Table Of ContentHANDBOOK 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
