Foundations of Rational Agency

302 Pages·1999·0.65 MB·English

by  Anand S. Rao

Foundations of Rational Agency
APPLIED LOGIC SERIES
VOLUME14
Managing Editor
Dov M. Gabbay, Department of Computer Science, King's College, London, U.K.
Co-Editor
Jon Barwise, Department of Philosophy, Indiana University, Bloomington, IN,
U.S.A.
Editorial Assistant
Jane Spurr, Department ofC omputer Science, King's College, London, U.K.
SCOPE OF THE SERIES
Logic is applied in an increasingly wide variety of disciplines, from the traditional
subjects of philosophy and mathematics to the more recent disciplines of cognitive
science, computer science, artificial intelligence, and linguistics, leading to new
vigor in this ancient subject. Kluwer, through its Applied Logic Series, seeks to
provide a home for outstanding books and research monographs in applied logic,
and in doing so demonstrates the underlying unity and applicability of logic.
The titles published in this series are listed at the end oft his volume.
Foundations of
Rational Agency
edited by
MICHAEL WOOLDRIDGE
Queen Mary and Westfield College,
London, United Kingdom
and
ANANDRAO
Mitchell Madison Group,
Melbourne, Australia
SPRINGER-SCIENCE+BUSINESS MEDIA, B.V.
A C.I.P. Catalogue record for this book is available from the Library of Congress.
ISBN 978-90-481-5177-6 ISBN 978-94-015-9204-8 (eBook)
DOI 10.1007/978-94-015-9204-8
Logo design by L. Rivlin
Printed on acid-free paper
AII Rights Reserved
© 1999 Springer Science+Business Media Dordrecht
Originally published by Kluwer Academic Publishers in 1999
Softcover reprint ofthe hardcover Ist edition 1999
No part of the material protected by this 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
EDITORIAL PREFACE
The Editors are very pleased to have the present collection on agents.
Agent theory is an important field at the crossroads of philosophy, artificial
intelligence, computer science, practical reasoning and logic.
All of the above disciplies are trying to model and understand human
agents, each for their own historical reasons, some dating back to Plato
and Aristotle.
This area is, therefore, a meeting ground for future cooperation between
all these communities. In fact, the state of the research and application is
such that cooperation is necessary, in my opinion, for the successful future
development of each strand.
Let us hope that the publication of this book, through the agency of
our series, will serve to advance cooperation between all agent resarch
communities.
The Editors
CONTENTS
A. RAO AND M. WOOLDRIDGE
Foundations of Rational Agency
S. RUSSELL
Rationality and Intelligence 11
A. SLOMAN
What Sort of Architecture is Required for a Human-like Agent? 35
J. L. POLLOCK
Planning Agents 53
P. J. GMYTRASIEWICZ
Toward Rational Interactions in Multiagent Domains 81
M.P. SINGH
Know-How 105
W. VANDER HOEK, B. VAN LINDER AND J-J.CH. MEYER
An Integrated Modal Approach to Rational Agents 133
D.R. TRAUM
Speech Acts for Dialogue Agents 169
A. HADDAD!
Communication among Rational Agents 203
B. J. GROSZ AND S. KRAUS
The Evolution of SharedPlans 227
S.R. THOMAS
A Survey of Agent-Oriented Programming 263
Y. LESPERANCE, H. J. LEVESQUE, AND R. REITER
A Situation Calculus Approach to Modeling and Programming Agents 275
A. RAO AND M. WOOLDRIDGE
FOUNDATIONS OF RATIONAL AGENCY
1 AGENTS AND RATIONAL AGENTS
The word agent is widely used in computer science these days. It can mean any
thing from a few lines of code that gets executed automatically when a certain
condition is satisfied (e.g., a daemon that gets scheduled by the Operating System
every day at midnight), to a more sophisticated program that can reason about its
own behaviour and achieve long-term goals (e.g., an autonomous intelligent vehi
cle on Mars) [13]. In addition to the simplicity or sophistication of these software
entities, agents are also characterised into different types based on their function
ality. Interface agents, mobile agents, believable agents, reactive agents, learning
agents, and emergent agents are just a few of these types. The level of autonomy
(from being completely autonomous to being intelligent assistants to humans) and
the level of their interaction with physical entities (from being totally in the soft
ware environment-softbots -to being intimately connected with their physical
environment - robots) are also used to characterise agents.
The aim of this book is not to cover this entire spectrum of agents, but rather
to examine one particular type of agents- the so called rational agents [19].
Rationality as a concept has been around for a long time even before the advent
of computers. Rational agents are software entities that perceive their physical or
software environment through appropriate sensors; have a model and can reason
about the environment that they inhabit; and based on their own mental state take
actions that change their environment. The key aspects of rationality include:
• balancing reactive and pro-active behaviour;
• balancing perception, deliberation, and action, especially when there are
limited resources;
• balancing self-interest and community-interest.
It is these types of agents that will be of primary interest to us. Such rational agents
have been investigated in a number of application domains, including intelligent
information retrieval, modeling the tactics of groups of pilots in combat scenarios,
aiding air-traffic controllers in the optimal sequencing and monitoring of aircraft,
and diagnosis of faults in the Space Shuttle [13, 11]. Although, we will not be
looking at these applications in detail, the foundations and theories discussed in
this book form an integral part of developing such systems.
M. Wooldridge and A. Roo (eds.), Foundations ofR ational Agency, 1-10.
@ 1999 Kluwer Academic Publishers.
2 A. RAO AND M. WOOLDRIDGE
2 HISTORY
Like most sub-fields of Artificial Intelligence (AI), the study of rational agents
is a truly inter-disciplinary area. One can trace the roots of rational agents to
philosophy, computer science, economics, sociology, and psychology. We explore
some of these fields and their contributions to the understanding of rationality and
the science of building rational agents below.
2.1 Practical Reasoning
Practical reasoning is a branch of philosophy that deals with how a rational agent
decides to act based on its internal mental state. Some of the major concerns of
this branch of philosophy include: What are the primitive mental attitudes? How
do these mental attitudes translate into actions by an agent? What are the processes
involved in the cycle of perception-thinking-action? How do these attitudes and
processes change under limited resources?
Arguably, the work from this branch of philosophy that has most influenced the
notion of building rational agents is the seminal work ofBratman [3]. In his 1987
book Intentions, Plans, and Practical Reason, he argues for the primacy of inten
tions in human practical reasoning, and also argues convincingly that intentions
are irreducible to other mental states - in particular, desires. He thereby lays the
groundwork for the attitudes of beliefs, desires, and intentions, as three separate
and irreducible mental states. Beliefs capture the informational state of an agent.
Unlike desires and intentions, beliefs do not play any motivational role within an
agent. Desires and intentions both play a motivational role and are referred to as
pro-attitudes. However, while desires merely influence the actions of an agent, in
tentions control the actions that are taken by an agent. Hence, desires are referred
to as conduct-influencing pro-attitudes and intentions as conduct-controlling pro
attitudes.
Bratman's notion of intention is closely tied with the notion of plans. Inten
tions are the building blocks of larger plans. Plans are used in two contexts - as
abstract structures or recipes, and as mental states that are intertwined with one's
beliefs and desires. It is in the latter sense that intentions are part of the adopted
or committed plans of an agent. Bratman 's contribution to rational agency extends
from the static analysis of these mental attitudes and their interrelationships to the
processes of means-end-reasoning, deliberation, and reconsideration that continu
ously shape and modify these attitudes [4 ].
While the three mental attitudes of beliefs, desires, and intentions may be a
good starting point, and arguably necessary mental attitudes for the study of ratio
nal agents, it is by no means a sufficient or complete analysis of mental attitudes
of rational agents. A number of researchers have examined modeling other related
attitudes, such as obligation, commitment, capability, wishes, wants, power, influ
ence, and countless others [ 19]. Mental attitudes are not restricted only to single
agents, but can also be ascribed to groups or "societies" of agents. Mutual belief,
FOUNDATIONS OF RATIONAL AGENCY 3
common knowledge, joint intention, joint goal, and social commitment are just
some of the multi-agent mental attitudes that have been studied (see e.g., [14]).
Rational agents are not solitary entities living in an uninhabited static envi
ronment; they are embedded in a continuously changing environment and have
to constantly interact with other agents. Hence, communication with other agents
and interaction with the environment are key concerns within this field. The theory
of speech acts and its extensions, as well as philosophical theories of action, time,
and change have greatly influenced the study of rational agents (see, e.g., [6]).
2.2 Philosophical Logic
While philosophical theories are based on arguments, refutations, and informal
claims, philosophical logic - a synthesis of philosophy and mathematical logic
- takes these claims to the next level in formalizing the assumptions, intuitions,
and claims as discussed in the philosophical theories. A variety of logics based on
variants of classical mathematical logics and modal logics have been used as the
basis of formalizing the mental attitudes of agents and their interaction with other
agents and the environment.
Modal and temporal logics [7] have been extensively used to capture the men
tal attitudes discussed earlier and analyze their properties. The possible-worlds
semantics of such logics provide an useful abstraction with which to analyze the
behaviour of rational agents. Action and program logics [ 12] have been used to
capture the connection between mental attitudes and action have also influenced
the operational semantics of rational agents.
2.3 Decision Theory and Game Theory
While philosophical logic is concerned with capturing the relationships between
different attitudes, decision theory and game theory - branches of mathematics
and economics-provide a quantitative account of rationality. While decision the
ory considered individual decision-making under uncertainty and individual pref
erence, game theory parameterised decision-making with respect to the decisions
of other agents in the environment [ 1].
2.4 Artificial Intelligence
Philosophical logic and classical decision theory contribute a great deal to the un
derstanding of the general principles of rational agency. However, they invari
ably consider idealized conditions in which computational considerations are not
an issue. Artificial Intelligence is, as much as anything, about building rational
agents [ 18], and as such it is often the test bed for experimenting with new philo
sophical, psychological, and sociological theories.
Bratman 's philosophical theory of intentions and the role of plans in practical
reasoning came at a time when AI researchers in planning were getting disillu-
4 A. RAO AND M. WOOLDRIDGE
sioned with the state-of-the art classical planning techniques (see, e.g., [9]). Search
based classical planning techniques, which worked in toy problems, did not scale
well to real-world problems. In particular, the lack of explicit modeling of other
agents and their actions, the failure to account for a continuously changing environ
ment, and the limited resources available to real computational agents, were some
of the more serious drawbacks. As a result, Bratman's theory of intentions, which
explicitly addressed at least two of these three drawbacks, was embraced warmly
by a group of planning researchers within AI. Planning that used pre-compiled
plans or recipes rather than doing first-principles classical planning came to be
known as reactive planning [10].
These developments led to one of the early implementations of rational agents
-the Procedural Reasoning System (PRS) [10]. This system explicitly modeled
the attitudes of beliefs, goals, and intentions. The system was programmed by
specifying the plans that would achieve certain goals, under certain belief condi
tions. The agent dynamically adopted these hierarchical plans based on its mental
state (i.e., its current beliefs, goals, and intentions).
Around the same time, a number of researchers in AI planning also started ad
dressing the drawbacks of classical planning using decision theory. A number of
decision-theoretic and utilitarian reasoning mechanisms were developed for plan
ning with limited resources in uncertain environments [2].
Both these streams influenced the architectures that emerged for rational agents.
The above architectures and techniques were also enhanced to account for com
munication, coordination, synchronization, and joint decision-making with other
rational agents.
2. 5 Software Engineering
As ideas mature from philosophy to implemented systems it gains widespread cur
rency amongst a larger group of practitioners requiring easy-to-use tools, efficient
languages, and well-defined analysis and design methodologies. This is when the
field makes a transition from AI to a software engineering discipline. Over the
past couple of years the field of rational agents is slowly making this transition.
This can be seen by the increasing emphasis on architectures, language constructs,
reliability, and usability concerns within the rational agent community.
3 RATIONAL AGENTS: CURRENT RESEARCH PARADIGMS
3.1 Foundations
Rationality and Intelligence by Stuart Russell sets the foundation for this collec
tion by examining the very definition of rational agency. He proposes four formal
definitions of rationality and discusses the merits of each definition. The last of
these four formal definitions - bounded optimality - grounds the notion of ra-