fact that plan 7r, when executed from the initial world state ,4, will achieve ,4'. Initially, we shall assume that plans are linear (see w for more complex plans), and represent them using a PROLOG-like list notation. Finally, the predicate plan(...) is

134

top-goal(7) 9oai(7) achvs( A, 7r) plan("/, 7r)

7 is a top-level system goal 7 is a sub-goal plan ~ achieves A plan ~ is a correct planfor 7

Table 1. Domain Predicates

used to communicate a plan to the originator of the top-level goal: plan(7 , 7r) means that plan 7r, if executed in the initial world, will achieve "7Given a planning problem (A, O, 7), the basic generated system will contain [A[ + ]OI + 1 agents: one for each element of A and O, and one 'top-level' agent. The toplevel agent takes in a request for a plan to achieve 7, and sends out a message that creates a sub-goal 7. For each operator description ( P , , D , , A , ) C O, an agent a is created, which encapsulates knowledge about the pre- and post-conditions of a. This knowledge is represented by the two rules (TO1) and (TO2). The first of these, (TO1), is essentially a rule for sub-goaling: it is fired when a message is received indicating that a goal has been created corresponding to the post-condition of a; in this case, the rule causes a new sub-goal to be created, corresponding to the pre-conditions of a. At some stage, a sub-goal created by this process will correspond to an initial state of the world (otherwise, the top-level goal 7 is not achievable). This is where the third type of agent plays a part. For each sentence ~Pi E A, an agent initi is created, containing a rule which represents the fact that if ever qoi is a sub-goal, it can be achieved by the empty plan, '[]'. When such a rule fires, this information is propagated by sending the message achvs({~}, []). These base agents can also combine initial conditions, sending out composite 'aehvs' messages. Within each c~ agent, there will be a single (TO2) rule, characterizing the effect of executing c~; this rule will fire when a message achvs (A t, 7r) is received, such that A ~matches the pre-condition of c~. When fired, the rule will cause a message aehvs(A', [c~ ] 7r]) to be broadcast, where A" is the world-model obtained by executing c~ in A ' We shall now describe the derived system (and in particular, the agents and rules used) in more detail.

Top-level agent: For {A, O, 7), we create a top-level agent as follows. top-level(top-goal, achieves)[plan, goal] : ( TG1) 0 top-goal(7) ~ goal(7); (TG2) Oachvs(A, Tr) A ~ top-goal(7 ) A (7 C_ A) ~ plan(7, Tr). The agent top-level accepts 'requests' for plans in the form of a message top-goal(7 ), where 7 is the goal, as above. The rule (TG1) then simply propagates 7 as a sub-goal. The predicate top-goal would be given to the system by a user. Rule (TG2) simply characterises the plan predicate: ~v is a correct plan for 7 if 7r achieves 7. When the top-level agent is informed of a plan 7r that achieves the top-level goal 7, it sends a message plan(7, 7r), indicating that a plan for the goal has been found. Thus, rule (TG1) represents the input to the system, whereas (TG2) represents the output.

135

Base agents: Given an initial world model ,5 = {qOx, . . . , ~Pm}, we generate m agents, initl,..., initm, each containing a rule (TB1) showing that the initial conditions are achieved by the empty plan, together with a rule allowing the combination of relevant initial conditions (TB2). initi ( goal ) [achvs ] : (TB1) 9oal(~vi) =~ achvs( {~oi}, []); (TB2) Oachvs(A', []) A (qPi ~- `5') =~ achvs((`5' U {(pi}), []). Action agents: For each operator description (Pa, Da, As) 6 O, where both A~, = {(Pl,..., qo,~} and P~ = { r r we create an agent a as follows. a(goal, achvs)[goal, achvs] : (TO1) goal(~l) V . . . V goal(qom) =~ goal(C1) A . . . A goal(r (TO2) Oachvs(,5',Tr) A (P~ C_ ,5') =r achvs(((,5' \ D,~) U As), [c~ I 7r]). Rule (TO1) generates sub-goals: if a sub-goal is received that matches against the post-condition of c~, then this rule causes the pre-conditions of c~ to be propagated as sub-goals. Rule (TO2) defines the effect that action a has upon an arbitrary state that satisfies its pre-condition. This rule effectively restricts us to linear plans-- we consider non-linear planning in w It is important to note that, while this approach may seem inefficient at first, achvs messages are only initiated for members of ,5 that are required for one of the possible plans. Correctness In this section, we prove that the approach to top-down planning discussed above is correct, in that: (i) any plan generated by the system is correct, and (ii) a system is guaranteed to eventually generate a plan for the top level goal 3'- Alternatively, the correctness of this planning approach can be established via correspondence to the (complete) concurrent theorem-proving system [10]. Theorem 2. Any plan generated by the system given above is correct. More precisely, if the message achvs (,5', 7r) is broadcast in a system derived from a problem (,5, O, 7), then 7r is a correct plan for ,5'.

Proof By induction on the structure of plans. The base case is where 7r is empty; a message achvs(A', []) will only be sent by an initi agent, in which case A is true in the initial world, and will clearly be achieved by the empty plan. Next, suppose that 7r is of the form [c~ ] 7r'], and that if achvs(A", rr') is sent, then 7r' is correct for A". If achvs(A', [a I 7r']) is subsequently sent, then it must originate from a (TO2) rule within the agent a. In this case, it is easy to see from inspection of (TO2) that [a I 7r'] is correct for ,5'. Theorem3. If there exists a solution to the problem (A, O, 3'), then eventually, a system derived as above will find it. More precisely, if there exists a solution to (,5, O, 7), and the message top-goal(7 ) is sent, then eventually a message achvs(% 7r) will be sent.

136

Proof By induction on the length of successful plans. The base case is that 7 is directly achieved by the initial conditions of the system, and thus 9oal(7) generates an appropriate achvs(A', []) message (where "y C_ A'). Assuming that all problems requiring plans of length n - 1 can be solved, we assume that the plan a l , . 9 a n achieves the goal 7- Here, the message goal(7) reaches agent an, which recognises 7 and broadcasts appropriate subgoals. By the induction hypothesis, the subgoals will be solved and achvs(A", 7r) will eventually be received by an (where P~ C_ A"). The an agent will then broadcast the solution to % Theorems 2 and 3 together imply that a Concurrent METATEM program derived from problem (A, O, "/) using the above scheme will be totally correct.

4.2 Data-Driven (Bottom-up) Planning The operation of many implemented planners corresponds to the basic approach developed in the preceding section, in that they are goal-driven. Of course, there is an alternative, whereby a plan is developed in a data-driven manner. Many of the concepts are similar to the top-down planner (e.g., the various domain predicates retain their meaning). For this reason, our presentation will be somewhat more terse. Given a planning problem (A, O, 7), we now generate a Concurrent METATEM system containing IOl + 2 agents: one top-level agent, (as above), one agent for A, and one agent for each element of O. The system works by forward chaining from the initial state of the world, generating all possible plans and their consequences. Eventually, the desired plan will be generated. However, given that there are I01! simple linear plans possible for operators O, it is not difficult to see that this form of plan generation will, in general, be impractical.

Top-level agent: This agent simply awaits a plan achieving the goal 3'top-level(top-goal, achvs)[plan] : (BG1) O achvs(A', 7r) A ~ top-goal(7) A (7 C_ A') ~ plan(% 70. Base agents: Given the initial world A = { ~ 1 , . 9 9 , ~ r n }, we now generate an agent, init which broadcasts all the relevant initial information 6. Again, the agent contains a rule allowing the combination of relevant initial conditions (BB2).

initO[aehvs] (BB1)

(BB2)

:

Oachv,(

start => A im achvs({qoi},[]); =1

', []) A

r

achvs(( ' O

[]).

6 We here choose to use one base agent, rather than m base agents, in order to reinforce the fact that information can be distributed amongst agents in a variety of ways.

137

Action agents: For each operation descriptor (P~, Da, A,~) E 0 we generate an agent a, as follows: a( achvs)[achvs] : (BO1) O achvs(,4', 70 A (P~ C_ ,4') =:V achvs(((,4' \ D~) U A(~), [a I 7r]). The rule (BO1) is identical to (TO2), above. Thus, there are no rules for decomposing a goal to produce sub-goals. Goals can only be solved by the required combination of plan elements being generated bottom-up. Again, the correctness of this approach can be easily shown. 4.3

Refinements

We will now briefly outline a few possible refinements to the basic planning mechanisms discussed in the previous sub-sections.

Uni-directional top-down planning: When considering simple linear plans, generated using the top-down approach in w there is often no need to pass messages back through the action agents to achieve the final plan. Now, we extend the goal predicate with a second argument in which the partial plan for the current goal is stored. If the toplevel agent broadcasts goal(% []), then each action agent need only have the following rule for producing subgoals. n

goal( ,,O ^ (As

# O)

goat(A i=1

Thus, if any post-condition of the action occurs within a goal, then a new subgoal corresponding to the pre-condition is generated and the current partial plan is extended with the action. Each base agent now broadcasts the plan if it can reduce the goal completely:

goal(~i,r) ~ plan(Tr). In this way, once a goal is completely decomposed, the second argument to goal must hold the plan that achieves the goal.

Non-linear planning: The top-down and bottom-up planners sketched above generate basic linear plans. An obvious extension is to develop non-linear plans. To provide this extension, we simply allow the preconditions of an action to be satisfied by states derived from different routes. Thus, assuming P,~ is a set of n literals, qvl,. 9 ~n, we simply change (BO1)/(TOI) to be

Oachvs('41,Trl) A ((Pl E ,41) Oachvs(,4.,

.) ^

e ,4.)

A ]

^

aeh.s(((A\D.)UA.), [. I

"4 = /lx U ... U A , A consistent(A) where consistent checks the satisfiability of a set of (usually ground) sentences.

138

Grouping and Efficiency: Until now, we have not considered the possibility of structuring the agent space. Here, we briefly outline a mechanism, based upon grouping [ 16, 9], whereby simple organisational structures can be implemented, thus limiting the extent of broadcast communication. The idea underlying the notion of an agent group is that each agent may be a member of several groups, and when an agent sends a message, that message is, by default, broadcast to all the members of its group(s), but to no other agents. Thus, if such groups are constructed appropriately, then communication between groups will be more restrained than the communication within groups. (It would be natural to implement such a scheme by limiting each group to a single processor if possible.) Consider the simple top-down planning approach outlined in w Here, even if a plan never requires certain operators or initial conditions, broadcast messages will still be sent to the agents representing these irrelevant items. If we are able to partition the agents space so that agents we are certain will not be needed in the final plan are excluded from a group, then broadcast communication may be effectively limited. The smaller the group produced, the less communication is required. As an example of this, consider the set of clauses presented in w By grouping clauses 2 and 3 together into a sub-group which can receive messages, but not send them, we can ensure that messages relating to p and r are allowed through to Ag~ and Ag3, while q messages will never be allowed out of this sub-group. In this way, communication regarding q is localised and broadcast message-passing is reduced. While grouping has many practical advantages, it can obviously lead to incompleteness. It is important that any heuristics used to group agents together are shown to retain the correctness of the problem-solving system. Thus, much of our current work, particularly with respect to concurrent theorem-proving, is centred around the development of appropriate heuristics.

5

Concluding Remarks and Related Work

We have introduced a model for distributed problem-solving, based upon an agentbased approach to concurrent theorem-proving. While space precludes both a longer discussion of the elements of the model and its application to other problem-solving domains, we believe the use of this model to represent varieties of distributed planning shows the potential for this approach. In addition to providing a consistent basis for distributed problem solving, this framework allows for the development of flexible and open agent-based systems, the use of broadcast communication being vital to the latter. While the utility of a logic-based approach for proving the correctness of distributed problem solving systems is clear in the case of planning, a wide range of further applications can be re-cast in this framework. In this way, the difficulties often encountered in establishing the correctness of dynamic distributed problem solvers may be alleviated. Our future work in this area is to continue developing implementations and refinements of the approach (a prototype system already exists), to extend it to a wider range of distributed problem solving applications, and to show how such distributed systems can be formally derived from their single process counterparts. In addition, we are actively developing heuristics which can be used not only to group agents appropriately,

139

but also to distribute information amongst agents. Finally, we briefly consider related work in the section below. 5.1

Related Work

Our approach to concurrent theorem proving and distributed problem solving is somewhat similar to the blackboard model [7]. However, there are also significant differences: perhaps most importantly, our model allows for true concurrency, in that agents execute in parallel, and communicate via message-passing, rather than via globally accessible data structure. In terms of Smith's general classification of distributed problem solving, our framework is based on result sharing (as opposed to task-sharing) [18]. With respect to the underlying model of concurrent theorem-proving, while other systems share some features with our approach, the particularly close link between the operational model, communication and deduction, the possibility of dynamic creation, and the openness of the system makes it significantly different from previous systems. In the DARES distributed reasoning system [15], agents cooperate to prove theorems. As in our model, information (set of clauses) is distributed amongst agents and local deduction occurs purely within an agent, but the agent can broadcast requests for further information. In contrast to our approach, not only is the number of agents static, but the opportunity for more sophisticated structuring of the agent space within the DARES system is absent. Further, the broadcast mechanism is not pervasive - - it is only used to solicit new data when an agent stalls. While the agents within the DARES system are all of the same type, one of the suggestions of that work was to consider different 'specialist' agents within the system. This form of system has been developed using the TEAMWORK approach, a general framework for the distribution of knowledge-based search [6]. While the number of agents within TEAMWORK is more fluid than in DARES, and more sophisticated structuring is provided through the concept of 'teams', the control within the system is centralised through the use of 'supervisor' agents. Also, in contrast to our model, less reliance is placed on broadcast communication. The clause diffusion approach to concurrent theorem-proving [4] also partitions sets of clauses amongst agents. Unlike our framework, new clauses generated may be allocated to other agents. Thus, while new information generated in our approach is distributed by broadcast message-passing, this is achieved in clause diffusion via the migration of clauses. In contrast to our approach, clause diffusion is not primarily intended as a basis for the development of dynamic cooperative agent-based systems. Finally, this work obviously has links with the considerable amount of research which characterises standard planning as sequential theorem-proving, beginning with Green's development of a planning procedure based on resolution [ 12]. Both Green's approach and subsequent developments are described in [ 11] and [ 1].

140

References 1. J. Allen, J. Hendler & A. Tate (eds). Readings in Planning. California: Morgan Kaufmann, 1990. 2. H. Barringer, M. Fisher, D. Gabbay, G. Gough, & R. Owens. METATEM: An Introduction. Formal Aspects of Computing, 7(5):533-549, 1995. 3. K. Birman. The Process Group Approach to Reliable Distributed Computing. TR91-1216, Dept. of Computer Science, Comell University, 1991. 4. M. Bonacina & J. Hsiang. The Clause-Diffusion Methodology for Distributed Deduction. Fundamenta lnformaticae, 24:17%207, 1995. 5. A. H. Bond & L. Gasser (eds). Readings in Distributed Artificial Intelligence. MorganKaufmann, 1988. 6. J. Denzinger. Knowledge-Based Distributed Search using Teamwork. In Proceedings of the First International Conference on Multi-Agent Systems (ICMAS), San Francisco, USA, 1995. 7. R. Englemore & T. Morgan (eds) Blackboard Systems. Addison-Wesley, 1988. 8. M. Fisher. Concurrent METATEM - - A Language for Modeling Reactive Systems. In Parallel Architectures and Languages, Europe (PARLE), Munich, Germany, June 1993. (Published in Lecture Notes in Computer Science, volume 694, Springer-Verlag). 9. M Fisher. A Survey of Concurrent METATEM - - The Language and its Applications. In First International Conference on Temporal Logic (ICTL ), Bonn, Germany, 1994. (Published in Lecture Notes in Computer Science, vol. 827, Springer-Verlag). 10. M. Fisher. An Alternative Approach to Concurrent Theorem-Proving. In J. GeUer, H. Kitano and C. Suttner (eds), Parallel Processing for Artificial Intelligence, 3, Elsevier B.V., 1997. l 1. M. Genesereth and N. Nilsson. Logical Foundations of Artificial Intelligence. California: Morgan Kaufmann, 1987. 12. C. Green. Application of Theorem Proving to Problem Solving. In Proceedings oflnternational Joint Conference on AI, 1969. (Also in B. Webber and N. Nilsson (eds), Readings in Artificial Intelligence. Morgan Kaufmann, 1981.) 13. E Kurfel3. Parallelism in Logic. Vieweg, 1991. 14. V. Lifschitz. On the Semantics of STRIPS. In Reasoning About Actions & Plans, Morgan Kaufmann Publishers: San Mateo, CA, 1986. 15. D. Macintosh, S. Conry, & R. Meyer. Distributed Automated Reasoning: Issues in Coordination, Cooperation, and Performance. IEEE Transactions on Systems, Man and Cybernetics, 21(6):1307-1316, 1991. 16. T. Maruichi, M. lchikawa, and M. Tokoro. Modelling Autonomous Agents and their Groups. In Y. Demazeau and J. P. Muller, editors, Decentralized AI 2 - Proceedings of the

2 nd European Workshop on Modelling Autonomous Agents and Multi-Agent Worlds (MAAMAW '90). Elsevier/North Holland, 1991. 17. D. A. Plaisted and S. A. Greenbaum. A Structure-Preserving Clause Form Translation. Journal of Symbolic Computation, 2(3):293-304, September 1986. 18. R. Smith & R. Davis. Frameworks for Cooperation in Distributed Problem Solving. IEEE Transactions on Systems, Man and Cybernetics, 11(1):61-70, 1981. 19. M. Wooldridge & N. Jennings. Intelligent agents: Theory and practice. The Knowledge Engineering Review, 10(2):115-152, 1995.

C o m m i t m e n t s A m o n g A u t o n o m o u s Agents in Information-Rich Environments* Munindar P. Singh ~ Department of Computer Science North Carolina State University Raleigh, NC 27695-8206, USA

singhQncsu, edu

A b s t r a c t . Commitments are crucial to understanding and designing autonomous agents and multiagent systems. We propose a definition of commitments that applies especially well to agents in information-rich applications, such as electronic commerce and virtual enterprises. Our approach has a number of important features, including - not gratuitously translating social concepts to psychological concepts - distinguishing between satisfied and inapplicable commitments - incorporating social policies to handle the creation, satisfaction, and cancelation of commitments relating commitments to organizational structure in a multiagent system - showing how commitments are acquired by agents as a consequence of adopting a role. -

1

Introduction

Commitments are central to DAI. In this paper, "commitment" refers to social, not psychological, commitment. C o m m i t m e n t s have drawn much research attention because they are an i m p o r t a n t abstraction for characterizing, understanding, analyzing, and designing multiagent systems. C o m m i t m e n t s help coordinate and structure multiagent systems to achieve coherence in their actions. Multiagent systems are finding increasing application in heterogeneous and open information environments--such systems are called cooperative information systems (CISs) [Singh & Huhns, 1995]. CISs have increased expectations of robustness and guarantees of the atomicity, durability, and recoverability of actions. Our ongoing research program seeks to develop abstractions for building flexible CISs to the standards of robustness of traditional systems. * This is an extended and revised version of a paper presented at the ICMAS-96 Workshop on Norms, Obligations, and Conventions. I would like to thanks Rosaria Conte, Christian Lemaitre, and the anonymous reviewers for their comments. I have benefited from discussions with several people over the years, most notably Nicholas Asher, Cristiano Castelfranchi, Les Gasser, Michael Georgeff, and Michael Huhns. ** This work is supported by the NCSU College of Engineering, the National Science Foundation under grants IRI-9529179 and IRI-9624425, and IBM corporation.

142

Technical Motivation Commitments arise not only in the study of agents, but also in distributed databases. However, databases (DB) implement a procedurally hard-wired and irrevocable form of commitment. Modern DB applications, which involve heterogeneity, flexibility, and human collaboration, do not fit the traditional mold. Some of these applications have been addressed using agentbased techniques, e.g., [Wittig, 1992; Singh & Huhns, 1994]; others with advanced database techniques, e.g., [Bukhres & Ehnagarmid, 1996]; and still others by combining in organizational techniques, e.g., [Papazoglou et al., 1992]. The DB and DAI strands of research into commitments have progressed without much cross-fertilization. The DB ideas have tended to be rigid, but in a manner that facilitates robustness. The DAI ideas have been more flexible. However, with respect to information systems, they do not guarantee correctness properties comparable to the DB approaches. We submit that a conceptually well-founded synthesis can yield abstractions for effectively programming CISs. We view CISs as recursively composed loci of commitments. These commitments can be about actions, but in database settings they are typically about results that are released or "published" by different components. Whereas the traditional database approach is to release outputs only when they are definite, in the case of nonterminating computations, we cannot afford to wait till they end! In general, we must allow outputs to be released prematurely. This is also essential, for example, in cases where the given activities must cooperate, so they may exchange their partial results before they terminate. The construction of effective CISs involves the careful synthesis of three kinds of concerns: ....data integrity: correctness of data despite concurrent access and failures; - control and data flow: how triggering, i.e., control, information and data flows through the system; and organizational structure: how the various components relate to each other

in achieving coherent behavior, e.g., whether a control signal is expected and would not be ignored depends on the organizational structure of the components. Traditional nested transactions provide integrity, but restrict the other aspects. Extended Transaction Models (ETMs) also focus on integrity, but allow freer control and data flow at the cost of relaxing the integrity requirements. Database workflow approaches ignore the integrity aspects, but deliver the control and data flow required by specific applications. Workflows in groupware also provide application-specific control and data flow without regard to integrity. In contrast with the above, our approach focuses on how the different components achieve coherence in their interactions. Control and data flow serve to achieve coherence, and integrity is a consequence of it. By organizational structure, we mean not only the roles that different agents play, but also the commitments that they may enter into based on their roles. In our approach, each recursively composed CIS provides a context in which its constituent agents interact. In particular, the agents respect certain commitmerit policies and cancelation policies, which determine when they may adopt

143

or drop commitments. In some cases, these policies might help achieve a correct data state; in others, they may only guarantee that the CIS as a whole is behaving properly.

Organization. Section 2 describes traditional ways of structuring computations. Section 3 discusses our approach to commitment, shows how it handles social policies and the structure of multiagent systems, and discusses its formal aspects and implementation. Section 4 reviews the pertinent literature from three main areas.

2 Problem: Structuring Computations in Open Information Systems We introduce our running example, which involves a simplified form of electronic commerce and virtual enterprises.

~"

valves v-id a z b

idia 21 43 43

f )dia 21 21 43

hoses h-id h12 h14

dia 21 43

J Fig. 1. Traditional (Closed) Transactions

Example 1. Suppose we need to purchase two interdependent parts--a valve and two hoses, with the requirement that their diameters match (otherwise, each is useless). Consider a composite activity that attempts to purchase a shipment of valves from Valvano & Co and matching hoses from Hoosier Inc., thus accessing the databases as shown in Figure 1 (please ignore GT, LT1, and LT2, for now). Let these subactivities be called val and hos, respectively. We imagine that Valvano and Hoosier form a virtual enterprise to provide a higher level of service to their common customers, but continue to run autonomous databases. The key requirement for the purchase is that either (a) both val and hos have an effect, or (b) neither does. |

144

Traditionally, it would be up to the application program to enforce this requirement. Although traditional database transactions have been used extensively in homogeneous settings, it now well-known that they are inappropriate for heterogeneous environments. We show why next. To avoid terminological confusion, we use the t e r m "succeed" instead of the database t e r m "commit" where only the success of an individual transaction is implied. 2.1

Traditional Database

Transactions

Traditional transactions are computations that satisfy a number of useful properties, in particular the so-called ACID properties [Gray & Reuter, 1993].

-

atomicity: all or none of a transaction happens consistency: a transaction preserves the consistency of the database isolation: intermediate results of a transaction are not visible externally durability: when a transaction concludes successfully, its effects are permanent.

If the individual transactions are p r o g r a m m e d correctly, the system guarantees consistency for any arbitrary concurrent mix of transactions. Atomicity is essential to ensure that the integrity of distributed data is preserved. Consequently, the actions or subtransactions that constitute a transaction must either (a) all happen, thereby transforming the database from a consistent state to a new consistent state, or (b) each fail to happen, thereby leaving the database in its original (consistent) state.

Example 2. Continuing with Example 1, we can obtain database support for maintaining consistency as shown in Figure 1. G T is a global, closed-nested transaction corresponding to the purchase activity. It consists of local subtransactions, LT1 and LT2, corresponding to val and hos. G T preserves consistency (either both LT1 and LT2 succeed or neither does), and allows only correct purchases to be visible. | Unfortunately, the above formulation proves highly undesirable. To ensure transaction atomicity, the system must ensure that both val and hos succeed, or neither does. To ensure transaction isolation, the system must ensure that no other transaction sees the intermediate results of val or hos. Further, if a transact.ion that runs on the same databases sees the final results of one subtransaction (e.g., val), then it also sees the final results of the other subtransaction (e.g., hos). The above requirements are stronger than our informal requirement that both or neither subtransaction should have an effect. To realize the above transactional properties requires a mutual commit protocol, e.g., two-phase commit, to be executed. However, that might be impossible, since Valvano and Hoosier are independent enterprises and their databases m a y not even have visible precommit states, essential to execute a m u t u a l commit protocol. Even if the precommit states are visible, it is seldom acceptable to lock resources while communicating with a remote site. Thus traditional transactions are unacceptable in heterogeneous environments.

145

2.2

Extended Database Transactions

Extended transaction models (ETMs) take some steps toward overcoming these limitations. However, they typically address only a part of the problem, chiefly by allowing results to be released prematurely. Failure recovery is typically achieved by compensating the subtransactions that erroneously recorded success (even though other related transactions did not)--the compensations are of course domain-specific. Consider the following example, which uses a simplified version of the DOM ETM [Buchmann et al., 1992].

valves v-id a z b

idia 21 43 43

f ~dia 21 21 43

hoses h-id h12 h14

dia 21 43

J Fig. 2. Extended (Open) Transactions

Example 3. Continuing with Example 2, we now define a purchase activity as in Figure 2. Here, GT is an open-nested global transaction consisting of the val (LT1) and hos (LT2) subtransactions. GT executes LT1 and LT2 concurrently. The results of LT1 and LT2 are visible even before GT has completed. If both or neither succeed, consistency is preserved. If one succeeds and one fails, then either (a) the one that succeeded can be compensated through LT1-1 or LT2-1, e.g., by canceling the its order, or (b) the one that failed can be retried. | This assumes that (a) compensating actions are defined for some of the subtransactions, and (b) it is acceptable to allow temporary inconsistencies. Extended transaction models do not provide perspicuous means to specify and schedule activities, nor means to coordinate them. Scheduling techniques are hard-coded separately for each transaction model.

2.3

Agents

Agents can perform several functions in enterprise integration scenarios. They can capture the semantic constraints and apply them in order to execute or

146

enact workfiows in an integrity-preserving manner. In this way, agents can carry out the business processes in an enterprise. For example, although database consistency is assured even if both transactions fail, the agent might encode that some progress is essential from the purchaser's standpoint.

Example 4. In the scenario of Examples 1 and 2, a purchasing agent can be used. This agent initiates val and hos concurrently. If both succeed, the purchase succeeds. However, if one or both fail, the agent can (a) retry the failed transactions a certain number of times, (b) search for alternative sources and attempt the transactions there, or (c) negotiate with the user's agent and with database agents to enable progress. | The agents can thus realize workflows that correspond to generalized forms of extended transaction models. More importantly, however, the agents can form a CIS and interact with each other in an effective manner. For example, agents can coordinate workflows so that the unavoidable interactions among those workflows do not violate data integrity or prevent progress. Further, the requirements for each workflow can be locally captured by the resource administrators most familiar with the resources that the workflow involves. The formal specifications are kept modular and small, which facilitates their acquisition and verification.

Example 5. Consider ongoing activities to repeatedly stock inventory, ship goods to customers, receive purchase orders, and forecast the market demand. These activities must be coordinated. (a) Stocking up replenishes the inventory for shipping. The stocking up and shipping agents must agree whether to build up large inventories or break up large purchase orders. (b) A purchase order must be received before it is shipped. (c) Market forecasting can either trigger stocking up, or disable it. | But how can we ensure that the agents behave properly? Surely, we need better abstractions than having the application programmer supply hardcoded solutions. 2.4

The Problem

Thus the main problem is to structure activities in a manner that can respect the autonomy of the information resources. The database approaches are restrictive. The agent approaches are flexible, but there is need for tools and formal approaches for designing them. In particular, there is need for a notion of commitment that fexibly reflects the organizational structure of how agents interact.

3

Solution: Spheres of C o m m i t m e n t

We define commitments in a manner that satisfies the above requirements. We dub our approach spheres of commitment (SoCom). SoComs involve not only the data integrity issues, but also reflect the organizational structure associated

147

with CISs, which constrains the control and data flow as well. Each SoCom is autonomous, and has authority over some information resources, on the basis of which it can enter into commitments about those resources. 3.1

S p h e r e s o f Control

To best appreciate our approach, it is instructive to see how spheres of control (SoCs) work. SoCs, which were proposed about two decades ago [Davies, 1978], capture some of the same intuitions as the extended transaction models. The database community is seeing a resurgence of interest in SoCs as the limitations of traditional transactions are being realized [Gray & Reuter, 1993, pp. 174180]. Intuitively, SoCs attempt to contain the effects of an action as long as there might be a necessity to undo them. Ordinarily, a result is released only when it is established that it is correct (and will remain correct). However, if a result may later have to be undone, it can be released only if an SoC can be set up that encloses the activities that consume the result. When the result needs to be undone, the enclosing SoC can undo the activities that used that result.

Example 6. Continuing with Example 2, we can define an SoC that contains the val and hos subtransactions. The results of these subtransactions can be made visible only to those activities that are also within the given SoC. If the results of val and hos are inappropriately released, they can be undone, possibly by also undoing the activities that consumed those results. | SoCs generalize transactions by essentially requiring the entire execution history to be maintained. SoCs require rolling back the execution to undo the effects of erroneously committed activities, followed by rolling forward the execution to redo the necessary computations. Unfortunately, despite their generality in some respects, in a fundamental sense SoCs remain almost as restrictive as traditional transactions. This is because SoCs are also data-centric, and attempt to preserve or restore data integrity. Specifically, we believe that the problem lies in two facts. - SoCs are not active entities, and - SoCs view commitments in the traditional DB sense, which is as depending solely on the computation that commits, not on the interplay between the computation that commits, and the computations that take advantage of that commitment. 3.2

Commitments

Despite its shortcomings, we find the SoC concept useful in motivating SoComs. SoComs provide a means for organizing agents and CISs. We begin by eliminating the distinction between agents and multiagent systems. We view agents as being either individuals or groups, which are recursively composed of agents. In this sense, a CIS is an agent and is potentially composed of agents. We augment

148

our initial definition of agents to additionally require them to be loci of social commitments. Thus, each agent or CIS can be a SoCom. Agents interact by forming c o m m i t m e n t s toward one another. We use the term commiler to refer to the agent that makes a commitment, and the term commitee (not "committee") to refer to the agent who receives the commitment. C o m m i t m e n t s are formed in a context, which is given by the enclosing CIS (or, ultimately, by society at large). We refer to this as the context group. Concomitant with a c o m m i t m e n t is a specification of how it m a y be satisfactorily discharged, and a specification of how it may be canceled. We define three main kinds of social actions, which are instantiated by the following operations on commitments. create

(satisfactorily) discharge -

cancel

Based on the above intuitions, we motivate the following logical form for commitments. D e f i n i t i o n 1. A c o m m i t m e n t is an expression of the form C(x, y, p, G, d), where x is the commiter, y the commitee, G the context group, p the discharge condition, and d the cancelation condition (formally a proposition). It is convenient to define the operations of notify and release as follows. notify(x, y, q) mean that x notifies y of q, and re/ease(y, c) means t h a t y "releases" the commiter of c o m m i t m e n t c, essentially agreeing to its success. In a sense, these are low-level operations, which can be used to implement the above social actions. They are however, quite natural and common to a number of domains. Where necessary, we include the release requirements in the discharge condition. For example, it is possible to commit to "making the sky green," or "making the sky appear green to the commitee" (these are different commitments, with different chances of satisfiability). We now discuss some possible cancelation conditions, which relate to different situations. Let the given c o m m i t m e n t be c = C(x, y, p, G, d). (Explicitly naming the c o m m i t m e n t itself enables setting up mutual commitments.) P]. d = false: the c o m m i t m e n t is irrevocable. P2. d = notify(x, y, q): the commiter is only obliged to notify the commitee, where q means that the c o m m i t m e n t c is being canceled. P3. d = true: the c o m m i t m e n t can be given up at will, and is effectively not a c o m m i t m e n t at all. P4. d -- re/ease(y, c): the commitee must explicitly release the commiter. Ph. d = release(G, c): the context group must explicitly release the commiter.

Example 7. Consider the situation of Example 2 after val has successfully completed its internal processing, but not yet officially published its results. This can be modeled as cl -- C(val, hos, succeed(val), G, cannot_succeed(hos)). Here

149

G corresponds to the global transaction. The above commitment means that if val can succeed, it will unless hos cannot succeed. Additional commitments are need to capture the entire specification, e.g., to ensure that val does not succeed unless hos succeeds. |

Fig. 3. Nested Spheres of Commitment

Example 8. Continuing with Example 2, we define two SoComs--shown in Figure 3--with authority over the Valvano and Hoosier databases, respectively. These SoComs execute the corresponding subtransactions. There is also a SoCorn corresponding to the Valvano-cum-I-Ioosier virtual enterprise (VE). As in Example 4, a customer agent carries out the desired workflow. This agent might itself be a SoCom with authority over purchases in its enterprise. A possible set of commitments could be as follows. - The Valvano and Hoosier SoComs inform other agents as to how many units of a valve or hose they have in stock. If stock is available, they will "lay-away" up to a certain number of units for a (potential) customer; if stock is not available, they will notify the customer. - However, if the stock falls low, the SoComs can ask a customer to decide or pay a nonrefundable deposit. -

150

- The customer commits to releasing chase. - The customer can request to apply selling SoCom's discretion. - The customer can request a refund of purchase price is refunded if a available.

a lay-away if he decides against the purthe deposit for another purchase, at the from the VE SoCom. The entire deposit matching item (hose or valve) was not

In this setup, val or hos in general cannot be undone--customers can't expect full refunds after the purchase. However, if val and hos are being performed as a package, i.e., in the Valvano-cum-Hoosier VE, the VE SoCom ensures that customers will get refunds if one of the subtransactions fails. Other customers who were told that stock was not available will be notified, and given an opportunity to retry their purchase. Lastly, negotiation and exceptions are allowed, although after commitments have been made, the decision might reside with one of the participants. I 3.3

Social Policies

Social policies are policies that govern the social actions--they characterize when the associated action occurs. It is helpful to define the order of a commitment as follows. D e f i n i t i o n 2 . Consider a commitment c = C ( x , y , p , G , d ) . c is 0-order iff p makes no reference to any commitments, c is (i + 1)-order iff the highest order commitment referred to in p is/-order. Social policies are formally represented as conditional expressions. Policies f o r / - o r d e r commitments are (i + 1)-order commitments. Even for policies, our fundamental correctness condition remains: if a commitment is created, then it must satisfactorily discharged, unless it is canceled in time. A variety of policies can be defined, with applicability in different settings. Policies also have a computational significance, which is that they can lead to commitments being created, discharged, or canceled without reference to the context group. It is the locality of policies that makes them useful in practice. Consider a simple example. Example 9. Continuing with Example 8, consider a customer who makes an Ecash deposit for some valves, but later decides not to get them. He might avoid losing his deposit by finding another purchaser for those valves. The selling SoCom would have to accept an alternative purchaser if the applicable social policies allow that, unless they were explicitly overridden by the contract. There is no need to invoke the context group, i.e., complain to the VE SoCom, or to file a lawsuit. I In the above the actions are performed by the constituent CISs. Sometimes, however, it is useful to perform actions at a higher level CIS. Such actions might be necessary when the actions of the member agents need to be atomically performed or undone.

151

Example 10. Continuing with Example 8, suppose an order for matching valves and hoses is successfully placed. It turns out later that the valve manufacturer discontinued the model that was ordered, but recommends a substitute. The substitute valve takes a different hose diameter than the original choice. Suppose the VE SoCom knows the relevant constraint, and is authorized to update the order. It would be better to undo and rerun both val and hos before notifying the customer, than to notify the customer about each subtransaction individually. This strategy assumes that the VE SoCom is responsible for performing actions to correct orders. | 3.4

Social v e r s u s Psychological Primitives

Some previous approaches, e.g., [Levesque et al., 1990; Grosz & Sidner, 1990], attempt to reduce social constructs to psychological constructs. They do not have an explicit construct for commitments, but postulate mutual beliefs among the committing agents. However, Mutual beliefs require the agents to hold beliefs about each other to unbounded levels of nesting, which can be tricky [Singh, 1996a]. Also, mutual beliefs cannot be implemented except through additional simplifying assumptions, which is why the direct approach of using social constructs is more appealing. In fact, it is known that in settings with asynchronous, unreliable or unboundedly delayable communication, mutual beliefs can be obtained only if they are there from the start--i.e., the mutual beliefs are the invariants of the system [Chandy & Misra, 1986]. We conjecture that named groups and named commitments, which are reminiscent of contract numbers in business dealings, provide the necessary connections among the agents. This is a reasonable conjecture, because commitments and the groups they exist in can provide the requisite context that is copresent with all of the agents. Membership in a group can require mutual commitments, which can refer to each other (by using each other's names). Thus, the effect that traditional theories attempt to achieve by using mutual beliefs can be achieved without mutual beliefs, and without reducing social primitives to psychological primitives. We believe that with further technical development, this will prove to be an important point in favor of social commitments. 3.5

Implementation

The above view of commitments can thus lead to CISs that behave flexibly. In order to make the construction of such CISs equally flexible, we are developing a generic facility for commitment specification and management. This facility would allow the specification of CISs along with the social policies that apply within them. We provide a generic set of Java classes through which abstract CISs can be specified. These specifications include the different roles in a given CIS, and the capabilities and resources required to instantiate each role. These specifications also include the social policies--expressed in terms of roles--that apply within the abstract CIS. Essentially, these are the commitments that the

152

role comes with. For example, the seller role presupposes that the seller will respond to requests for price quotes, and honor its quotes. The abstract CISs are instantiated with concrete agents filling each role. The concrete agents may be individuals or groups. Recalling [Gasser, 1991], a concrete agent may fill in more than one role in an abstract CIS, and participate in more than one abstract CIS concurrently. The act of joining a CIS corresponds to creating commitments. The commitments associated with a role are schematic. Upon instantiation of the roles, these are instantiated into commitments by and toward concrete agents. Thus agents can thus autonomously enter into SoComs. Agents must make sure they have the capabilities and resources required to take on any additional role, and its concomitant commitments. Some of the inherited commitments might require overriding some prior commitments. For example, the Valvano agent must relax its refund policy when joining the above-mentioned VE. Once the concrete CISs have been instantiated, any of the member agents can initiate an activity, which can trigger additional activities. The facility provides primitives through which agents can instantiate a CIS, create commitments within the context of a CIS, and satisfy or cancel commitments. The facility takes care of the bookkeeping required for these operations, and to ensure that the correctness condition is met. The underlying means of execution is based on a temporal logic approach, which extends the results of [Singh, 1996b], to provide primitives for coordinating heterogeneous activities.

4

Comparisons

with the Literature

D A I Approaches. Gasser describes some of the sociological issues underlying multiagent systems [Gasser, 1991]. His notion of the multiple simultaneous roles played by social agents inspired part of our discussion above. Castelfranehi studies concepts similar to those here [Castelfranchi, 1993]. Our context groups generalize his notion of a witness. Castelfranehi distinguishes a notion of collective commitment, which is subsumed by our concept of commitment (through the orthogonal representation of the structure of multiagent systems). Tnomela develops an interesting theory of joint action and intention that bears similarities to collective commitments [Tuomela, 1991]. [Sichman et al., 1994] develop a theory and interpreter for agents who can perform social reasoning. Their agents represent knowledge about one another to determine their relative autonomy or dependence for various goals. Dependence leads to joint plans for achieving the intended goals. This theory does not talk about commitments per se, so it is complementary to our approach. We also believe that our approach with its emphasis on structure and context can be married with that of [Sichman et al., 1994] to lead to more sophisticated forms of social reasoning. The approach of [Levesque et al., 1990] requires the agents to have a mutual belief about their goals. Further, it hardwires a specific approach to canceling commitments (for joint intentions)--the participating agents must achieve a mutual belief that the given commitment has been canceled. The approach of

153

[Jennings, 1993] is closer in spirit to the present approach. Jennings postulates conventions as ways in which to reason about commitments. Thus, he can generalize on [Levesque et al., 1990]. However, for teams, he requires a "minimum" convention, which recalls the approach of [Levesque et al., 1990]. Jennings also requires a mental state as concomitant with a joint commitment. While we share many of the intuitions and motivations of [Jennings, 1993] (including applications involving heterogeneous information systems), we attain greater generality through the explicit use of the structure of multiagent systems. The agents always enter into commitments in the context of their multiagent system, and sometimes to that system. This has the pleasant effect that social concepts are not made dependent on psychological concepts. The multiagent system serves as the default repository for the cancelation and commitment policies, although these can, in several useful cases, be assigned to the member agents. We believe the relationship of our approach to open-nested transaction models and workflows will lead to superior multiagent systems for information applications. Distributed assumption-based [Mason & Johnson, 1989] or justification-based [Huhns & Bridgeland, 1991] truth maintenance systems (DTMSs) are also germane. These systems help a group of agents revise their beliefs as a consequence of messages received. On the one hand, DTMSs can be given a knowledge-level characterization in terms of commitments; on the other hand, they can be used to implement some of the reasoning required in maintaining commitments.

DB and Groupware Approaches. A number of extended transaction models have been proposed, e.g., [Bukhres & Elmagarmid, 1996]. The extended transaction models allow partial results to be released, and then attempt to restore consistency through actions to compensate for the effects of erroneously completed actions. Some workflow scheduling approaches exist that provide functionality to capture control flow among tasks. The database approaches don't provide much support for the organizational aspects. For example, they ignore social commitments altogether. Some of the groupware approaches, which study organizational structure, do not consider quite as rich a form of commitments as here. For example, information control nets are primarily geared toward control and data flow aspects [Nutt, 1993]. The notion of commitments finds applicability in some groupware tools. For example, [Medina-Mora & Cartron, 1996] shows how the flow of work in an organization is expressed through commitments in the ActionWorkflow tool. This tool comes with a fixed set of specifications from which the developer can choose. Although the participants can decide whether a given task was successfully performed, there is no notion of failure recovery, of commitments being canceled, or of commitment and cancelation policies. Still, we believe, this is an interesting system that shows how much can be achieved through the careful use of commitments.

154

5

Conclusions and Future Work

We sought to present the unifying principles behind commitment for single-agent and multiagent systems. Our approach marries insights from DB and DAI, to yield a framework for flexible, yet robust, cooperative information systems. Our approach makes the following contributions. It - does not require translating commitments to psychological concepts, such as beliefs - distinguishes between satisfied and inapplicable commitments - incorporates policies to handle the creation, satisfaction, and cancelation of commitments - relates commitments to organizational structure in a multiagent system - shows how commitments are acquired by agents as a consequence of membership in a group. A practical challenge is determining classes of commitments and policies that are more relaxed than the traditional approaches, yet can be efficiently implemented. Two other technical challenges are introducing temporal aspects into the language, and relating the development of commitments to decision theoretic analyses of rational behavior.

References [Buchmann et al., 1992] Buchmann, Alejandro; ()zsu, M. Tamer; Hornick, Mark; Georgakopoulos, Dimitrios; and Manola, Frank A.; 1992. A transaction model for active distributed object systems. In [Elmagarmid, 1992]. Chapter 5, 123-158. [Bukhres & Elmagarmid, 1996] Bukhres, Omran A. and Elmagarmid, Ahmed K., editors. Object-Oriented Multidatabase Systems: A Solution for Advanced Applications. Prentice Hall. [Castelfranchi, 1993] Castelfranchi, Cristiano; 1993. Commitments: From individual intentions to groups and organizations. In Proceedings of the AAAI-93 Workshop on AI and Theories of Groups and Organizations: Conceptual and Empirical Research. [Chandy & Misra, 1986] Davies, K. M. and Jayadev Misra; 1986. How Processes Learn. Distributed Computing 1:40-52. [Davies, 1978] Davies, Charles T. Jr.; 1978. Data processing spheres of control. IBM Systems Journal 17(2):179-198. [Elmagarmid, 1992] Elmagarmid, Ahmed K., editor. Database Transaction Models for Advanced Applications. Morgan Kaufmann. [Gasser, 1991] Gasser, Les; 1991. Social conceptions of knowledge and action: DAI foundations and open systems semantics. Artificial Intelligence 47:107-138. [Gray & Reuter, 1993] Gray, Jim and Reuter, Andrea.s; 1993. Transaction Processing: Concepts and Techniques. Morgan Kaufmann. [Grosz & Sidner, 1990] Grosz, Barbara and Sidner, Candace; 1990. Plans for discourse. In Cohen, P.; Morgan, J4 and Pollack, M., editors, SDF Benchmark Series: Intentions in Communication. MIT Press, Cambridge, MA. [Huhns & Bridgeland, 1991] Huhns, Michael N. and Bridgeland, David M.; 1991. Multiagent truth maintenance. IEEE Transactions on Systems, Man, and Cybernetics 21(6):1437-1445.

155

[Jennings, 1993] Jennings, N. R.; 1993. Commitments and conventions: The foundation of coordination in multi-agent systems. The Knowledge Engineering Review

2(3):223-250. [Levesque et al., 1990] Levesque, H. J.; Cohen, P. R.; and Nunes, J. T.; 1990. On acting together. In Proceedings of the National Conference on Artificial Intelligence. [Mason & Johnson, 1989] Mason, Cindy L. and Johnson, Rowland R.; 1989. DATMS: A Framework for Distributed Assumption-Based Reasoning. In Gasser, L. and Huhns, M. N., editors, Distributed Artificial Intelligence, Volume II. Pitman/Morgan Kaufmann, London. 293-318. [Medina-Morn & Cartron, 1996] Medina-Morn, Radl and Cartron, Kelly W.; 1996. ActionWorkflowR in use: Clark County department of business license. In Proceedings of the 12th International Conference on Data Engineering (ICDE). 288-294. [Nutt, 1993] Nutt, Gary J.; 1993. Using workflow in contemporary IS applications. Technical Report CU-CS-663-93, University of Colorado. [Papazoglou et al., 1992] Papazoglou, Mike P.; Laufmann, Steven C.; and Sellis, Timothy K.; 1992. An organizational framework for cooperating intelligent information systems. International Journal on Intelligent and Cooperative Information Systems 1(1):169-202. [Sichman et al., 1994] Sichman, Jaime Sims Conte, Rosaria; Demazeau, Yves; and Castelfranchi, Cristiano; 1994. A social reasoning mechanism based on dependence networks. In Proceedings of the 11th European Conference on Artificial Intelligence. [Singh & Huhns, 1994] Singh, Munindar P. and Huhns, Michael N.; 1994. Automating workflows for service provisioning: Integrating AI and database technologies. IEEE Expertg(5). Special issue on The Best of CAIA '94 with selected papers from Proceedings of the 10th IEEE Conference on Artificial Intelligence for Applications, March 1994. [Singh & Huhns, 1995] Singh, Munindar P. and Huhns, Michael N.; 1995. Cooperative information systems. Tutorials notes from conferences including the International Joint Conference on Artificial Intelligence (IJCAI), Montreal, 1995; the IEEE International Conference on Data Engineering (ICDE), New Orleans, 1996; and the European Conference on Artificial Intelligence (ECAI), Budapest, 1996. [Singh, 1996a] Singh, Munindar P.; 1996a. A conceptual analysis of commitments in multiagent systems. Technical Report TR-96-09, Department of Computer Science, North Carolina State University, Raleigh, NC. h t t p ://w~w. csc. ncsu. e d u / f a c u l t y / mpsingh/papers/mas/commit, ps. [Singh, 1996b] Singh, Munindar P.; 1996b. Synthesizing distributed constrained events from transactional workflow specifications. In Proceedings of the 12th International Conference on Data Engineering (ICDE). [Tuomela, 1991] Tuomela, Ralmo; 1991. We will do it: An analysis of group-intentions. Philosophy and Phenomenological Research LI(2):249-277. [Wittig, 1992] Wittig, Thies, editor. ARCHON: An Architecture for Multi-agent Systems. Ellis Horwood Limited, West Sussex, UK.

Making a Case for Multi-Agent Systems Fredrik Ygge ~

Hans Akkermans

qEnerSearch AB and University of Karlskrona/Ronneby Department of Computer Science (IDE) 3 7 2 25 R o n n e b y , S w e d e n F r e d r i k . Y g g e @ ide.hk-r, se, h t t p : / / w w w . rby.hk-r, s e / - f r e d r i k y w

and University of Twente

Department of Information Systems (INF/IS) P.O. B o x 2 1 7 , N L - 7 5 0 0 A E E n s c h e d e , T h e N e t h e r l a n d s akkermans @ecn.nl, J.M.Akkermans @cs.utwente.nl

Abstract Multi-Agent Systems (MAS) promise to offer solutions to problems where established, older paradigms fall short. To be able to keep promises, however, indepth studies of advantages and weaknesses of MAS solutions versus conventional ones in practical applications are needed. In this paper we offer one such study. Climate control in large buildings is one application area where MAS, and marketoriented programming in particular, have been reported to be very successful. We have therefore constructed and implemented a variety of market designs for this problem, as well as different standard control engineering solutions. This paper gives a detailed analysis and comparison, so as to learn about differences between standard versus MAS approaches, and yielding new insights about benefits and limitations of computational markets.

1

Introduction

When new paradigms arise on the scientific horizon, they must prove their value in comparison and competition with existing, more established ones. The multi-agent (MAS) paradigm is no exception. In a recent book on software agents, edited by Jeffrey Bradshaw [Bradshaw, 1997], Norman observes that perhaps "the most relevant predecessors to today's intelligent agents are servomechanisms and other control devices". And indeed, a number of applications for which MAS have recently claimed success, are close to the realm of what is traditionally called control engineering. One clear example is the climate control of large buildings with many office rooms. Here, Huberman et al.

157

[Huberman and Clearwater, 1995] have constructed a working MAS solution based on a market approach, which has been reported to outperform existing conventional control. A key question is: in what respect and to what extent are multi-agent solutions better than their alternatives? We believe that the above-mentioned application provides a nice opportunity to study this question in more detail. It is practically very relevant, it lends itself to alternative solutions, and it is quite prototypical for a wide range of industrial applications in resource allocation (including the energy management applications we are working on [Ygge and Akkermans, 1996], the file allocation problem of Kurose and Simha [Kurose and Simha, 1989], and the flow problems investigated by Wellman [Wellman, 1993]). The contribution of this paper is a detailed analysis of a published MAS approach to building climate control and a comparison between this approach and traditional approaches. We also introduce a novel approach to this problem based on a general equilibrium market. From the analysis of these approaches we draw conclusions not only about the suitability of these approaches, but of many other MAS approaches as well. The structure of the paper is as follows. Section 2 introduces the application: it describes the office environment and gives the physical model for cooling power and the various temperature and outside weather influences. First, we give a standard control engineering solution, based on local and independent integral controllers (Section 3). Next, we consider the market-based approach as put forward by Huberman et al. [Huberman and Clearwater, 1995] (Section 4), and we discuss a number of variations on this market design (Section 5), providing a kind of factor analysis for its success. Section 6 then shows the results of an improved control engineering scheme that exploits global data. Finally, we propose a market design of our own, based on local data only, and make a comparison across the different approaches (Section 7). Section 8 puts our results into perspective and summarizes the conclusions.

2

The Office Environment

In this section, we present the mathematical-physical model of the office environment. For those readers who find the mathematics forbidding, we first give a conceptual summary which makes it possible to skip the equations. The offices are attached to a pipe in which the resource (cold air) is transported as in Figure 1. The characteristics of this system are similar to the characteristics of a district heating system, but with offices instead of households. We assume that there are 100 offices in total, and that they are equally distributed towards East, South, West and North. The thermodynamics of the office environment is actually quite simple. Every office is seen as a storage place for heat, but heat may dissipate to its environment. In the model, the thermodynamic behaviour of an office is equivalent to a simple electrical RC-circuit. Here, voltage is analogous to temperature, and electrical current is analogous to heat

158

Figure 1: Offices and air transportation.

flow. C and R then respectively denote heat capacitance and heat losses to the environment. A good general reference on thermodynamic models as we use here is [Incropera and Witt, 1990]. The heat equations are continuous in time, but are discretized according to standard procedures used in control engineering, described e.g., in [Ogata, 1990]. The AI aspects of thermodynamic model building with component libraries are discussed extensively in [Borst et al., 1997].

2.1

Basic Physical Properties

The resource treated is cooling power. Each office can only make use of a fraction, ~/, of the available resource at that office, Paio, i.e.

Pcio ~_ 71" Paio,

(1)

where Pcio is the consumed power. The available resource at one office is equal to the available resource at the previous office minus the consumed resource at the previous office. Throughout the paper we use an 71of 0.5. The index i denotes the time interval under observation and the index o denotes the office under observation. This naming convention is used throughout the entire paper. In addition, the index k is also sometimes used for time intervals. We treat everything in discrete time with a time interval of one minute. For all offices

159

the temperature, Tio, is described by i

Tio = To,o + E ( P h k o

(2)

-- P c k o ) / C o ,

k=l

where Phko is the heating power and Co is the thermal capacitance. The heating power is described by Phio = (T,,io - T i o ) / R o , (3) where Ro is the thermal resistance and Trio is a virtual outdoor temperature detailed below. LFrom Eq. (2) and Eq. (3) we obtain Tio -

2.2

1

1 1 + noCo

(Ti-l,o +

Tv,~ _ Pc~o ),

Ro

Co

i > O.

(4)

Weather Model

All external weather influences on the office environment are modelled by a virtual temperature, representing outdoor temperature, sun radiation etc. We assume that there is sunshine every day and that the outdoor temperature, T ~ varies from 22 to 35~ according to T~ = 22 + 13. e -((i's-a) rood 24-12)2/20 (5) where s is the length of each time interval expressed in hours, i.e. here s = 1/60. The virtual temperature, Trio, is described by Trio = T~ i~

T ~o + Tdio, --1- ~

(6)

where Ta is a random disturbance, thought to represent small fluctuations caused by e.g. the wind. Td is Gaussian distributed with an average value of 0 and a standard deviation of 1. Tr is a sun radiation component. For the offices located at the East side Tr is described by T rEast = 8 " e - ( ( i's+4 ) rood 24-12)2/5 (7) i

and correspondingly for the South and the West offices

24-12)2/5

(8)

TrWest = 8" e -((i's-4) rood 24-12)2/5

(9)

TrSi~

-- 15 9e -(i's

mod

and

The various temperatures are plotted in Figure 2.

160

35

~40:

o

0 :: . . . . . . . . . . . . . .

5 J

i,

~§

:: :

~ ~o7

I I I I

Time of dlly, h

Time of day, h

Time of day, h

Figure 2: The leftmost plot shows the outdoor temperature, T ~

The middle plot shows the sun radiation components, T~, (with peaks at time 8, 12, and 16 hours for offices located at the East, South, and West side respectively). Finally, the outdoor temperature plus the sun radiation components are plotted to the right.

2.3

Office Temperatures Without Control

In Figure 3, the temperature for a South oriented office is plotted with different thermal

resistances, Ro, a n d t h e r m a l capacitances, Co. For simplicity w e a s s u m e all Ro to be equal and all Co to be equal. From this figure we note two things: first, the higher RoCo the bigger the lag in temperature changes, and second, the higher RoCo the smaller the

fluctuations in temperature. For the experiments in this paper we chose Ro = 10 and Co -- 10. An Ro or a Co equal to zero implies that Tio = Trio, as can be seen by letting Ro or Co approach zero in Eq. (4).

4s T

i

40

~ I ! i

~

o 351

R=0 or C=0

i - - B - - R= 10, C= 10,

~

- - X - - R=I 0 and C= 20 or R=20 and i C=10 ! - - R=20 a n d C = 2 0

25

[

2O #

o

04

~

{o

O3

0

O~

"~

~

aO

0

Time of day, h

Figure 3: The indoor temperature for an uncontrolled office is plotted for different values of the thermal resistance and the thermal capacitance. Small values of the thermal resistance and capacitance give higher peaks, while higher values give smoother curves.

161

2.4

Simulations

All solutions discussed in this paper have been implemented in and simulated by the authors using C++ on an IBM compatible PC running Windows95. Furthermore, for guaranteeing that a fair comparison on the same footing was carried out, all simulations have been independently recreated and verified from the paper, as part of a master's thesis project, using Python on an IBM compatible PC running Linux.

3 3.1

CONTROL-A: Conventional Independent Controllers Integral Control for the Offices

The application of regulating the office temperature has a long tradition in control theory. The most widely known controllers are different variants of the PID controller. The letters PID denote that the control signal is proportional to the error (i.e. the difference the between the setpoint and the actual value) - - the P; proportional to the integral of the error - - the I; and proportional to the derivative of the e r r o r - - the D. Here, we will use a variant of the integrating controller, i.e., an I-controller, of the form

Fio = Fi-l,o +/~(Tio - T~etP),

(10)

where F is the output signal from the controller and ~ is a gain parameter. For the simulations it is assumed that Fio will not be below 0 and that it will not exceed 3. The control signal Fio is sent to the actuator and the actual Peio is obtained from

Pcio = ~ Fio, ( ~" Paio,

Fio < 71. Paio Fio > ~" Paio

(ll)

Plots of the office temperatures with different gains are shown in Figure 4. The gain of the controller is not critical for this application. Too high a gain will result in the controller overreacting when the temperature exceeds the setpoint, after which it will be under the setpoint for quite some time. This leads to a larger error than if smaller adjustments are made. Also, the amplitude of the control signal then gets unnecessarily high, but the system does not get dangerously unstable. We note that the maximum deviation here is •176 Thus, controllers using any of the three gains perform very well. Apart from the plots shown here, also a step response has been analyzed. From a brief analysis we chose a gain of 10.

The Performance Measure We use the same evaluation of the building control as Huberman et al. [Huberman and Clearwater, 1995], namely the standard deviation of the deviation from the setpoint, I

N

1 S t d D e v ( T - T ~etp) = ~1 -~ ~ [ ( T i o - T seep) - ((Ti) - 1, seller !. tio < 1, buyer

(13)

Then the total trade volume, V, is calculated from N

gi = ~ l l - t i o l , o=1

where N is the number of offices.

(14)

164

Then every agent calculates its request volume, v, according to

I1 - tiol Vio = ( x - -

(15)

E

When an agent buys or sells its v the actual movement of a valve, called V A V , is computed from A V A K i o = f ( f l o w w , Vio, VA$~o), (16) where f is an empirically determined function which is not explicitly given in the papers [Huberman and Clearwater, 1995, Clearwater and Huberman, 1994]. From this the actual V A V position for each interval is updated according to

VAVi+1,o = VAl/Tio + AVAV~o. Bids

(17)

The bids are based on the marginal utility I of the form described by

U(tio/T, setp, mio) = [U(O, mio)] ('-t'~

l1

= [U(O, mio)]'

(Ti)

~ ' ,

~,

(18)

with

U(O, mio) = u3 - (ua - Ul)e -'ym'~

(19)

? = In [U3 -- Ul ] ,

(20)

and Lu3 -- u2 J

where ul = 20, u2 = 200, and u3 = 2000, and m is the amount of money that an agent has, as given by mio = 100(2 - VAVio). (21) By observing Eq. (19) and Eq. (20), we note that the equations can be simplified to U(O, mio) =

?23 -

( u 3 - 1 1 , 1 ) e-?mi~ = ?/,3 -

u3 - ( u a

-ul)

('//,3 - ~ l ) ( e ~ r )

( ~ a - u ~ -m`~ . \ ua-u2 ]

-mi~ :

(22)

Since the V A V varies between 0 and 1, the amount of money an agent has varies between 100 and 200, and thus U(0, m) varies between 1999.85632 and 1999.99999. Hence, the notion of gold does not effect the marginal utility in practice, as will be verified by the simulations in Section 5. The bids are calculated from multiplying the marginal utility with the previous price,

price, according to Bi+l,o = U w ( t i o / T setp, mio) x pricei.

(23)

IWhat Huberman et al. call utility in their work [Hubermanand Clearwater, 1995, Clearwater and Huberman, 1994] is generally called marginal utility in micro-economictheory.

165

Auction All bids are sent to an auctioneer which calculates a market price where supply meets demand. All agents which requested to buy and which bids are above or equal to the market price are assigned the corresponding amount, and, similarly, all agents which requested to sell and which bids are below or equal to the market price are assigned the corresponding of amount of resource. Assumptions and Simplifications Since f in Eq. (16) above is not explicitly given and since the relation between the V A V position and Pc is specific to the office environment, we need to make an assumption about how the bid volumes relate to Pc. We take the simplest choice and let Eq. (16) and Eq. (17) be replaced by

Fi+l,o = Fio + rio,

(24)

where plus or minus depends on whether it was an accepted buy or sell bid. Pcio is obtained from Eq. (11). This simplification is not crucial to our central line of reasoning. As pointed out by Clearwater & Huberman [Clearwater and Huberman, 1994], the purpose of the auction is only to reallocate between the agents and not to affect the average temperature. This means that even if the valves are not opened very much, there is plenty of resource in the system, and if the offices are all above the setpoint, no valve can open further without another one closing. In order not to complicate the model further, the simulation is only performed in a time interval where there is a total lack of resource, with the total available resource assigned to the system. Thus, we do not implement a mechanism that inserts and deletes the total resource to the system. We choose to simulate between 3 p.m. and 7 p.m. We assume the amount of money to be given by

mio = 100(2

Foma= - Fio, -~o--~-~-

).

(25)

Thus, the amount of money will vary from 100 to 200, as in the original work. Eq. (23) turns out to produce major problems in simulations. Above we have seen that U(0, m) ~ 2000. Equation (18) shows that U(tio/T setp, mio) is minimized for minimized Tio and maximized new = pQnll Then a random sequence of agents from R12 is run changing Pnew to Pnewnew = PnewQ n'~ = pQn,~QR,~ hence OR1 = Qn,,QR,2 Note that if the two matrices are 3 by 3 block diagonal, then so is their product. T,(~) 5. R1 is of the form R1 = /-~11 What should QR~{") be? Remember that P*(~) 1~11 denotes the REP in which the string consisting of n random sequences from L(Rll) has probability (1 - ~)c~n of being generated, thus we get.: O P R O P E R T Y ;

relations at_time: current_time: currently : effect : event_after : event_to_happen : next : next_time_point : precedes :

P R O P E R T Y * SIGN * TIME ; TIME ; P R O P E R T Y * SIGN ; EVENT * P R O P E R T Y * SIGN ; EVENT * TIME ; EVENT ; P R O P E R T Y * SIGN ; TIME ; TIME * TIME ;

end signature signature specific_matedal_world_sig objects car_to_appear: car, ice_cream, supermarket:

EVENT ; OBJECT ;

218

pl, p2, p3: car present:

POSITION ; PROPERTY ;

functions close_by for: goto: has_hit: next_on path: sells:

OBJECT * AGENT -> PROPERTY ; POSITION -> ACTION ; OBJECT * OBJECT -> PROPERTY ; POSITION * POSITION * POSITION -> PROPERTY ; OBJECT * OBJECT -> PROPERTY ;

relations cu rrent_action_by: current_observation_by: current_observation_result_of:

ACTION * AGENT ; PROPERTY * AGENT ; PROPERTY * SIGN * OBJECT ;

end signature signature specific_material brain_sig sorts AGENT_ATOM, BRAIN LOCATION, INFORMATION OBJECT, LTM LOCATION, STM_LOCATION;

sub-sorts AGENTATOM : BRAIN_LOCATION : LTM_LOCATION : STM_LOCATION : INFORMATION_OBJECT:

AGENT_PROPERTY ; POSITION ; BRAIN_LOCATION ; BRAIN_LOCATION ; OBJECT ;

functions contents_of stmto_ltm: has_amnesia: information_object: Itm_location: recovered: recovering: stm location: to_be_stored:

INFORMATION_OBJECT * STM_LOCATION -> EVENT ; AGENT -> AGENT_PROPERTY ; AGENT_ATOM * SIGN -> INFORMATION OBJECT ; AGENT_ATOM * TIME -> LTM_LOCATION AGENT -> AGENT_PROPERTY ; AGENT -> EVENT ; AGENT_ATOM * TIME -> STM_LOCATION AGENT ATOM * SIGN ;

end signature

The (temporal) knowledge simulating the processes within the material world is specified as follows: /* domain dependent knowledge */ at time(position(supermarket, p3), pos, T: TIME) ; attime(sells(supermarket, ice_cream),pes,T: TIME) ; attime(position(agent, pl), pos, tl) ; a t t i m e ( c a r p r e s e n t , neg, t l ) ; effect(car_toappear, position(car, pl), neg) ; effect(car_toappear, position(car, p2), pos) ; effect(goto(P : POSITION), position(agent, P : POSITION), pes) ; effect(recovering(X : OBJECT), has_amnesia(X : OBJECT), neg) ; eventafter(car_toappear, tl) ; precedes(tO, t l ) ; precedes(t1, t2) ; precedes(t2, t3) ; if

at_time(position(A : AGENT, pl), pos, T: TIME) and at_time(position(O:OBJECT,p3),pos,T:TIME) then at time(close by_for(O : OBJECT, A : AGENT), pos ,T : TIME) ; if

current observation_by(P : PROPERTY, A : AGENT)

and current_time(T : TIME) and at_time(P : PROPERTY, S : SIGN, T : TIME) then current_observation_result of(P : PROPERTY, S : SIGN, A : AGENT) ;

219

/* domain independent knowledge */ if

at_time(position(X : OBJECT, P : POSITION), pos, T1 : TIME) end at_time(position(Y : OBJECT, Q : POSITION), pos, T1 : TIME) and not equal(X : OBJECT, Y : OBJECT) and net equal(P : POSITION, Q : POSITION) and precedes(T1 : TIME, T2 : TIME) and at_time(position(X : OBJECT, R : POSITION), pos, T2 : TIME) and at_time(position(Y : OBJECT, R : POSITION), pos, T2 : TIME) then at_time(has_hit(X : OBJECT, Y : OBJECT), pos, T2 : TIME) ; if at_time(has_hit(X : OBJECT, agent), pos, T : TIME) then at_time(has_amnesia(agent), pos, T : TIME) ; if

current action by(A : ACTION, X : AGENT ) and effect(A : ACTION, P : PROPERTY, S : SIGN) then next(P : PROPERTY, S : SIGN) ; if

event_to_happen(E : EVENT) and effect(E : EVENT, P : PROPERTY, S : SIGN) then next(P : PROPERTY, S : SIGN) ; if currently(has_amnesia(X : OBJECT), pos) then event_to_happen(recovering(X : OBJECT)) ; if

current_time(T2 : TIME) end precedes(T1 : TIME, T2 : TIME) and at_time(position(I : INFORMATION_OBJECT, B: STM_LOCATION), pos, T1 : TIME) then eventto_happen(contents of stm to_ltm(I : INFORMATION_OBJECT, B: STM_LOCATION)) ; if

current_time(T1 : TIME) and precedes(T1 : TIME, T2 : TIME) then effect(contents_of_stm_to_ltm(I : INFORMATION_OBJECT, B: STM_LOCATION), position(I : INFORMATION_OBJECT, Itm_location( : INFORMATION_OBJECT, T2 : TIME)), pos ) ; if

currently(has_amnesia(X : AGENT), neg) and current_time(T2 : TIME) and precedes(T1 : TIME, T2 : TIME) and not event_to_happen(contents_of_stm_to_ltm(I : INFORMATION_OBJECT, B: STM_LOCATION)) and at_time(position(I : INFORMATION_OBJECT, B: STM_LOCATION), pos, T1 : TIME) then at_time(position(I : INFORMATION OBJECT, B: STM_LOCATION), pos, T2 : TIME) ; if

current_time(T2 : TIME) and precedes(T1 : TIME, T2 : TIME) and at_time(I : INFORMATION_OBJECT, B: LTM LOCATION), pos, T1 : TIME) then at_time(I : INFORMATION_OBJECT, B: LTM_LOCATION), pos, T2 : TIME) ; if

current_time(T:TIME) and at_time(P : PROPERTY, S : SIGN, T : TIME) then currenUy(P : PROPERTY, S : SIGN) ; if

event_after(E : EVENT, T : TIME ) and current_time(T : TIME) then event_to_happen(E : EVENT ) ; if not equal(P : POSITION, Q : POSITION) then effect(goto(P : POSITION), position(agent, Q : POSITION), neg) ; If

current_time(T1 : TIME) and precedes(T1 : TIME, T2 : TIME) then next_tirne_point(T2 : TIME) ; if

next_time_point(T2 : TIME) and next(X : PROPERTY, S : SIGN) then at_time(X : PROPERTY, S : SIGN, T2 : TIME) ;

220

To execute the temporal rules specified above, updates are required from the current time point to the next time point. These updates are specified by an information link from the component material woddtO itself.

4.2 Symbolic Representation of the Material World In order to reason about the material world and its behaviour, a symbolic representation of the material world is called for. In Figure 5, the component symbolic_representation of material wodd specifies a simulation of such a representation. The vocabulary used within the component symbolic representation of material_worldis specified by the following signature. signature symbolicrepresentation of world_sig sorts WORLD_TERM ;

mets-descriptions matedal_world_sig :

WORLD_TERM ;

relations to be e x e c u t e d b y : to be observed_by: just_acquired :

ACTION * AGENT ; PROPERTY * AGENT ; WORLD_TERM ;

end signature

This signature introduces a new sort WORLD_TERMthat is used in the construction of a meta-description of the signature material world_sig. In the meta-description all n-ary relations of the signature are transformed into n-ary functions into the sort WORLD_TERM.This construction allows, for example, the following atom: just_acquired(current_observation_result_of(car_present, neg, agent)) Within

the component

symbolic_representation_of_material_world n o

knowledge is specified.

The component in principle only models the maintenance of representation states. Also within this component updates are maintained, i.e., whenever an observation has been performed. Updates are specified by an information link from the component symbolic_representation of materialworld t o

itself.

4.3 Transduction Links As discussed in the introduction, there are two issues in using a symbolic representation of the material world. The first is how changes in the material world become to be reflected in the symbolic representation (upward transduction). The second is how changes in the symbolic representation of the world affect the material world itself (downward transduction). In Figure 5, the simulations of transducers are modelled within the framework DESIRE as information links between the output and input interfaces of the components material_worldand symbolic_representation of material_world. The information links that model transducers are called t r a n s d u c t i o n l i n k s (and depicted in italics). The upward transducer is modelled by the transduction link m a t e r i a l e f f e c t u a t i o n of world, the d o w n w a r d by the transduction link symbolic_representation_of world. The downward link transfers actions that are to be performed to the component material_world. Given that the computer systems uses of one or more sensors, observations can be made. The results of observations are transferred to the component symbolicrepresentationof_material_world, by way of the transduction link symbolic_representation_of_world, during which a symbolic representation

221

of the observation results is made that can be processed by the receiving component. In Figure 5 each component has a levelled interface (denoted by the rectangles on side of the components). The transduction link symbolic_representation_of_world transfers epistemic meta-level information on the material world (e.g., expressed by the truth of the atom true(current observation_result_of(car_present, pos, agent))) to object level information that can be used by the component symbolic_representation_of_materiaLwortd (expressed by the truth of the atom just_acquired(current_observation result_of(car present, pos, agent))); atom links

)

true(current_observation result_of(P : PROPERTY, S : SIGN, agent)), just acquired(current_observation_result_of(P : PROPERTY, S : SIGN, agent)) : > ; true(currenLobservation result_of(P : PROPERTY, S : SIGN, agent)), just_acquired(currentobservation_result_of(P : PROPERTY, neg, agent)) : ;

The transduction link material_effectuation_of_world links information on actions to be executed of the component symbolic_representation of_material_world, to meta-level information on the material world: atom links

(

to_be_executed_by(A : ACTION, agent), assumption(current_action_by(A : ACTION, agent), pos) : , , > ; to_be_observed_by(P : PROPERTY, agent), assumption(current_observaUon_by(P : PROPERTY, agent), pos) : , , > ;

In this example, the truth value combinations and ensure that previous actions are retracted, so that actions will not be performed ad infinitum.

5 An Agent's Rational Behaviour in Interaction with the Material World As discussed in Section 4 the downward transduction link is needed for the actual execution of actions. However, the component symbolic_representation_of material_world is not modelled as a component in which rational decisions are made on which observation or action is to be performed and when (pro-active behaviour). Such mental decision processes are modelled in the component agent, see Figure 6.

5.1 Agent The component agent models the cognitive symbolic reasoning system of an agent as a logical system. The rational agent can determine to perform observations and actions. The vocabulary used within the component agent is specified by the following signature.

222

observations

symbolic

and actions

I

material representation of world

agent

observation

I

]

]..~ ][ world J~

symb~176176 material

results

Fig. 6. Transduction and symbolic links connecting agent and material world signature symbolic_agent_sig sorts WORLD_TERM

;

rneta-descriptions material_world_sig functions current_observation_result closeby : own_position : visiting :

relations belief: current_belief: desire: goal: most_recent observation: observed: observed_at: possible_observation: to_beexecuted: to be observed: end signature

WORLDTERM :

;

P R O P E R T Y * SIGN -> W O R L D _ T E R M OBJECT-> PROPERTY ; POSITION ; A G E N T -> W O R L D T E R M ;

;

WORLD_TERM ; P R O P E R T Y * SIGN ; OBJECT ; WORLD_TERM ; P R O P E R T Y " SIGN ; PROPERTY ; P R O P E R T Y * SIGN * T : T I M E ; PROPERTY ; ACTION ; PROPERTY ;

Note again the meta-description construct within this signature. 5.2 The Agent Components The agent is modelled as a composed component consisting of two sub-components, ownprocess_control and maintain_world_information, see Figure 7. The reasoning about its goals, desires, and plans is performed within the component own_process control. Its knowledge about the world, obtained by observations, is maintained within the component maintain_wodO_information.

223

agenttaskcontrol

effectuation [ infoto O P C = ~

own process control

I ~ob

servationresults ~

illobs~ervations actionsand

maintain world

effecruation ~

information

oDsel~r

Fig. 7.

representation fofroraOPC

I~

J representation itlfofrom MWI

wOrlOinlo

Transduction links between the agent and its material representation

The component ownprocess_controlcontains the following knowledge: desire(ice_cream) ; to be observed(own_position(P2 : POSITION)); to be observed(car_present); If desire(G : OBJECT) then possible_observation(position(S : OBJECT, P1 : POSITION)) and possible_observation(close_by(S : OBJECT)) and possible_observation(sells(S : OBJECT, G : OBJECT)); if

possible_observation(P : PROPERTY) and not observed(P : PROPERTY) then to_be_observed(P : PROPERTY); if

current_belief(sells(S : OBJECT, G : OBJECT), pos) and desire(G : OBJECT) and current_belief(close_by(S : OBJECT), pos) then goal(visiting(S : OBJECT)); if

goal(visiting(S : OBJECT)) and current_belief(position(S : OBJECT, P : POSITION), pos) and current_belief(own_position(Q : POSITION), pos) and current_belief(car_present, neg) and current_belief(next_on_path(R : POSITION, Q : POSITION, P : POSITION), pos) then to be executed(goto(R : POSITION)); if

current_time(T:TIME) and just_acquired(current_observation_result(P : PROPERTY, S : SIGN))

then observed_at(P : PROPERTY, S : SIGN, T : TIME);

The link observed_world_infotransfers the just acquired knowledge about the world from own_process_control tO maintain_world_information.The agent obtains this knowledge by observations. The link updates the truth values of the atom most_recent_observation ensuring that the atom indeed reflects the most recent information about the world.

224

The link most_recent_observation results determines the beliefs that are to be held by the agent (within its component own_process_control).

5.3 Symbolic Links The symbols representing the decisions to perform observations and actions are linked to the symbolic system modelled by the component symbolic_representation of material_world. All connections between symbolic systems are called symbolic links. Symbolic links are modelled as information links within the framework DESIRE. The symbolic link that transfers the symbolic representations of observations and actions that are to be performed is called observations and actions. This link connects the object level of the output interface of the component agent with the object level input interface of the component symbolic representation_of_materiaLworld: term links (close_by(O : OBJECT), close by_for(O : OBJECT, agent)) ; (own_position(P : POSITION), position(agent, P : POSITION)) ; atom links to be_observed(P : PROPERTY), to be observed_by(P : PROPERTY, agent) : , , > ; to_be_executed(A : ACTION), to_be executed_by(A : ACTION, agent) : , , > ;

The results of observations performed within material_world are transferred to the component agent through the transduction link symbolic_representation_world(see previous section) and the symbolic ]ink observation_results that connects the component symbolic_representation of materialworld t o t h e c o m p o n e n t

agent:

term links (close_by_for(O : OBJECT, agent), close by(O : OBJECT)) ; (position(agent, P : POSITION), own position(P : POSITION)) ; atom links just_acquired(current_observation_result_of(X : PROPERTY, S : SIGN, agent)), just_acquired(current_observation_result(X : PROPERTY, S : SIGN)) : , > ; just_acquired(current_observation_resutt_of(X : PROPERTY, S : SIGN, agent)), observed(X : PROPERTY) : > ; current_time(T : TIME), current_time(T : TIME) : , , > ;

6 An Agent and its Material Representation In Figure 8 the cognitive symbolic system of the agent is modelled by the component agent described in the previous section. The component material_representation_ofagent models the material representation of (the symbolic system of) the agent. As discussed in the Introduction, the relation between the agent and its material representation is modelled in a manner similar to the manner in which the relation between the material world and its symbolic representation is modelled. An upward

225

transducer defines how symbolic aspects of the agent are represented in a material form, a downward transducer defines how properties of the material world affect the processes of the symbolic system within the agent.

material representation of agent

symbolic effectuationof agem I I material representationof agent

agent

Fig. 8.

Transduction links between the agent and its material representation

6.1 Material Representation of an Agent The vocabulary used within the component material_representation_of_agentis specified by the following composed signature. signature rnaterial_representation_of agent_sig signatures generic_material_world_sig, specific_material brain_sig ; meta-descriptions symbolie_agent_sig: AGENT_ATOM ; end signature

The signature symbolic_agent_sigis used in a meta-description construct, such that the relations of that signature can be used as functions into the sort AGENTATOM.The sort AGENT_ATOMis a sub-sort of the sort AGENT_PROPERTY,which in turn is a sub-sort of the sort PROPERTY.Therefore, all relations that have PROPERTYas an argument and are specified in the signatures generic_material world_sig and speeific_material_brain_sig can be applied to the new terms. Within the component material_woad a simple model for memory is specified. The component material_representation_of_agent only maintains a state, from and to which information is transferred to and from the component materiaLworld.The only exception

226

is the following knowledge base rule that combines the information to be stored and the current time point and determines the actual storage of the information as a physical property: if

current_time(T : TIME) and to_be_stored(A : AGENT_ATOM, S : SIGN) then at_time(position(information_object(A : AGENT ATOM, S : SIGN), stm_location(A : AGENT_ATOM, T : TIME), pos, T : TIME) ;

6.2 Transduetion Links The information maintained by the agent is built of atoms with an explicit reference to their truth value in the form of a sign. The atom is transformed into a term by the transition from the agent to its material representation. For example, the atom observed_at(car_present, neg, tl) that could be used within the component agent, is represented by a term within the component material_representation_of_agent.I f the atom is true within agent, the sign pos is to be added within material_representation_of_agent,i f t h c atom was false, the sign neg is to be added. If the atom has the truth-value unknown it is not stored in material_representation of agent. If the agent, by reasoning, makes cognitive changes in its beliefs, desires, goals, or knowledge, the material representations of these changes are materialised in the brain. This process of upward transduction is modelled (see Figures 7 and 8) by the transduction links material representation_or_agent, representation into from OPC and representation into from_MWl. A s a n example, the atom links of the transduction link representation into from_OPC are specified as follows: atom links

( ) ( )

true(A : IIOA), to be stored(A : A G E N T A T O M , pos) : > ; false(A : IIOA), to be stored(A : AGENT ATOM, neg) : > ;

An example of an instantiated atom link of representation into from_OPC is: ( )

true(observed_at(car~oresent, neg, tl)), to_be stored(observed_at(car present, neg, tl), pos) : > ;

An example of an instantiated atom link of material_representation of_agent is: ( )

to be stored(observed at(car present, neg, tl), pos), to be stored(observed_at(car present, neg, tl), pos) : > ;

For simplicity in this paper it is assumed that there exist functions that relate information in memory to locations within the brain, i.e., positions: position(I : INFORMATION_OBJECT, B : BRAIN_LOCATION)

The simple model for memory used in this paper has a short-term memory and a long term memory. To model this distinction, the sub-sort BRAIN_LOCATIONof POSITIONhas two sub-sorts: STM_LOCAT~ONand LTM_LOCAnON.Given the atom of the agent (a term of the sort AGENT_ATOM)and a time point (a term of the sort TIME), the function stm_location relates information to a position within the short term memory, whereas ItmJocation

227

relates information to a position within the long term memory. The time point used by the function is the moment in time that the information is stored into the memory. An information object is specified as information_object(A : AGENT_ATOM, S : SIGN),

where the sort AGENTATOM contains objects that refer to atoms of the agent, e.g., observed_at(car present, neg, tl). The current status of the memory is modelled by atoms of the form: currently(position(information_object(A: AGENT_ATOM,S : SIGN),B : BRAIN_LOCATION),pos) If a physical change within the component material_representation_of_agent occurs, the symbolic interpretation of the changed information is linked to the component agent by the downward transduction process, modelled by the transduction links symbolic_effectuation__of_agent, effectuation_info to_OPC and effectuation_info_to_MWl. T h e atom link of the transduction link effectuation_infoto__oPc are specified as follows: atom links

( )

currently(position(information_object(A : AGENT_ATOM, S : SIGN), B : STM_LOCATION), pos), assumption(A : AGENT_ATOM, S : SIGN) : , , > ;

By these transduction links object level information from the component material_representation_of_agent is transferred to meta-level information within the component agent, which defines the current information state of the agent.

material updatematerial representatio~ of age

representation of agent

~pdate naterialworld

slmboliceffectuationof agent

I

materialrepresentationof agent

material world

Fig. 9. Transduction and material links connecting material world and agent

228

7 The Material World's Physical Behaviour in Interaction with an Agent The material representation of the agent is a part of the material world. Therefore, the component material_representation_of_agentis modelled as a simple component for passing on information. The material links connecting these two components (see Figure 9), update_material_worldand update_material_representation_ofagent, are s i m p l e i d e n t i t y l i n k s , i.e.,

they only transfer information, they do not translate it. For example, the material link update_material_representation_of_agentlinks atoms to themselves: ( )

at_time(position(I : INFORMATION_OBJECT, B : BRAIN_LOCATION), S : SIGN, T : TIME), at_time(position(I : INFORMATION_OBJECT, B : BRAIN_LOCATION), S : SIGN, T : TIME) : , , > ;

8 The Complete Model As can be seen from Figures 5, 6, 8, and 9 it is possible to create a symbolic representation of a material system and to create a material representation of a symbolic system. In Figure 10 all components and all information links (transduction, symbolic and material links) of the top level of the complete model are presented. Together, they sketch two connections between the agent and the material world. The connection between material representations and symbolic representations is made by transduction links, between symbolic representations by symbolic links and between material representations by material links.

9 Trace of the Example Interaction Patterns In this section it is shown how the course of events in the example introduced in Section 2 is simulated as a reactive pattern using the model introduced in the previous sections. The trace is started at the moment that the agent is in position pl and has observed that a supermarket where ice cream is sold is at position p3, and that a path from pl to p3 is available with p2 as next position. Moreover, the agent has observed that no car was present. These observations where made using the transduction links symbolic_effectuation of world and material_representation of world b e t w e e n the m a t e r i a l w o r l d

and its symbolic representation, and the symbolic links observations_andactionsand observation_results. As a result the observation information is available within the agent (as current beliefs). The trace is started at time point tl. The situation at time tl is represented in Figure 3. - reasoning within the component agent;

it derives conclusions goal(visiting(supermarket)),to_be_executed(goto(p2)) transfer the action to the material world

by the symbolic link observations and actions tO the component symbolic_representation_of_materialworldand b y the d o w n w a r d t r a n s d u c t i o n l i n k materiaI_effectuation of world tO the c o m p o n e n t material_world

229

r"reTaT_

update material

martial

1

of agent

~rial

representation r'] update world

o

reS;eT~nb~176

s)~aboliceffectuationof agent I material representationof agent

[El

of materialworld

II

material rep. . . . . . tion of world [ I s)mbolic effectuationof world

agent

matenaJ world

observation results

Fig. 10. Transduction, symbolic and material links connecting agent and material world

- execution o f the action and the event car appears within the material world; determination o f consequences thereof see Figure 4.

determination of the effect at_time(position(agent,p2), t2) of the action goto(p2); determination of the effect aLtime(position(car,p2), t2) of the event car__to_appear; determination of aLtime(has_hit(car,agent),t2); determination of at_time(has_amnesia(agent),t2); no determination of at_time(position(l:lNFORMATION_OBOECT, B:STM_LOCATION),pos, 12) because the condition currently(has_amnesia(agent),neg) lacks; - transfer the effects to the agent

the material effects and their consequences are transferred by the material link update_material_representation_oLagentand from there by the downward transduction link symbolic_effectcation_of_agenttO the component agent; because of this no facts are available anymore that were materially represented in the STM memory: the agent looses facts such as: goal(visiting(supermarket)), to be executed(goto(p2)),and all observations performed at the previous two time points - reasoning o f the agent with lost STM.

the agent has a severe lack of knowledge about its current situation; e.g., what is its position, what was its goal; it decides to observe again. By new observations information on the current material situation can be obtained; however, information about its (previous) goal cannot be found that easily. and so on .....

230

10 Other Types of Interaction Patterns Between Mind and Matter In the previous sections the example course of events was simulated as a reactive pattern trough Figure 10 from the lower left hand side component (agent) tO the upper right hand side component (symbolic_representation_of_material_world)to the lower right hand side component (material world) tO the higher left hand side component (materialrepresentation_of_agent) to the lower left hand side component (agent). Also for various other types of interaction between symbolic systems and material systems such patterns can be identified. In this section a number of examples are discussed. 9

Drug use

Using the model introduced in this paper the process of taking a (narcotic) drug can be simulated as follows (see Figure 10): - decision o f the agent to take the drug

reasoning within the component agent; deriving conclusion to_be executed(take_drug) - transfer the action to the material world

by the symbolic link observations and actions to the component symbolic_representation of material_worldand by the downward transduction link material_effectuation_of_worldtO the component material_world - execution o f the action take drug within the material world

determination of the effect active_brain of the action takedrug - transfer the effects o f take drug to the agent

by the material link update_material_representation of_agentand the downward transduction link symbolic effectuation of agenttO the component agent - execution o f the agent with drug effect 9

Agents planning and executing birth and death

Using the model introduced in this paper the process of creating a new child agent by a rational agent can be simulated by a similar pattern in Figure 10: - decision o f the agent to create a child agent

reasoning within the component agent; deriving conclusion to be executed(create_child) - transfer the action to the material world

by the symbolic link observations_and_actionstO the component symbolic_representation_of_material_worldand by the downward transduction link material_effectuation of worldtO the component material_world - execution o f the action create chiM within the material world

determination of the effect of the action createchild - transfer the effects o f to create child to the agent

by the material link update_material_representation_of_agent and the downward transduction link symbolic effectuatioo_ot_agenttO the component agent; this link modifies the component agent by replacing it by two similar components - execution o f the agent and its child agent

In a similar manner a rational action to kill an agent can be modelled,

231

9 Psychosomatic diseases For psychosomatic diseases the pattern in Figure 10 proceeds in a different direction: from the lower left hand side component to the upper left hand side component to the lower right hand side component. For example, a heart attack induced by psychological factors can be modelled as follows. the agent reasons about highly stress-provoking information stressful reasoning within the component agent transfer of the stress to the material representation of the agent by the upward transduction link material_representation of agent to the component materialrepresentation_of_agent (to the property over_activebrain) and by the material link update_material_worldto the component material wodd - execution of the material world determination of the effect over_active_brainto heart functioning

11 Discussion Internal representations of the material world as maintained by an agent, are related to the material world by a representation/reference relation. In this paper a simulation model is introduced covering both a sub-model for the agent (simulating its mental processes) and a sub-model for the material world (simulating its physical processes). The semantical relations between the two sub-models are formalised as dual repesentation relations. In the model it is taken into account that the agent's mind has a materialisation in the form of a brain. Most parts of the specification of the model are generic; although the example instantiation that is used to illustrate the model is kept rather simple, the generic part of the model can be (re)used to simulate a variety of phenomena in which (multiple) mind-matter interactions occur. The compositional design method DESIRE supports that specific components in the model can be replaced by other components without affecting the rest of the model. For example, more sophisticated memory models can replace the rather simplistic model used as an illustration in this paper. The work in this paper is of importance for 9 foundational questions from a philosophical and logical perspective 9 research in cognitive psychology, neuro-physiology, and their relation 9 application to dynamic multi-agent domains in which agents can be created and killed The relevance of the model for each of these three areas will be explained. An interesting foundational philosophical and logical issue is the semantics of dual representation relations (see also [Hofstadter, 1979]). Both from a static and from a dynamic perspective further questions can be formulated and addressed. For example, the further development of a foundation of semantic attachments and reflection principles [Weyhrauch, 1980] in the context of dual representation relations, and dynamically changing mental and physical states. Another question is the semantically sound integration of (qualitative and quantitative) simulation techniques and (temporal) logical modelling.

232

Cognitive and neuro-physiological models can be semantically integrated using the model introduced in this paper. The presented generic model can be instantiated by existing models of both kinds. A useful test for existing philosophical approaches to the mind-body problem (e.g., such as described in [Harman, 1989]) is to investigate the possibility to operationalise them using the presented model. Among the applications of the model are agents capable of planning and executing life affecting actions, such as giving birth and killing (other) agents. These capabilities are essential for Internet agents that can decide on the fly to create new agents to assist them in their tasks and removing these agents after completion of the task they were created for. This application area is one of the focusses of our current research.

References Brazier, F.M.T., Dunin-Keplicz, B., Jennings, N.R and Treur, J. Formal specification of Multi-Agent Systems: a real-world case. In: V. Lesser (ed.), Proceedings of the First International Conference on Multi-Agent Systems, ICMAS'95, MIT Press, Cambridge, MA, 1995, pp. 25-32. Brazier, F.M.T., Dunin-Keplicz, B., Jennings, N.R. and Treur, J. DESIRE: modelling multiagent systems in a compositional formal framework, International Journal of Cooperative Information Systems, M. Huhns, M. Singh, (eds.), special issue on Formal Methods in Cooperative Information Systems: Multi-Agent Systems, vol. 6, 1997, pp. 67-94. Brazier, F.M.T., Jonker, C.M., Treur, J., Formalisation of a cooperation model based on joint intentions. In: J.P. MOiler, M.J. Wooldridge, N.R. Jennings (eds.), Intelligent Agents III (Proc. of the Third International Workshop on Agent Theories, Architectures and Languages, ATAL'96), Lecture Notes in AI, volume 1193, Springer Verlag, 1997, pp. 141155. Brazier, FM.T., Treur, J., Wijngaards, N.J.E. and Willems, M. Temporal semantics of complex reasoning tasks. In: B.R. Gaines, M.A. Musen (eds.), Proceedings of the 10th Banff Knowledge Acquisition for Knowledge-based Systems workshop, KAW'96, Calgary: SRDG Publications, Department of Computer Science, University of Calgary, 1996, pp. 15/1-15/17. Chang, C.C., Keisler, H.J., Model theory, North Holland, 1973. Dalen, D. van, Logic and Structure, Springer Verlag, 1980. Engelfriet, L, Treur, J., Temporal Theories of Reasoning. In: C. MacNish, D. Pearce, LM. Pereira (eds.), Logics in Artifical Intelligence, Proceedings of the 4th European Workshop on Logics in Artificial Intelligence, JELIA '94, Springer Verlag, 1994, pp. 279-299. Also in: Journal of Applied Non-Classical Logics 5 (1995), pp. 239-261. Harman, G. Some Philosophical Issues in Cognitive Science: Qualia, Intentionality, and the Mind-Body Problem. In: (Posner, 1989), pp. 831-848. Hofstadter, D., G~del, Escher, Bach: an Eternal Golden Braid. Basic Books, New York, 1979. Hodges, W., Model theory, Cambridge University Press, 1993. Laird, J.E., Newell, A., Rosenbloom, P.S., Soar: An architecture for general intelligence. Artificial Intelligence 33 (1987), pp. 1-64. Lindsay, P.H. Norman, D.A., Human Information Processing. Academic Press, 1977. Newell, A., Physical Symbol Systems, Cognitive Science 2 (1980), pp. 135-184.

233

Newell, A., Rosenbloom, P.S., Laird, J.E., Symbolic Architectures for Cognition. In: [Posner, 1989], pp. 93-132. Posner, M.I. (ed.), Foundations of Cognitive Science. MIT Press, 1989. Pylyshyn, Z.W., Do mental events have durations? Behavioral and Brain Sciences, vol. 2 (1979), pp. 277-278. Pylyshyn, Z.W., Computation and Cognition: Towards a Foundation for Cognitive Science. MIT Press, 1984. Pylyshyn, Z.W., Computing in Cognitive Science. In: [Posner, 1989], pp. 49-92. Simon, H.A., Kaplan, C.A., Foundations of Cognitive Science. In: [Posner, 1989], pp. 1-48. Smith, B.C., Reflection and semantics in a procedural language. MIT Comp. Science Tech. Report 272, Cambridge, Massachusetts, 1982. Smith, B.C., Prologue to Reflection and Semantics in a Procedural Language. In: Brachman, R.J., Levesque, H.J. (eds.), Readings in Knowledge Representation. Morgan Kaufman, 1985, pp. 31-40. Tarski, A., Der Wahrheitsbegriff in den formalisierten Sprachen. Studia Philosophica, 1:261405, 1936. English translation: A. Tarski, Logic, Semantics, Metamathematics. Oxford University Press, 1956. Treur, J., Completeness and definability in diagnostic expert systems, In Proceedings European Conference on Artificial Intelligence, ECAI-88, 1988, pp. 619-624. Treur, J., Declarative functionality descriptions of interactive reasoning modules, In H. Boley, M.M. Richter (eds.) Proceedings International Workshop on Processing of Declarative Knowledge, PDK'91, Lecture Notes in Artificial Intelligence, vol. 567, Springer-Verlag, 1991, pp. 221-236. Treur, J., Temporal Semantics of Meta-Level Architectures for Dynamic Control of Reasoning. In: L. Fribourg, F. Turini (ed.), Logic Program Synthesis and Transformation - MetaProgramming in Logic (Proc. of the Fourth International Workshop on Meta-Programming in Logic, META'94), Lecture Notes in Computer Science, vol. 883, Springer Verlag, 1994, pp. 353-376. Weyhrauch, R.W., Prolegomena to a theory of mechanized formal reasoning, Artificial Intelligence 13 (1980), p 133-170.

Delegation Conflicts

Cristiano Castelfranchi - Rino Falcone IP - CNR - Roma, Italy Division of "AI, Cognitive Modelling and Interaction'* {cris, faleone} @pscs2.irmkant.rm.cnr.it

Abstract. In this paper we study possible conflicts arising between an agent (the "client") delegating some tasks to some other agent, and this agent (the "contractor") adopting and/or satisfying those tasks; conflicts which are either due to the intelligence and the initiative of the delegated agent or to an inappropriate delegation by the client. We present a plan-based definition of delegation, adoption and task, and a theory of different kinds and levels of delegation and adoption. We examine two kinds of conflicts due to different cooperative attitudes between the two agents: 9 conflicts that arise when the provided help does not match the intended delegation (sub help, over help, critical help, hyper-critical help). In particular, we examine paradoxical conflicts due to the agent's willingness to collaborate and to better and deeply help the other, included conflicts due to the tutorial (paternalistic) attitude of the helper. ~ conflicts between the reasons and motives for the adoption as planned by the client and the reasons for adopting offered by the helper. We neither analyse conflicts due to some misunderstanding or to the helper's personal interest, nor conI'licts about the delegation of the control. We claim that delegation and its related conflicts are the core of the interaction with any kind of autonomous agent and are relevant for modelling both MAS, organizations, and user-agents interaction. In fact, in order to exploit local knowledge and local adaptation delegation cannot be fully specified.

Introduction Autonomy and intelligence are precious features in those agents one is relying on for some task or function. This is why natural selection developed a mind in some organisms so that they can flexibly and creatively guarantee their biological functions, and see about their adaptation. In fact, if an agent has to passively and mechanically just executing a rigid and very specific task, it will not be able to solve possible problems, to adapt its behaviour to circumstances, to find better solutions, etc. But, of course, there is a tradeoff: the more intelligent and autonomous the agent (able to solve problems, to choose between alternatives, to reason and to plan by itself) the less quickly and passively This research has been supported by Italian National Research Council Project SARI (Coord. Prof. L. Caducei Aiello), 1996-97. A number of aspects of this model are under development within the GOLEM Project (University of Bari - IP-CNR). We would like to thank F. de Rosis for useful comments on previous version of this paper.

235

"obedient" it is. The probability that the provided solution or behaviour does not correspond to what exactly we expect and delegated, increases. In this paper we study possible conflicts arising between a "client" delegating some tasks to some agent, and the "contractor" or in general the agent adopting and/or satisfying those tasks; conflicts which are either due to the intelligence and the initiative of the delegated agent or to an inappropriate delegation by the client. In particular, we examine paradoxical conflicts due to the agent's willingness to collaborate and to better and deeply help the other. In the first part, we present our definition of delegation and adoption, a plan-based definition of tasks, and of different kinds and levels of delegation and adoption. This implicitly also eharaeterises different levels of agency and autonomy (in the delegated agent). Delegation and adoption are two basic ingredients of any collaboration and organization. In fact, the huge majority of DAI and MA, CSCW and negotiation systems, communication protocols, cooperative software agents, etc. are based on the idea that cooperation works through the allocation of some task (or sub-task) of a given agent (individual or complex) to another agent, via some "request" (offer, proposal, announcement, etc.) meeting some "commitment" (bid, help, contract, adoption, etc.). This core constituent of any interactive, negotial, cooperative system is not so clear, well founded and systematically studied as it could seem. Our claim is that any support system for cooperation and any theory of cooperation require an analytic theory of delegation and adoption. We will contribute to an important aspect of this theory with a plan-based analysis of delegation. In the second part we present the most interesting conflicts that arise when the provided help does not match the intended delegation. In particular, we will examine conflicts due to over help (we will distinguish among over, critical, overcritical, and hypercritical help) and to the tutorial (paternalistic) attitude of the helper. In the third part another source of conflict relative to the relationship between delegation and adoption is examined: the conflict between the reasons and motives for the adoption as planned by the client and the reasons for adopting offered by the helper. All these conflicts might be based on some misunderstanding between the delegating and the delegated agent, but we will not consider this case, since we are interested in studying conflicts due to different cooperative attitudes between the two agents, which cannot be solved by a simple clarification, and require other kinds and levels of negotiation. All this is claimed to be important both for supporting human cooperative behaviours and the organisations, and for agents and Multi-Agent (MA) systems. This relevance becomes clear considering on the one hand that the notion of "agent" itself is very often based on the notion of delegation [Mac, Goo, Luc], on the other hand that task delegation and task adoption, and the related conflicts and negotiations are the core problem of MA systems [Carl, Ros] and of their protocols [Had]. Also our examples (see w will provide some evidence of current and future importance of this kind of conflicts in MASs. We claim that delegation and its related conflicts are the core of the interaction with any kind of autonomous agent.

I A plan-based theory of Delegation/Adoption I.I Definitions Informally, in delegation an agent A needs or likes an action of another agent B and includes it in its own plan. In other words, A is trying to achieve some of its goals through

236

B's actions; thus A has the goal that B performs a given action. A is constructing a MA plan [Casl, Kin] and B has a "part", a share in this plan: B's task (either a state-goal or an action-goal). In adoption an agent B has a goal since and until it is the goal of another agent A, i.e. B has the goal of performing an action since this action is included in the plan of A. So, also in this ease B plays a part in this plan. Both delegation and adoption may be unilateral: B may ignore A's delegation while A may ignore B's adoption. In both cases A and B are, in fact, performing a MA plan. In the following we present a plan-based formal analysis of delegation/adoption. We assume that, to delegate an action necessarily implies to delegate some results of that action. Conversely, to delegate a goal state always implies the delegation of at least one action (possibly unknown by A) that produces such a goal state as result. Thus, we consider the couple action/goal x=(ct,g) as the real object of the delegation, and we will call it task. Then by x, we will refer to the action (ct), to its resulting world state (g), or to both. The action (ct) may be either elementary or complex (i.e. a plan). It is possible to delegate not only tasks but also roles [Wer], since roles are based on classes of delegated tasks (see w 1.6). In this way roles are viewed both as social relationships among agents and as defined position in MA-ptans and in organizations. Plan hierarchy affects role hierarchy, and the delegation levels correspond to different levels and entitlements of roles [Cas2]. There exists an hierarchical structure of roles which is related to the kind of task and to the plan structure (for ex. executive roles and decisional roles). Once established a role (role-contract) this creates constraints for task-delegation: the delegation of a specific task must instantiate the role tasks. There could rise conflicts between task-delegation and role-contract, and the negotiation about specific tasks can bring it up for discussion again the related role-contract.

12 Plan Ontology Since the hierarchical nature of a plan and the position of x in it is the basis of the levels of both delegation and adoption, we need to introduce in this section a more formal representation of agents, actions, plans [Poll and action results. For a more detailed description of these concepts see [Cas2, Fall. Let Act={ct I..... cq} be a finite set of actions, let Agt={Al .... A,, B, C .... } a finite set of agents. Each agent has an action repertoire, a plan library, resources, goals, beliefs, interests [Con]. The general plan library is li = II" U II d, where I I ' is the abstraction hierarchy rule set and lId is the decomposition hierarchy rule set. As usual for each action there are: body, preconditions, constraints, results. We will call c t a composed action (plan) in 1-I if there is in II d at least one rule: ct -> cq,..,ct,. The actions cq ..... cq are called component actions of a. We will call et an abstract action (plan] in lI if there is in IT at least one rule: ct --> cq. cq is called a specialized action of ct. An action r is called elementary action in 1-I if there is no rule r in li such that ct' is the left part of r. We will call BAct (Basic Actions), the set of elementary actions in lI and CAct (Complex Actions) the remaining actions in Act: BAct__Act, CAct = Act - BAct.

237

Given cq, o~ and H, we will say that at dominates a2 (or a2 is dominated by al) if there is a set of rules (r~.... r~,) in II, such that: (cq ---Lr0^(cqERr. )^(Lri65Rri.0 where: Lrj and Rrj are, respectively, the left part and the right part of the rule rj and 2.ci,~m. We will say that aj dominates at level k a2 if the set (h .... r~) includes k rules. We will call Act^, the set of actions known by the agent A. Act^_CAct. Called HA the A's plan library, the set of the irreducible actions (through decomposition or specification rules) included in it is composed of two subsets: the set of actions that A believes to be elementary (BAct^) and the set of actions that A believes to be complex but has not reduction rules for (NRAct^: Non Reduced actions). Then BAct^CAct and it might be that BAct^~BAct. In fact, given an elementary action, an agent knows (or not) the body of that action. We will call skill set of an agent A, S^, the actions in BAct^ whose body is known by A (action repertoi re of A). S ^ _BAct^. US~ (on all A,EAgt) __CBAct. I In sum, an agent A has his own plan library, HA, in which some actions (CAct A and NRActA) are complex actions (and he knows the reduction rules o f CActA) while some other actions (BActA) are elementary actions (and he knows the body o f a subset - SA - o f

them). A has a complete executableknow-how of ct if either ctES, or in I'I^there are a set of rules (r~.... rm) able to transform ct in (cq ....a k) and for each l-:i-~k,oqES^. The operator CEK(A,ct) returns ((r,....r,0),then CEK(A,a)~0 (0 is the empty set) when A has at least a complete executable know-how of a. Infact,CEK(A,a) characterizesthe executiveautonomy of the agent A relativeto a. To execute an action a -that we represent with Execution(a)- means: - to execute the body o f a, i f ct is an elementary action or - to execute the body o f each elementary action to which a can be reduced (through one o f the possible sequences o f rules in H), i f a is not an elementary action.

From the previous assertions it follows that an action a might be an elementary action for the agent A while it might he a plan for another agent B. Again, the same plan a could have, for different agents, different reduction rules. Agents execute actions to achieve goals: they look up in their memory the actions fitting the goals, select and execute them. Fixed some pertinent world states c, we will call R(a,c) the operator that, when applied to an action a and to c, returns the set of the results produced by a (when executed alone). We will assume that changing the pertinent aspects of the world state in which an action is applicable, its results will change and the name of the action itself changes. Then R(a,c) may be denoted with R(a) because c are defined in a univoque way.

1 Notice that we do not call; as usual, "body" the decomposition rule of a plan but only the procedural attachment of the elementary actions and their procedural composition in case of complex actions.

238

We define P(tx) = Pc(a) U Cn(ct) where Pc is the operator that when applied to any action ct returns the set of the preconditions of ct and Cn(ct) is the set of constraints of tx. An action a can be executed if preconditions and constraints of ct are satisfied. RA(Ct) returns the results that A believes ct will produce when executed. R^(ct) might (or not) correspond with R(ct), however when an action has been executed each agent in Agt has the same perception of its results: exactly R(ct). For each action ct, Executed(ct)ER(et), after Execution(a), Executed(a) holds. We will call relevant results of an action for a goal (set of world states), the subpart of the results of that action which correspond with the goal; more formally, given a and g, we define the operator Rr such that: Rr(ct,g)={g~ I ~(Eg} if gC_R(ct), --0 otherwise. Then, the same action used for different goals has different relevant results. Let us suppose that ct is a component (or specialized) action of ct' and Rr(ct',g),~0; we define pertinent results of ct in ct' for g, Pr(ct,ct',g), the results of tx useful for that plan ct' aimed at the goal g; they correspond with a subset of R(ct) such that: 1) if ct is a component action of ct': P r ( a , a ' , g ) = {q~ER(a)) I ( q ~ R r ( a ' , g ) ) v ((qi=P(al))^(dominate-level-1 a' al)^(a,~al))}; in other terms, an action ct is in a plan ct' (aimed at a goal g) either because some of its results are relevant results of a ' (aimed at g) or because some of its results produce the preconditions of another action cq in that plan. 2) if ct is a specialized action of ct': Pr(ct,ct',g) = {ql(ch~R(et)) ^ (~ljl (qjE~Rr(a',g)) ^ (q~ is a specialization of %)2 }. The pertinent results of an action ct in a' represent the real reason for which that action ct is in that plan ct'. Hereafter we will call A the delegating-adopted agent (the client) and B the delegatedadopting agent (the contractor).

1.3 Types and levels of delegation Delegation is a "social action", and also a meta-action, since its object is an action. We define the operator of delegation with 4 parameters: (Delegates A B x d), where A,BEAgt, x=(ct,g), d=deadline. This means that A delegates to B the task x with the deadline d. In the following we will put aside both the deadline of x, and the fact that in

2 Let us define temporary results of an action a in a plan ct', the results of a that are not results of ct': Tr(a,a') = {q~ I(o~ER(a)) ^ (qi~R(a')))}. We define transitory results (or pertinent temporary results) of an action a in a plan (1' aimed at the goal g: TRr(ct,a',g) = Tr(a,a')^Pr(a,ct',g) they correspond with those results of a that enable another action cq in a ' but that are not results of a ' aimed at the goal g: TRr(a,(x',g)={q~ I (qER(a)) A (qi~R(et')) ^ (o~=P(a~)) ^ (dominate-levei-I ct' cq) ^ ((x,~al)}. Let us define relevant results of ct in a ' aimed at g: Rr(a,a',g)={q~l(q~ER(a))^ (q,O~'(a' ,g))}

239

delegating x, A could implicitly delegate also the realization o f a preconditions (that normally implies some problem-solving and/or planning). We can consider several dimensions of the notion of delegation.

Relation-based types of delegation Different types of delegation may be characterized on the basis of the relation between the delegating agent and the delegated one. Weak delegation - there is no agreement, no request or even influence: A is just exploiting in its plan a fully autonomous action of B. More precisely, a) The achievement o f x 3 is a goal of A. b) A believes that exists another agent B that has the power of [Cas3] achieving x. c) A believes that B will achieve x in time. c-bis) A believes that B intends to achieve x in time (in the case that B is a cognitive agen0. d) A prefers 4 to achieve x through B. e) The achievement of x through B is the goal of A. f) A has the goal (relativized to (e)) of not achieving x by itself. We consider (a, b, c, and d) what the agent A sees as a "Potential for rely on" the agent B; and (e and f) what A sees as the "Decision to rely on" B. We consider "Potential for rely on" and "Decision to rely on" as two constructs temporally and logically related to each other. All these cognitive ingredients behind the act of delegation represent A's trust towards B.

Mild delegation - there is no agreement, no request, but A is itself eliciting, inducing in B the desired behaviour in order to exploit it. Strict delegation: - is based on B's adopting A's task normally in response to A's request/order.

Specification-based types of delegation The object of delegation (x) can be minimally specified (open delegation), completely specified (close delegation) or specified at any intermediate level. Let us consider two main cases from A's point of view:

3 , Achieve T" means either i) 3a' EAct with gc_R(a') and (Executed a g , if x=g or ii) (Executed a), if either x=-(a,g) or x=a. 4 This means that, either relative to the achievement x or relative to a broader goal g' that includes the achievement of'c, A believes to be dependent on B [Sic].

240

- Pure Executive (Close) Delegation: when either etESA,or et~BActA, or g is the relevant result of et (and ct~SA or et~BActA). In other words, the delegating agent believes to delegate a completely specified task. - Open Delegation: either etC-CActA,or etENRAct^; and also when g is the relevant result of ct (and etECAct^ or et~NRAct^). In other words, agent A believes to delegate an incompletely specified task: either A is delegating a complex or abstract action, or he is delegating just a result (state of the world). The agent B can (or must) realize the delegated task in an autonomous way. Implicit aspects of the delegation produce various possible misunderstandings among the agents. A's perspective can be in contrast with B's point of view: x can be considered at different levels of complexity from the two interacting agents (see table 1). iiii ii

A,'s point of view x=a with (ctESA)v(et~BActA) ~It is an elementary action/~ x=et with (ctENRActA)v I(ctECActA) "It is a complex action/"

correspondin~ B's point of view (a~SB)v(aEBActa) (aC--CActa)v(aENRActB) "It is an elementary action/" -> No Conflict (a~SB) v (et~BActB)

"It is a complex action/" .> Conflict (aECActa)v (ctENRActB)

"It is an elementary action.'" -> Conflict

"It is a complex action/" -> No Conflict

table 1 It is worth to understand the great importance of open delegation in collaboration theory. On the one hand, we would like to stress that open delegation is not only due to A's preference (utility) or practical ignorance or limited ability (know how). Of course, when A is delegating to B x, he is always depending on B as for x [Sic]: he needs B's action for some of his goals (either some domain goals or goals like saving time, effort, resources, etc.). However, open delegation is fundamental because it is also due to A's ignorance about the world and its dynamics. In fact, frequently enough it is not possible or convenient to fully specify 9 because some local and updated knowledge is needed in order for that part of the plan to be successfully executed. Open delegation is one of the basis of the flexibility of distributed and MA plans. To be radical, delegating actions to an autonomous agent always requires some level of "openness~: the agent cannot avoid monitoring and adapting its own actions. On the other hand, we would like to show how the distributed character of the MA plans derives from the open delegatio. As we saw, A can delegate to B either an entire plan or some part of it (partial delegation). The combination of the partial delegation (where B might ignore the other parts of the plan) and of the open delegation (where A might ignore the sub-plan chosen and developed by B) creates the possibility that A and B (or B and C, both delegated by A) collaborate in a plan that they do not share and that nobody entirely knows: that is a distributed plan [Gro, Con]. However, for each part of the plan there will be at least one agent that knows it.

241

Kinds of delegation object The object of the delegation can be a practical or domain action as well as a meta-action (searching, planning, choosing, problem solving, and so on). When A is open-delegating to B some domain action, it is necessarily also delegating to B some meta-action: at least searching for a plan, applying it, and sometimes deciding between alternative solutions. We call B's discreption about x, the fact that some decision relative to x is delegated to B.

Control-based types of delegation Control is an action aimed at knowing whether an other action has been successfully executed or not. Controlling an action means to verify that its relevant results hold (including the execution of the action itself). Given Rr(ct,g) of any action etEAct, a set of actions ctcr=Act -that we call *control actions o f a a i m e d a t g"- may be associated to it. Each action in ctc can be either an elementary or a complex action. The relevant results of each CtkECtc for the goal of controlling ct can be indicated through Rr(ct k (control ct g)). It returns the truth values of each &Eg in Rr(ct, g). Plans typically contain control actions of some of their actions. When A is delegating a given object-action what about its control actions? Considering, for simplicity, that the control action is executed by a single agent, when (Delegates A B x) there are four possibilities: i) ~l~kEaC l(Delegates A B at) (i.e., A delegates the control to B); ii) =lo,,Ea ' , :iX~_Agt (with (X,,A)^(X,,B)) I (Delegates A X otO (i.e., A delegates the control to a third agent); iii) For each a t E c : , for each X~_Agt I (not (Executed cto) (i.e., A gives up the control); iv) :lCtkE/Ct~ I (Executed ak)^(Agent (Execution ctk )=A) (i.e., A maintains the control). Given a plan ctEActA with its component actions (cq ..... a,), if A delegates to B the whole ct, for each action oq~(a~ ..... a,3 A can apply all previous control possibilities. For sake of brevity in this paper we will not consider the rich and important typology of control-related conflicts. 5

5 Apart from the control goals and actions, other collateral goals/actions normally support any action in any plan: coordination and support goals~actions. When A has a plan a with two component actions a; and a2, and he intends to execute it, he also has the goal of coordinating the execution of a, with the execution of oh, to avoid the creation of possible obstacles to oh or to create favouring conditions for it (or viceversa). What happens of these coordination and support goals when A delegates a 2 to B? Like in the case of the control goals, there are several possibilities: i) A drops any coordination goal (that is quite risky); ii) A maintains the goal of coordinating his own action a, with B's task, avoiding conflicts and creating favouring conditions: collaborative coordination (this notion is quite close to [Gro] notion of "Intention that", although we derive it from a general plan property); iii) A delegates this goal to the agent C that has been delegated the action a~ (like he can delegate to B the goal of reciprocally coordinating oh with r

242

1.4 Types and levels of Adoption (Help) In analogy with the delegation we introduce the corresponding operator for the adoption: (Adopts B A x). We consider now some dimensions of the adoption notion.

Relation-based types of adoption Weak adoption - there is no agreement, no information or even influence: B autonomously has the goal of performing a given action since and until this action is either contained in A 's plan or it is an interest (see w 2) of A itself. Notice that this kind of help can be completely unilateral and spontaneous from B, and/or even ignored by A. In other words, B can adopt some of A's goals independently on A's delegation or request. More precisely, a') B believes that the achievement of "r is a goal of A. b') B believes that B has the power o f achieving T. c') B believes that A will not achieve x by itself. d') B intends to achieve x for A (i.e., B has the goal to achieve x relativized to the previous beliefs). In analogy with the weak delegation we consider (a', b', and c') what the agent B sees as a "Potential for weak adoption" of the agent A; and (d') what B sees as the "Decision to

weak adopting" A. Strict adoption: - there is an agreement between A and B about A's task delegation to B in response to B's offer (or B's adopting A's task in response to A's request/order).

Delegation-Adoption (Contract) In Strict Delegation, the delegated agent knows that the delegating agent is relying on it and accepts the task; in Strict Adoption, the helped agent knows about the adoption and accepts it. In other words, Strict Delegation requires Strict Adoption, and viceversa: they are two facets of a unitary social relation that we call "delegation-adoption" or "contract" .6 There is a delegation-adoption relationship between A and B for x, when:

It does not seems possible to delegate coordination goals to a third agent different from the agent that has to execute a~, like for monitoring and control goals. 6 Our treatment of delegation/adoption relationship can be distinguished from that of Haddadi [Had] for several aspects like the fact that we introduce the more basic elements of weak adoption and weak delegation. The most relevant difference seems to be the fact that for us in the goal adoption the contractor acquires a new goal (changes his mind) and does so just because it is a goal of the other agent (the client). This makes our notion of adoption more dynamic and flexible, covering several types of social relationships and also clearly related to the notion of influencing [Cas3].

243

"Potential for request of contract" from A to B: - On A's point of view: a) The achievement of x is a goal of A. b) A believes that exists an agent B that has the power of achieving x. d) A prefers to achieve x through B. - On B's point of view: b') B believes that B has the power of achieving x.

"Agreement": A series of mutual beliefs (MB) are true: (MB A, B, (a, b, c, d, e, f, h, a', b', c', d')) where: h) B is socially committed to A to achieve x for A. The delegation/adoption relation is the core of the social commitment [Cas4, Sin, Fik, Bon] relationship. Thus, it is a basic ingredient for joint intentions, true cooperation and team work [Kin, Lev,,Gro]. In other words, we claim that in collaborative activity each partner is relying on the other partners "strongly" delegating them some tasks, and, viceversa, each partner is adopting by the other partners his own tasks. Both delegations and adoptions can be either explicit or implicit.

Levels of adoption relative to the delegated task

Literal help - B adopts exactly what has been delegated by A (elementary or complex action, etc.). Overhelp - B goes beyond what has been delegated by A without changing A's plan. Critical help - B satisfies the relevant results of the requested plan/action, but modifies that plan/action. Overcritical help - B realizes an Overhelp and in addition modifies/changes that plan/action. Hyper-critical help - B adopts goals or interests of A that A itself did not take into account: by doing so, B neither performs the action/plan nor satisfies the results that were delegated.

1.5 Levels of agency Notice that the open delegation presupposes some cognitive skills in the delegated agent. The same holds also for certain kinds of adoption which presupposes in B the ability for plan abduction and recognition and agent modelling [Cas2]. Types and levels of delegation characterize the autonomy of the delegated agent. The autonomy of the delegated agent (B) from the delegating one (A) increases along various dimensions: - the more open the delegation, or - the many the control actions given up or delegated to B, or - the many the delegated decisions (discretion), or - the less dependent is B on A as for the resources necessary for the task [Sic], the more autonomous B is from A as for that task.

244

1.6 Delegation and Role

Agents delegate roles as they delegate tasks. In a broad sense any task delegation is the creation of a role: in fact, given an occasional execution of any plan through the execution of its component actions by more than one agent, one might say that these agents have a given "role" in that plan and group. This is a "transitory" or occasional role. However, we decide to use (as usual) the term "role" only for more long term and stable organizations; and to use just the term "task" for occasional delegation. As we saw, we can consider the couple action/result x----(ct,g)as the delegation-adoption object. We can specialize the defined contract relation in two subtypes: the task-contract and the role-contract. The task-contract concerns an occasional delegation. Let us define as Role contract or relation (p), the triple p=(A, B, T); where A is the Role Contractor class, B is the Role Client class and T is the Role Task class. More precisely: - T is the "Role domain" or "Role competence": it is the set of the services the role can provide; - A and B are the classes of the contractor agents and of the client agents respectively (in some cases A and B are simply individuals) and there is a relation of DelegationAdoption between these two types of agents about the Role Task (Nomic Task). Then, for each task ~ - T if BiEB, 1; is a potential task of Bi, that is to say the agent is delegated (from A) to bring it about. Analogously, for each task ~ if Ai~A "~ is its potential task to delegate to B.

Levels of delegation relative to the B's role or the B's offered help By comparing the task delegated with the role tasks or with the help spontaneously offered by B, we can characterize various kinds of delegation: - the delegated task matches with the role tasks of the agent or with its offer. - the delegated task is an "over-task" compared with the role tasks of the agent (for example the delegated plan contains the plan in the role task) or with its offer. - the delegated task is a "sub-task" compared with the role tasks of the agent (for example the plan in the role task contains the delegated plan) or with its offer. - the delegated task does not match with the role tasks of the agent or with its offer. The above analysis permits to consider the conflicts arising from the mismatch between due or offered help by B and requested tasks by A. This kind of conflicts is specially important in organizations.

2 C o n f l i c t s a b o u t the l e v e l o f a d o p t i o n

Given this characterisation of delegation and adoption and of their plan-based levels, we can derive a basic ontology of conflicts arising between the two agents when there is a mismatch between the intended delegation and the intended adoption. These mismatches are neither due to simple misunderstandings of A's request/expectation or of B's offer, nor to a wrong or incomplete plan/intention recognition by B.

245

2.1 Sub-help The supporter or the contractor might either offer the adoption or in fact satisfy just a subgoal of the delegated task. Given the situation: (Delegates A B x=-(ct,g)) with (dominates ct' ct). In other words, A delegates (to B) x within x'=(ct',g'), then if (Adopts B A lrl) ,~ (dominates a al) we say that B subhelps A (see fig 1). In other words, B does not satisfy the delegated task. Example in conversation: A: "What time is it?", B: "I don't know". A's subgoal that B answers, is satisfied, but the goal (to know the time) is not. Example in practical domain: A delegates to B "make-fettuccinipesto" and B just "make-pesto". ct'(g')

delegated adopted

riga This is due to several possible reasons: B is not able to do all the task; it is not convenient for it; it does not want to help A as for the higher goal because for example it believes that A is able to do it by itself, etc. Let's not go deeply in this area since we are mainly interested in collaborative conflicts which come from B's intention of helping A beyond his request or delegation and to exploit its own knowledge and intelligence (reasoning, problem solving, planning, and decision skills) for A.

2.2 Beyond Delegation B's intention to really collaborate can create some problems. On the one hand, one cannot be satisfied by an agent that is helping it just doing what is literally requested to do. This is not a very collaborative agent. It has no initiative, it does not care of our interests, it does not use its knowledge and intelligence to correct our plans and requests that might be incomplete, wrong or self-defeating. Thus, a truly helpful agent should not be "helpful" in the sense defined by [Cohl]: Va (Bel x (Goal y (Eventually (Done x a))))^ -, (Goal x -, (Done x a)) (P-Goal x (Done x a)) This agent is just adopting an action (not its goal) and in a literal way. It is a mere executor, it obeys, but is not very helpful. It may even be dangerous. However, on the other hand, there are dangers also when the agent take the initiative of helping the client beyond its requests.

246

Conflicts of Over, Critical, Overcritical and Hypercritical Help Given the situation: (Delegates A B x=-(~t,g)) with (dominates ct' et), then if (Adopts B A lrl) ,~ (dominates cs a) A (dominates-or-equal a' al) we say that B overhelps A (see fig.2). ct'(g')

delegated adopted

L ....

fig.2

Example in conversation domain [Car2]: A: "What time is it?", B: "Be calm, it is 5pm and our meeting is at 6pm, we are in time". Both, the delegated action (to inform about time) and the higher, non-delegated results (plan) (to know whether A are late or not; to not be anxious) are adopted and satisfied by B. Practical example: A asks B to prepare the sauce for the ravioli she will prepare for dinner, and B prepares both the sauce and the "ravioli with sauce". Given the situation: (Delegates A B -c=(ct,g)), with (dominates ct' a), then if

(Adopts B A zx) A (zx=(ct~,g)) we say that B makes a critical help of x (see fig.3). In fact, what happens is that B adopts g, that is to say it is sufficient for B to find in Act, an action et x whatever, such that g_cR,(cq). ct'(g') delegated adopted alternative

fig.3 Critical help holds in the following alternative cases: i) (CEK(B,et)=0) v ( g g R , ( a ) ) v (P(a)=false); that is to say, agent B either is not able to execute ct or, on the basis of his knowledge on action results, guesses that g is not among the results of ct, or the conditions of et are not true (and he is unable to realize them). Correspondingly he must guess that there is an action et x such as: (CEK(B,cq),~0) ^

247

(g_CR,(cq)) ^ (P(cq)=true); in other words B finds another way to realize g, using another action ctx in its action repertoire, such that: B is able to realize it, the new action contains g among its results and its conditions are satisfied. ii) B thinks that the other results of ct (beyond g) are in conflict with other goals - in plan or off plan - or interests of the client. On the other side, he thinks that there is an action cq with: (CEK(B,cq),,0) ^ (gCp~(cq)) ^ (P(cq)=true) and the results of o~ are not in conflict with other goals or interests of the client. iii) There is also the case of optimization, where the conditions in (i) are all false but there is an action a , such that g is reached in a more profitable way for B (relative to any criteria). Given the situation: (Delegates A B x=(ct,g)), with (dominates ct' ct), then if (Adopts B A ~rx)A Or,=(ax,g')) we say that B makes an overcritical help of x (see fig.4). In fact, what happens is that B adopts g', that is to say it is sufficient for B to find in Act, an action cq whatever, such that g'g_P~(cq). It is a mixed case in which there are overhelp and critical help at the same time. a'(g')

~ , , ~

~

--

.-.

~

delegated adopted alternative

g) fig.4

Overcritical help holds in the following alternative cases: i) Pr(a,a',g')=0 and at the same time (::lcq~Acts I Pr(ctx,ct',g'),~0 ^ CEK(B,%)~0 ^ P(ct,)=true). In other words, there are no pertinent results of ct in Qt'; but it exists at least one action cq which is pertinent in ct' aimed at g'. This means that a is useless for x'. It is even possible that it is noxious: i.e. that R(ct) produces results that contradict those intended with x'. A is delegating to B a plan that in B's view is wrong or self-defeating. ii) Pr(ct,ct',g'),~0 A CEK(B,a)#0 A P(a)=true and in addition (3 c t , ~ e t B I CEK(B,cq),~0 A P(ct0=true A Pr(cq,ct',g'),dt), moreover iil) R(ct~) achieves the goals internal to the plan (i.e. g') in a better way (maximization). Example: A asks B "to buy second class train tickets for Naples" (action ct) for her plan *to go to Naples by train cheaply" (action a'). B adopts A's goal "to be in Naples and spend little money" (goal g') replacing the whole plan (oh) with another plan: "go with Paul by car'. ii2) R(ctx) achieves not only the goals of the plan (i.e. g') but also other goals of A external to that plan (ex. g"): (g'CR(~))^(g"CR(cq)). Example: A asks B "to buy second class train tickets for Naples" (action ct) for her plan "to go to Naples by train cheaply" (action or'). B adopts A's goal "to be in Naples and spend little money" (goal g') replacing the whole plan (et') with another plan (ctx)

248

"to go with Paul by car" that satisfies also another goal of A - that she did not consider or satisfy in her plan - but B knows: "to travel with friends". ii3) R(cq) achieves not only the goals of the plan but also s o m e interests (i) o f A: (g'CR(a~)) ^ (iCR(oh,)). Example: A asks B "to buy second class train tickets for Naples" (action ~x) for her plan "to go to Naples by train cheaply" (action ct'). B adopts A's goal "to be in Naples and spend little money" (goal g') replacing the whole plan (ct') with another plan (ctO "to go to Naples by bus" that satisfies an interest of A of "not risking to meet Paul that she ignores to be on the same train". Given the situation: (Delegates A B x=(ct,g)), with (dominates a' ct), then if (Adopts B A gO we say that B makes an hypercritical help of x (see fig.5). In fact, B adopts g~, where gt is an interest (or an off-plan goal) of A more important than g' (we leave here this notion just intuitive). Since there is a conflict between the result R(ct) (and/or the result R(ct')) and some g~ of A, to adopt g~ would imply to not obtain R(ct) (or R(ct')). ct'(g') ~ct(g)

1) ~ ~ . . . .

Ql(gl)

~

1

delegated adopted

fig.5 In any case of over, critical, overcritical and hypercritical help there is apparently a conflict, since A has the goal that B does a, while B is doing or intends to do something different. These conflicts can be very rapidly solved for two reasons. First, B's intention is helping A, it is a collaborative one; second, normally B is "entitled" by A (either explicitly or implicitly) to do this deeper help, and A is expecting this initiative and autonomy. Thus, normally there is no real conflict since A is ready to accept B's collaborative initiative. However, sometimes these cases initiate serious conflicts to be negotiated. In fact, A might be against B's initiative or offer for several reasons: - It is not a (better) solution. A disagrees about B's plan. They have different knowledge about domain plans and A is not persuaded (cannot revise his own beliefs converging with B's beliefs). So A do not consider a good or a better solution what B is doing/proposing. (This applies to critical, overcritical and hypercritical help). - I don't trust you. There are two cases. First, A does not believe that the proposed

solution is worst, but he does not rely too much on B's intelligence, competence, or honesty (she might have some personal interest to pursue or suggest that solution; see later). Second, A believes that B is not able to correctly execute the larger plan. (Critical, overcritical and hypercritical help).

249

- Who entitled you? A does not like B's initiative of going his own way. He didn't entitle

B helping him beyond his literal request. Either what he really wants is to be "obeyed" (for example in order to don't lose the control), or he doesn't want the other to be intrusive, or he wants solve his own problems by himself; etc. (Over, critical, overcritical and hypercritical help). - It is not your job~role. This is similar to the previous point, but here B and A disagree about the institutional position of B: A challenges B's pretence of autonomy and/or of high-level collaboration. (Over, critical, overcritical and hypercritical help). - Don't be paternalistic/Nobody knows better than me what is good for me.

This case deserves special attention, since it's based on interest adoption which is the highest level of helpfulness, but also the most risky. (Hypercritical help) As we said several important kinds of conflict are due to either misunderstanding or to wrong/incomplete plan abduction by B, or to B considering his personal interest. These conflicts are out of the scope of this paper.

Tutorial and paternalistic conflicts The adoption of A's interests beyond its request and expectation is the more problematic case. In fact, B is claiming to know better then A what is good for him. Not only to know better than him a solution or a plan for his current goals or for his long term goals. It claims that A does not have the proper goals. When B takes care of A's interests and tries to induce A to pursue certain goals because this is its own interest ('for your benefit") we call this social attitude and relation tutorial. Of course, this attitude is very pretentious and might be annoying for A, which perceives it as "paternalistic'. Any adult agent pretends to know and to be able to decide what is better for him. Nevertheless, it is objective -given our cognitive and rational limits- in many cases that we ignore our interest, what is better for us, and that we do not pursue as goal what we need. Thus this dialectic is intrinsic in any relation of deep and spontaneous help.7 Normally in tutorial adoption and attitude there is conflict since the contractor wants that the client has a different goal from those currently it has. In fact objective "interests" are defined [Con] as something that an agent believes that should be a goal of the other agent, but that (it believes) it is not. Something that is useful or necessary for the other agent, that it is needed, but that the other does not understand or know, or it does not prefer (then ignoring what is better for it). When you believe that I is an interest of yours ('is in your interest') I becomes a goal of yours, although not necessarily a pursued goal. When you believe that I is your prevailing interest (or that is your interest to pursue/), not only I becomes a goal but it is preferred and pursued. What the tutorial agent is trying to do is to influence you to adopt your interest as a goal or the prevailing goal of yours. A tutorial software agent or system, would be an agent that for example answering to your request of reserving a table in a nice restaurant could says "it is too expensive for

7 Tutorial relations not only are annoying because of this pretence to know better than myself my good, but also because B might be hypocrite: it might pass as "my interest" and "for me" what is its interest or the interest of the institution it is representing. In this case it is really paternalistic and is deceiving me (manipulating me).

250

you!", or requested to order a box of whisky answers "alcohol is toxic". Want we such an intrusive and father-like agent? What kind of limits have we to put to this helping intrusion? This would be very annoying, but what about a software agent that without expliciting its criteria just give us advises and plans that are based on our presumptuous interests more than on our actual goals? This might be even worst, even if it has some interesting aspects. Consider for example a tourist adviser that has the goal of avoiding crowding of tourists all in the same famous places and the goal of make them visit beautiful but not well-known monuments. This is claimed to be in the interest of the tourist themselves (but of course is also in the interest of the tourist office, of the city and of its major, etc.). Suppose you say this system in Rome that you have just one day and that you would like to see Caravaggio. Well, this system will adopt your goal, but also your interest, and send you to see some beautiful Caravaggio that is not that famous and perhaps is not what you expected. This may be a very good advice and system: you might appreciate this discover and your quiet enjoying it. But this may also be quite bad: you might be disappointed not seeing the Caravaggio your friends will ask you about when you will be back home. So, what to do? Has this kind of help to be allowed in artificial agents? With what kind of limits? There are several possible remedies: - Agents should tacitly and automatically adopt the interests of the client only when those interests are reconcilable with the client's request, and there is a plan for satisfying both. In this case the over adoption is just a better solution for a broader problem. More formally, A delegates x=(ct,g) and B adopts x'=(ct',g') with (gUi)C_g' (i is an A's interest). In their social life humans accept (and expect) this kind of help only by certain agents (mother, friends, doctor, etc.) relative to certain area of goals (for ex. health from the doctor, salvation from a priest, happiness from a friend). Similarly we could decide whether we want a tutorial attitude by our software agents or helping systems or not, and relative to which domain. In practice, we could establish various classes of tasks where the agent can assume a paternalistic behaviour. The adoption of our interests in case of conflict must be explicit, so that the adopted agent can refuse or at least discuss till is persuaded. In this case the hypercritical adoption must be communicated to the client before being applied. - The agent's refusal of the client's request for tutorial reasons is not allowed (except for some "vital" interest, like life). -

-

3 Conflicts about reasons for adopting In this section we will examine a second type of conflicts due to different cooperative attitudes between the two agents. We will do this in a non formal way, since these conflicts are not relative to the plan but to agent's motivations. Humans not only want goal-adoption and practical help from the others, but they also consider very important -sometimes more important of the adoption in itself- the mental attitude of the helper, the reasons for adopting. In fact they search and plan for a given type of adoption based on specific reasons, and they ask for a given mental attitude, not just for help. Let's give the example of speech acts. The mind of the addressee has been insufficiently analysed in speech act theory [Lev], while it plays a very important role. We claim that

251

speech acts differ from one another as for the different minds the speaker is attempting to obtain from the hearer, the speaker attempts to induce different reasons for goal-adoption in the addressee. The difference between a pray and a command is not only pragmatic, sociological and contextual: a pray is used between a powerless and a powerful person, etc.; a command requires a command position, an authority, etc., therefore it is pragmatically inappropriate that a general prays a soldier or that a soldier gives commands to a general. In our view, the difference is in the cognitive analysis of the act: in the speaker's plan about the hearer's mind. 9 A pray is a request of adhesion for pity, for generosity: I'm planning your mental attitude in helping me. 9 A "please-request" ("could you please...") is searching for courtesy-based adhesion. 9 A command is aimed at obedience: I want you to do that not for any reason but because you acknowledge my authority. This is within the speech-act plan itself, and in its meaning, not in the context! Thus, speech acts prescribe an entire mind rather than a given behaviour only. This pretence by the delegating agent (the speaker) produces several possible conflicts. When you cry "Help me! help me!" drowning in a river you neither want nor expect that the guy which is supposed to help you ask you "What will you give me?". There is a conflict here about the reasons for adoption: you are asking for an altruistic adoption, and the other offers you an exchange-based adoption [Con]. When, after a sexual intercourse, your panner girl asks you for some money, or viceversa when the men puts some money on the table, there is a terrible disillusion: there was a misunderstanding and a conflict about reasons for adoption. In the former case, the men was searching/asking for a cooperative and symmetric adoption (based on the same reasons he has: appeal, pleasure, affect,..) while the girl offered a commercial adoption. In the latter case, the men was searching for an exchange-money based adoption of his sexual desires, without affective or others "complications", while the girl was adopting for other reasons (attraction, sympathy, love, etc.). This kind of conflicts are relevant also among artificial intelligent agents. In fact, also among these agents there are several reasons for adopting each other, in particular in open environments (like the web) and in MASs with heterogeneous, self-interest agents. One should at least distinguish between "free" (no obligations) and "due" adoption and commitment.

3.1 Due adoptions: debts and roles Debts. First of all, it is very reasonable that in several MA systems8 the self-interested agents have to have a memory of previous interactions, and in particular have to maintain both a memory of the reliability and honesty of the other agents (their "reputation"), and a book/record of their credits (I did something for him, I'm waiting for some reciprocation) and debts (she did something for me, she is waiting for some reciprocation). Of course, this would also require a "social norm" (an obligation) or a built-in goal of reciprocation in the agents.

8 In our view, this is needed also in CSCW systems (supporting commitments and collaborations among human partners)

252

Now, suppose agents which have such a debts-credits information; they are prone to

anotherform of delegation conflict: agent A asks B to do action ct for reciprocation, while B is ready to help A but not as reciprocation. Either B believes that she has no debts with A, or she does not want to reciprocate. B might for example intent to help A for exchange (asking for some immediate reward or for some future reciprocation) or for sympathy, benevolence, etc. The conflict is not about helping or not, or about the amount of reward [Kra]: it's about the reasons for adopting. A delegates r to B provided that B adopts his goal for specific reasons (duty), while B adopts A's task, provided that A accepts her reasons (not a "due" adoption but a free one). 9 Duties (role). Second, suppose an organisation, i.e. a collective activity of a group of agents based on some previous commitment among them relative not just to a specific and extemporaneous task, but to a class of possible tasks (see the notion of Organisationai-Commitment). These commitments about classes of goals within the organisation plans, define, as we said the Role of the agent in that organisation. The existence of such an established Role, generic commitments, and pre-agreement, deeply changes the relation between the client and the contractor. In fact, if A asks B to do something (ct) that belongs to her Role, this is just an instantiation of what B already promised/agreed to do: it is her Role-duty. B has to do this "by Role", by contract. Also this source of duty rises possible conflicts of delegation. On the one side, B might disagree about ct belonging to her office: (Bel A (3pl I ((A~_.Al)^(B~Bi)^(x~rl)))) ^ (Bel B (-, =lpl I ((AEAI)^(B~EBI)^(-t~T0))). On the other side, the conflicts might give a different interpretation of the original contract (Organisational Commitment); the conflict might also be about reasons for adopting: A might search for a due, role-based adoption, while B is ready to help A but for other reasons. For example A gives a "command" to B, while B does not want to "obey" but just to friendly help or to exchange. Free adoption Of course, also within free adoption there might be conflicts about reasons: A wants that B helps him for cooperation (since he believes that they are cointerested in a common goal) while B asks for some reward. Moreover, it is not excluded the possibility of either implementing or let evolve in some population of artificial agents (robots, softbots) some form of "reciprocal altruism". In this case a new motive for help (a "terminal goal") is provided, and new conflicts are allowed: an agent might ask for an altruistic help while the other might supply an exchange or a reciprocating help; or viceversa.

3.2 Role conflicts Since along the paper we had to mention conflicts relative to the role of the agents (as a set of classes of pre-established tasks), let us shortly recapitulate, for sake of clarity, the main types of role conflicts. Given (Delegates A B x):

9 This conflict about titles for delegating and reasons for adopting, has not to be mixed up with possible conflicts about the amount of the debt/credit: "I acknowledge my debt, but what you ask me as reciprocation is more than you gave me!".

253

9 A's delegation does not match with B's role: either a sub-task or an over-task is requested: (Bel B (=lp21 ((A~--A2,)A(I~-Bz)A('I~'~--T2)) A ((dominates-at-level-k ct' ct) v (dominates-at-level-k ct ct')) v (no plan relations between ct ct')). 9 A's delegation does not fit with A's role: A shouldn't delegate/ask for that kind of task (for ex. it is not entitled to delegate x): (Bel B Vpi such that ((B~_B,) ^(x~-T,)) then (A~A~). 9 A and B disagree about x belonging to B's office: (Bel B Vpi such that (BEB i ) then (xvr where ((dominates-at-level-k ct' ct) v (dominates-at-level-k ct ct')) 9 A and B disagree about the reasons for B's adoption (whether it has to be a role adoption or not).

4 Conclusions We any We and

claimed that delegation and its related conflicts are the core of the interaction with kind of autonomous agent. presented our definition of delegation and adoption, a plan-based definition of tasks, of different kinds and levels of delegation and adoption. We attempted to show that: i) There are several levels of cooperation - more or less "deep" and helpful- and several levels of task delegation. ii) These levels are related to the hierarchical structure of plans or tasks. iii) There is a non-arbitrary correspondence between levels of delegation and levels of adoption; we called "contract" this relation. iv) A "deep" cooperation needs understanding of plans, goals, and interests of the other agent or user. v) There is a fundamental distinction between the delegation/adoption of: a domain task (practical action), or a planning or problem solving action or a control action. We illustrated the most interesting conflicts that arise when the provided help does not match the intended delegation (except conflicts relative to the control, and conflicts due to misunderstanding). We discussed also conflicts due to critical over help, and to the tutorial (paternalistic) attitude of the helper, which care of our interests against our requests. Finally, another source of conflict relative to the relationship between delegation and adoption has been examined: the conflict between the reasons and motives for the adoption as planned by the client and the reasons for adopting offered by the helper; a conflict that presupposes a record of debts and credits among the interacting agents. References [Bon] Bond, A.H., Commitments, Some DAI insigths from Symbolic Interactionist Sociology. In Proceedings of the 9A International AAA1 Workshop on Distributed Artificial Intelligence, .239261. Menlo Park, 1989. [Carl] Chu-Carroll J., Carberry, S., Conflict detection and resolution in collaborative planning, IJCAI-95 Workshop on Agent Theories, Architectures, and Languages, Montreal, Canada. 1995. [Car2] Chu-Carroll J., Carberry, S., A Plan-Based Model for Response Generation in Collaborative Task-Oriented Dialogues in Proceeedings of AAAI-94. 1994.

254

[Casl] Castelfranchi, C., Falcone, R., Towards a theory of single-agent into multi-agent plan transformation. The 3rd Pacific Rim International Conference on Artificial Intelligence, Beijing, China, 16-18 agosto 1994. [Cas2] Castelfranchi, C. & Falcone, R., Levels of help, levels of delegation and agent modeling. AAAI-96 Agent Modeling Workshop, 4 angustl996. [Cas3] Social Power: a missed point in DAI, MA and HCI. In Decentralized AL Y. Demazeau & J.P.Mueller (eds), 49-62. Amsterdam: Elsevier. [Cas4] Castelfranchi, C., Commitment: from intentions to groups and organizations. In Proceedings oflCMAS'96, S.Fruncisco, June 1996, AAAI-MIT Press. [Coh] Cohen, Ph. & Levesque, H., "Rational Interaction as the Basis for Communication". Technical Report, N~ CSLI, Stanford. 1987. [Con] Conte,R. & Castelfranehi, C. Cognitive and Social Action, UCL Press, London, 1995. [Fall Falcone, R., Castelfranchi, C., "On behalf of ..": levels of help, levels of delegation and their conflicts, 4th ModelAge Workshop: "Formal Model of Agents", Certosa di Pontignano (Siena), 1517 gennaio 1997. [Fik] Fikes, R. ED1982. A commitment-based framework for describing informal cooperative work. Cognitive Science, 6:331-347. [Goo] R. Goodwin, Formalizing Properties of Agents. Technical report, CMU-CS-93-159, 1993. [Gro] Grosz B., Kraus S., Collaborative plans for complex group action, Artificial Intelligence 86, pp. 269-357, 1996. [Had] A. Haddadi, Communication and Cooperation in Agent Systems. Springer-Verlag, 1996. [Kin] D.Kinny, M. Ljungberg, A.Rao, E.Sonenberg, G.Tidhar and E.Wemer, Planned Team Activity, in C. Castelfranchi and E. Wemer (Eds.) Artificial Social Systems (MAAMAW'92), Springer-Vedag, LNAI-830, 1994. [Kra] Kraus Sarit, Agents Contracting tasks in non-collaborative environments, in proceedings of AAAI'93, pp. 243-248, 1993. [Lev] Levesque HJ., P.R. Cohen, Nunes J.H.T. On acting together. In Proceedings of the 8th National Conference on Artificial Intelligence, 94-100. San Marco, Califomia: Kaufmann. 1990 [Lev] S.C. Levinson, "The Essential Inadequacies of Speech Act Models of Dialogue. In H. Parrett editor, Possibilities and Limitations of Pragmatics, 198 I. [Luc] M. Luck & M. d'Inverno, "A formal freamwork for agency and autonomy". In proceedings of the First International Conference on Multi-Agent Systems, pages 254-260. AAAI Press/MIT Press, 1995. [Mac] P. Maes, Situated agents can have goals. In P. Maes, editor, Designing Autonomous Agents, pp. 49-70. The MIT Press, 1990. [Ros] Rosenschein, J.S. and Zlotkin, G. Rules of encounters Designing conventions for automated negotiation among computers. Cambridge, MA: MIT Press. 1994. [Pol] Pollack, M., Plans as complex mental attitudes in Cohen, P.R., Morgan, J. and Pollack, M.E. (eds), Intentions in Communication, MIT press, USA, tap 77-103, 1990. [Sic] Sichman, J, R. Conte, C. Castelfranchi, Y. Demazeau. A social reasoning mechanism based on dependence networks. In Proceedings of the 11th ECAI, 1994. [Sin] M.P. Singh, Multiagent Systems: A Theoretical Framework for Intentions, Know-how, and Communications. Springer Verlag, LNCS, volume 799, 1995. [Wer] Werner, E., Cooperating agents: A unified theory of communication and social structure. In L.Gasser and M.N.Huhns, editors, Distribuited Artificial Intelligence: Volume II. Morgan Kaufmann Publishers, 1990.

Lecture Notes in Artificial Intelligence (LNAI)

Vol. 1076: N. Shadbolt, K. O'Hara, G. Schreiber (Eds.), Advances in Knowledge Acquisition. Proceedings, 1996. XII, 371 pages. 1996.

Vol. 1160: S. Arikawa, A.K. Sharma (Eds.), Algorithmic Learning Theory. Proceedings, 1996. XVII, 337 pages. 1996.

Vol. 1079: Z. W. Rag, M. Michalewicz (Eds.), Foundations of Intelligent Systems. Proceedings, 1996. XI, 664 pages. 1996.

Vol. 1168: 1. Smith, B. Faltings (Eds.), Advances in CaseBased Reasoning. Proceedings, 1996. IX, 531 pages. 1996.

Vol. 1081: G. McCalla (Ed.), Advances in Artificial Intelligence. Proceedings, 1996. XII, 459 pages. 1996. Vol. 1083: K. Sparek Jones, J.R. Galliers, Evaluating Natural Language Processing Systems. XV, 228 pages. 1996. Vol. 1085: D.M. Gabbay, H.J. Ohlbach (Eds.), Practical Reasoning. Proceedings, 1996. XV, 721 pages. 1996. Vol. 1087: C. Zhang, D. Lukose (Eds.), Distributed Artificial Intelligence. Proceedings, 1995. VIII, 232 pages. 1996. Vol. 1093: L. Dorst, M. van Lambatgen, F. Voorbraak (Eds.), Reasoning with Uncertainty in Robotics. Proceedings, 1995. VIII, 387 pages. 1996. Vol. 1095: W. McCune, R. Padmanabhan, Automated Deduction in Equational Logic and Cubic Curves. X, 231 pages. 1996. Vol. 1104: M.A. McRobbie, J.K. Slaney (Eds.), Automated Deduction -Cade- 13. Proceedings, 1996. XV, 764 pages. 1996. Vol. 1111 : J. J. Alferes, L. Moniz Pereira, Reasoning with Logic Programming. XX1, 326 pages. 1996. Vol. 1114: N. Foo, R. Goebel (Eds.), PRICAI'96: Topics in Artificial Intelligence. Proceedings, 1996. XXI, 658 pages. 1996. Vol. 1115: P.W. Eklund, G. Ellis, G. Mann (Eds.), Conceptual Structures: Knowledge Representation as lnterlingua. Proceedings, 1996. XIII, 321 pages. 1996. Vol. 1126: J.J. Alferes, L. Moniz Pereira, E. Orlowska (Eds.), Logics in Artificial Intelligence. Proceedings, 1996. IX, 417 pages. 1996. Vol. 1137: G. GiSrz, S. H011dobler (Eds.), KI-96: Advances in Artificial Intelligence. Proceedings, 1996. XI, 387 pages. 1996. Vol. 1147: L. Miclet, C. de la Higuera (Eds.), Grammatical Inference: Learning Syntax from Sentences. Proceedings, 1996. VIII, 327 pages, t996. Vol. 1152: T. Furuhashi, Y. Uchikawa (Eds.), Fuzzy Logic, Neural Networks, and Evolutionary Computation. Proceedings, 1995. VIII, 243 pages. 1996. Vol. 1159: D.L. Borges, C.A.A. Kaestner (Eds.), Advances in Artificial Intelligence. Proceedings, 1996. XI, 243 pages. 1996.

Vol. 117 l: A. Franz, Automatic Ambiguity Resolution in Natural Language Processing. XIX, 155 pages. 1996. Vol. t 177: J.P. Mtiller, The Design of Intelligent Agents. XV, 227 pages. 1996. Vol. 1187: K. Schleehta, Nonmonotonie Logics. IX, 243 pages. 1997. Vol. 1188: T.P. Martin, A.L. Raleseu (Eds.), Fuzzy Logic in Artificial Intelligence. Proceedings, 1995. VIII, 272 pages. 1997. Vol. 1193: J.P. Miiller, M.J. Wooldridge, N.R. Jennings (Eds.), Intelligent Agents III. XV, 401 pages. 1997. Vol. 1195: R. Trappl, P. Petta (Eds.), Creating Personalities for Synthetic Actors. VII, 251 pages. 1997. Vol. 1198: H. S. Nwana, N. Azarmi (Eds.), Software Agents and Soft Computing: Towards Enhancing Machine Intelligents. XIV, 298 pages. 1997. Vol. 1202: P. Kandzia, M. Klusch (Eds.), Cooperative Information Agents. Proceedings, 1997. IX, 287 pages. 1997. Vol. 1208: S. Ben-David (Ed.), Computational Learning Theory. Proceedings, 1997. VIII, 331 pages. 1997. Vol. 1209: L. Cavedon, A. Rat, W. Wobcke (Eds.), Intelligent Agent Systems. Proceedings, 1996. IX, 188 pages. 1997. Vol. 1211: E. Keravnou, C. Garbay, R. Baud, J. Wyatt (Eds.), Artificial Intelligence in Medicine. Proceedings, 1997. XIII, 526 pages, t997. Vol. 1216: J. Dix, L. Moniz Pereira, T.C. Przymusinski (Eds.), Non-Monotonic Extensions of Logic Programming. Proceedings, 1996. XI, 224 pages. 1997. Vol. 1221: G. WeiB (Ed.), Distributed Artificial Intelligence Meets Machine Learning. Proceedings, 1996. X, 294 pages. 1997. Vol. 1224: M. van Someren, G. Widmer (Eds.), Machine Learning: ECML-97. Proceedings, 1997. XI, 361 pages. 1997. Vol. 1227: D. Galmiche (Ed.), Automated Reasoning with Analytic Tableaux and Related Methods. Proceedings, 1997. XI, 373 pages. 1997. Vol. 1228: S.-H. Nienhuys-Cheng, R. de Wolf, Foundations of Inductive Logic Programming. XVII, 404 pages. 1997. Vol. 1237: M. Boman, W. Van de Velde (Eds.), MultiAgent Rationality. Proceedings, 1997. XII, 254 pages. 1997.

Lecture Notes in Computer Science

Vol. 1202: P. Kandzia, M. Klusch (Eds.), Cooperative Information Agents. Proceedings, 1997. IX, 287 pages. 1997. (Subseries LNAIL Vol. 1203: G. Bongiovanni, D.P. Bovet, G. Di Battista (Eds.), Algorithms and Complexity. Proceedings, 1997. VIII, 311 pages. 1997. Vol. 1204: H. M6ssenb6ck (Ed.), Modular Programming Languages. Proceedings, 1997. X, 379 pages. 1997. Vol. 1205: J. Toccaz, E. Grimson, R. M6sges (Eds.), CVRMed-MRCAS'97. Proceedings, 1997. XIX, 834 pages. 1997. Vol. 1206: J. Bigtin, G. Chollet, G. Borgefors (Eds.), Audio- and Video-based Biometric Person Authentication. Proceedings, 1997. XII, 450 pages. 1997. Vol. 1207: J. Gallagher (Ed.), Logic Program Synthesis and Transformation. Proceedings, 1996. VII, 325 pages. 1997. Vol. 1208: S. Ben-David (Ed.), Computational Learning Theory. Proceedings, 1997. VIII, 331 pages. 1997. (Subseries LNAI).

Vol. 1219: K. Rothermel, R. Popescu-Zeletin (Eds.), Mobile Agents. Proceedings, 1997. VIII, 223 pages. 1997. Vol. 1220: P. Brezany, Input/Output Intensive Massively Parallel Computing. XIV, 288 pages. 1997. Vol. 1221: G. Weig (Ed.), Distributed Artificial Intelligence Meets Machine Learning. Proceedings, 1996. X, 294 pages. 1997. (Subseries LNAI). Vol. 1222: J. Vitek, C. Tschudin (Eds.), Mobile Object Systems. Proceedings, 1996. X, 319 pages. 1997. Vol. 1223: M. Pelillo, E.R. Hancock (Eds.), Energy Minimization Methods in Computer Vision and Pattern Recognition. Proceedings, 1997. XII, 549 pages. 1997. Vol. 1224: M. van Someren, G. Widmer (Eds.), Machine Learning: ECML-97. Proceedings, 1997. XI, 361 pages. 1997. (Subseries LNAI). Vol. 1225: B. Hertzberger, P. Sloot (Eds.), HighPerformance Computing and Networking. Proceedings, 1997. XXI, 1066 pages. 1997. Vok 1226: B. Reusch (Ed.), Computational Intelligence. Proceedings, 1997. XIII, 609 pages. 1997.

Vol. 1209: L. Cavedon, A. Rao, W. Wobcke (Eds.), Intelligent Agent Systems. Proceedings, 1996. IX, 188 pages. 1997. (Subseries LNAI).

Vol. 1227: D. Galmiehe (Ed.), Automated Reasoning with Analytic Tableaux and Related Methods. Proceedings, 1997. XI, 373 pages. 1997. (Subseries LNAI).

Vol. 1210: P. de Groote, J.R. Hindley (Eds.), Typed Lambda Calculi and Applications. Proceedings, 1997. VIII, 405 pages. 1997.

Vol. 122g: S.-H. Nienhuys-Cheng, R. de Wolf, Foundations of Inductive Logic Programming. XVII, 404 pages. 1997. (Subseries LNAI).Vol. 1230: J. Duncan, G. Gindi (Eds.), Information Processing in Medical Imaging. Proceedings, 1997. XVI, 557 pages. 1997.

Vol. 1211: E. Keravnou, C. Garbay, R. Baud, J. Wyatt (Eds.), Artificial Intelligence in Medicine. Proceedings, 1997. XIII, 526 pages. 1997. (Subseries LNAI). Vol. 1212: J. P. Bowen, M.G. Hinchey, D. Till (Eds.), ZUM '97: The Z Formal Specification Notation. Proceedings, 1997. X, 435 pages. 1997. Vol. 1213: P. J. Angeline, R. G. Reynolds, J. R. McDonnell, R. Eberhart (Eds.), Evolutionary Programming VI. Proceedings, 1997. X, 457 pages. 1997. Vol. 1214: M. Bidoit, M. Dauchet (Eds.), TAPSOFT '97: Theory and Practice of Software Development. Proceedings, 1997. XV, 884 pages. 1997. Vol. 1215: J. M. L. M. Palma, J. Dongarra (Eds.), Vector and Parallel Processing - VECPAR'96. Proceedings, 1996. XI, 471 pages. 1997. Vol. 1216: J. Dix, L. Moniz Pereira, T.C. Przymusinski (Eds.), Non-Monotonic Extensions of Logic Programming. Proceedings, 1996. XI, 224 pages. 1997. (Subseries LNAI). Vol. 1217: E. Brinksma (Ed.), Tools and Algorithms for the Construction and Analysis of Systems. Proceedings, 1997. X, 433 pages. 1997. Vol. 1218: G. Paun, A. Salomaa (Eds.), New Trends in Formal Languages. IX, 465 pages. 1997.

Vol. 1231: M, Bertran, T. Rus (Eds.), TransformationBased Reactive Systems Development. Proceedings, 1997. XI, 431 pages. 1997. Vol. 1232: H. Comon (Ed.), Rewriting Techniques and Applications. Proceedings, 1997. XI, 339 pages. 1997. Vol. 1233: W. Fumy (Ed,), Advances in Cryptology - EUROCRYPT '97. Proceedings, 1997. XI, 509 pages. 1997. Vol 1234: S. Adian, A. Nerode (Eds.), Logical Foundations of Computer Science. Proceedings, 1997. IX, 431 pages. 1997. Vol. 1235: R. Conradi (Ed.), Software Configuration Management. Proceedings, 1997. VIII, 234 pages. 1997. Vol. 1237: M. Boman, W. Van de Velde (Eds.), MultiAgent Rationality. Proceedings, 1997. XII, 254 pages. 1997. (Subseries LNAI). Vol. 1240: J. Mira, R. Moreno-Diaz, J. Cabestany (Eds.), Biological and Artificial Computation: From Neuroscience to Technology. Proceedings, 1997. XXI, 1401 pages. 1997