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HANDBOOK OF DEFEASIBLE REASONING AND UNCERTAINTY MANAGEMENT SYSTEMS VOLUME6 HANDBOOK OF DEFEASIBLE REASONING AND UNCERTAINTY MANAGEMENT SYSTEMS EDITORS: DOV M. GABBAY King's College, London, U.K. PHILIPPE SMETS /RID/A - Universite Libre de Bruxelles, Belgium Volume 1: Quantified Representation of Uncertainty and Imprecision Edited by P. Smets Volume 2: Reasoning with Actual and Potential Contradictions Edited by P. Besnard and A. Hunter Volume 3: Belief Change Edited by D. Dubois and H. Prade Volume 4: Abductive Reasoning and Learning Edited by Dov M. Gabb ay and Rudolf Kruse Volume 5: Algorithms for Uncertainty and Defeasible Reasoning Edited by Jtirg Kohlas and Serafin Moral Volume 6: Dynamics and Management of Reasoning Processes Edited by J.-J. Ch. Meyer and J. Treur HANDBOOK OF DEFEASIBLE REASONING AND UNCERTAINTY MANAGEMENT SYSTEMS VOLUME6 DYNAMICS AND MANAGEMENT OF REASONING PROCESSES Volume Editors: J.-J. CH. MEYER Utrecht University, The Netherlands and J.TREUR Vrije Universiteit Amsterdam, The Netherlands SPRINGER-SCIENCE+BUSINESS MEDIA, B.V. A C.I.P. Catalogue record for this book is available from the Library of Congress. ISBN 978-90-481-5903-1 ISBN 978-94-017-1743-4 (eBook) ( DOI 10.1007/978-94-017-1743-4 Printed on acid-free paper All Rights Reserved © 2001 Springer Science+Business Media Dordrecht Originally published by Kluwer Academic Publishers in 2001 No part ofthe material protected by tllis copyright notice may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording or by any information storage and retrieval system, without written permission from the copyright owner. CONTENTS Preface vn Introduction 1 J.-J. Ch. Meyer and J. Treur Basic Concepts 7 J.-J. Ch. Meyer and J. Treur Temporal Semantics of Meta-Level Architectures for Dynamic 15 Control of Reasoning J.Treur Formal Semantics of 41 Temporal Epistemic Reflection W. van der Hoek, J.-J. Meyer and J. Treur Compositional Verification of Diagnostic Process Models 65 F. Cornelissen, C. M. Jonker and J. Treur Specification of Nonmonotonic Reasoning 83 J. Engelfriet and J. Treur An Interpretation of Default Logic in Minimal Temporal 105 Epistemic Logic J. Engelfriet and j. Treur The Dynamics of Default Reasoning 125 B. van Linder, W. van der Hoek and J.-J. Cb. Meyer Default Logic as Dynamic Doxastic Logic 159 K. Segerberg Temporalized Epistemic Default Logic 177 W. van der Hoek, J.-J. Ch. Meyer and J. Treur vi Meta-level Selection Techniques for the Control of Default 195 Reasoning V. Allis, Y.-H. Tan and J. Treur A New Semantics for Logic Programs 217 F. Lin and R. Reiter Context-Dependent Natural Deduction for Non-Monotonic 249 Reasoning P. Besnard and Y.-H. Tan Dynamic Normative Reasoning Under Uncertainty 267 L. van der Torre and Y.-H. Tan A Formal Analysis of Pro-activenes and Reactiveness in 299 Cooperative Information Gathering C. M. Jonker and J. Treur Modelling Internal Dynamic Behaviour of BDI Agents 339 F. M. T. Brazier, B. Dunin-Keplicz, J. Treur and R. Verbrugge Deliberate Evolution in Multi-Agent Systems 363 F. M. T. Brazier, C. M. Jonker, J. Treur and N.J. E. Wijngaards Index 381 PREFACE This volume, the 6th volume in the DRUMS Handbook series, is part of the after math of the successful ESPRIT project DRUMS (Defeasible Reasoning and Un certainty Management Systems) which took place in two stages from 1989-1996. In the second stage (1993-1996) a work package was introduced devoted to the topics Reasoning and Dynamics, covering both the topics of 'Dynamics of Rea soning', where reasoning is viewed as a process, and 'Reasoning about Dynamics', which must be understood as pertaining to how both designers of and agents within dynamic systems may reason about these systems. The present volume presents work done in this context. This work has an emphasis on modelling and formal techniques in the investigation of the topic "Reasoning and Dynamics", but it is not mere theory that occupied us. Rather research was aimed at bridging the gap between theory and practice. Therefore also real-life applications of the modelling techniques were considered, and we hope this also shows in this volume, which is focused on the dynamics of reasoning processes. In order to give the book a broader perspective, we have invited a number of well-known researchers outside the project but working on similar topics to contribute as well. We have very pleasant recollections of the project, with its lively workshops and other meetings, with the many sites and researchers involved, both within and outside our own work package. We thank everyone involved, in particular the authors of the papers of this vol ume, and the series editors Dov Gabbay and Philippe Smets for their encourage ment, the latter having also been the overall project leader with a good taste for quality of both science and life (including food). Last, but by no means least, we thank Jane Spurr for the splendid job she did, helping us to get this volume together, and in particular for her meticulous translation of Word files into Latex. John-Jules Meyer Jan Treur JOHN-JULES CH. MEYER AND JAN TREUR INTRODUCTION AGENTS AND REASONING A currently popular definition of artificial intelligence (AI) is: "the study of agents that exist in an environment and perceive and act". Agents, often referred to as intelligent agents, are (hardware or software) entities that act on the basis of a "mental state". They possess both informational and motivational attitudes, which means that while performing their actions they are guided by their knowledge and beliefs as well as their desires, intentions and goals, and, moreover, they are able to modify their knowledge, intentions, etc. in the process of acting as well. Clearly the description of agent behaviour involves reasoning about the dynam ics of acting, and if agents are supposed to be deliberative, they should also them selves be able to do so. Furthermore, since the actions of agents may-apart from actions that change the external world directly-also include reasoning as a spe cial kind of mental action (for example, performing some belief-revising action or an action comprising of reasoning by default), it may be clear that in the context of agent systems the dynamics of reasoning and reasoning about dynamics go hand in hand. THE APPLICATION-FOUNDATION GAP IN AI One of the recognized problems in AI is the gap between applications and formal foundations. This volume, as well as the following one in the DRUMS series, does not present the final solution to this problem, but at least does an attempt to reduce the gap by bringing together state-of-the-art material from both sides and by clarifying their mutual relation. One of the areas where the application-foundation gap is deeply felt is the area of reasoning processes as they occur within agents. In practical applications complex (expert) reasoning processes are modelled, both in the context of knowledge-based systems for complex tasks and in the con text of intelligent agents. In such practical reasoning processes it is often im portant, given the aims of the reasoning, to minimize the amount of information needed, and thus make (rational) decisions about the focuses of the reasoning pro cess. Therefore models of such practical reasoning processes, have to address complex dynamics, including automated solutions for- dynamically focusing of the reasoning on certain goals, - dynamically selecting a focused set of assump tions as premises,- adding (default) assumptions during the reasoning process, -dynamically select (intermediate) conclusions of the reasoning to be verified, D.M. Gabbay and Ph. Smets (eds.), Handbook of Defeasible Reasoning and Uncertainty Management Systems, Vol. 6, 1-6. © 2001 Kluwer Academic Publishers. 2 JOHN-JULES CH. MEYER AND JAN TREUR - verify these conclusions by observation or communication, - deciding to re tract certain assumptions during the reasoning process. For example, in models for diagnostic reasoning processes almost all of these aspects of dynamics often occur. But also for information gathering agents these dynamic aspects play an important role. From the side of foundations of reasoning processes, since 1980 a lot of work has been done, in particular on logics for nonmonotonic reasoning. However, very few contributions have addressed the dynamics of these reasoning processes, and no contribution at all has covered all aspects listed above. More recently, the area of agent models has been addressed. For example, in order to formalize agents with beliefs, desires and intentions (BDI-agents), spe cific logics have been developed. However, the architectures for BDI-agents that have been developed for practical applications are not related in a clear manner to these logics. In particular, although some degree of dynamics is captured by them, BDI-logics lack a clear relationship to the internal dynamics of the reasoning in applications of BDI-agents: e.g., the revision of beliefs, desires and intentions under the appropriate circumstances, have not been sufficiently addressed in the foundational approaches. In applications of BDI-agents, this rather complex issue of revision of beliefs, desires and/or intentions has to be addressed, and actually has been addressed; e.g., when to revise only an intention and not the underlying desire, when to revise both, when to revise a desire and still keep a (committed) intention which was based on the desire? In a comparable manner, existing theories of diagnosis from first principles ad dress diagnosis from a static perspective, but do not cover process-oriented ques tions such as, on which hypothesis should the process be focused, and which ob servations should be done at which moment. Similarly, most contributions in the area of logics for nonmonotonic reasoning only offer a formal description of the possible conclusions of such a reasoning process, and not of the dynamics of the reasoning process itself. In conclusion, as far as the relation to applications is concerned, within these foundational approaches especially the dynamic aspects of the agent's internal (reasoning)processes are often not, or at least not fully covered. The areas of models for reasoning patterns and agents, show a gap between applications and foundations in which the lack of adequate dynamics incorporated in the founda tions plays an important role. For this reason in this volume the main theme is dynamics of reasoning processes. In the next volume, also dynamics of the exter nal world is addressed. Agents often reason about both types of dynamics. FORMAL FOUNDATIONS In this book we will consider various formal means regarding the topics reasoning and dynamics, mostly from an intelligent agent perspective. By formal means we mean formal logics I calculi as well as formal specification languages together with their formal semantics. We will see how in particular temporal and dynamic INTRODUCTION 3 logic I semantics are useful means to perform this formal analysis. (Since these frameworks play such a prominent role in this book, we have included a separate 'Basic Concepts' chapter following this introduction, where these are explained succinctly.) Formal foundations of reasoning models are of importance for different rea sons. First, by defining semantics in a formal manner, a precise and unambiguous meaning of the syntactical constructs is obtained, which may help designers. This requires that designers are familiar with the formal techniques used to define such formal semantics. Unfortunately, this requirement is often not fulfilled for applica tion developers in practice, and there is no reason to expect that this will change in the short term. However, more realistically, those who develop a modelling tech nique often have more knowledge of formal methods. Therefore they can benefit a lot from knowledge of formal foundations during development of their modelling technique, and use that also as a basis to informally or semi-formally describe the semantics for others (application developers using the modelling technique) with a less formal background. Secondly, formal foundations are especially important to obtain the possibility of verification of a design or verification of requirements. Verification is usually a rather technical and tedious matter, only feasible for specialists ("verification engineers"). They need to know about the formal foundations, including formal semantics and proof systems. DYNAMICS OF REASONING In this volume the emphasis is on the investigation of the dynamics of reasoning processes within an agent. Reasoning takes (place in) time, so, for example, it is natural to view the behaviour of reasoning processes from a temporal perspective, and consider a temporal semantics of these processes. For example, in meta-level architectures the reasoning may switch from object level to meta-level, and one may describe such behaviour by means of temporal models. Furthermore, to rea son about this temporal behaviour it is very natural to employ temporal logic of some kind. But, of course, taking a temporal stance is possible for the analysis of any reasoning process or system, like (multi-)agent systems. A large variety of reasoning patterns is presented in this volume: from dy namic generation and retraction of assumptions and dynamic control of reasoning in meta-level architectures to diagnostic reasoning processes and non-monotonic reasoning processes. All these reasoning processes have in common that they are defeasible in some sense, that is, it is possible (or perhaps even typical) that at a certain stage of the reasoning process certain conclusions may be (tentatively) arrived at, which possibly should be abandoned at a later stage. This type of rea soning is generally called defeasible reasoning. In the first two papers meta-level reasoning is exploited to focus the object level reasoning process, either by dy namically focusing on a set of goals for the object level reasoning, or on a set of