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978-0-521-51900-7 - Artificial Intelligence: Foundations of Computational Agents
David L. Poole and Alan K. Mackworth
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Artificial Intelligence
Foundations of Computational Agents
Artificial Intelligence: Foundations of Computational Agents is about the science of
artificial intelligence (AI). It presents AI as the study of the design of intelligent computational agents. The book is structured as a textbook, but it is accessible to a wide
audience of professionals and researchers.
The past decades have witnessed the emergence of AI as a serious science and
engineering discipline. This book provides the first accessible synthesis of the field
aimed at undergraduate and graduate students. It provides a coherent vision of the
foundations of the field as it is today, in terms of a multidimensional design space
that has been partially explored. As with any science worth its salt, AI has a coherent,
formal theory and a rambunctious experimental wing. The book balances theory and
experiment, showing how to link them intimately together. It develops the science of
AI together with its engineering applications.
David L. Poole is Professor of Computer Science at the University of British Columbia.
He is a coauthor of Computational Intelligence: A Logical Approach (1998), cochair of the
Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10), and coeditor
of the Proceedings of the Tenth Conference in Uncertainty in Artificial Intelligence (1994).
Poole is a former associate editor of the Journal of Artificial Intelligence Research. He is
an associate editor of Artificial Intelligence and on the editorial boards of AI Magazine
and AAAI Press. He is the secretary of the Association for Uncertainty in Artificial
Intelligence and is a Fellow of the Association for the Advancement of Artificial
Intelligence.
Alan K. Mackworth is Professor of Computer Science and Canada Research Chair
in Artificial Intelligence at the University of British Columbia. He has authored more
than 100 papers and coauthored the text Computational Intelligence: A Logical Approach.
He was President and Trustee of International Joint Conferences on AI (IJCAI) Inc.
Mackworth was vice president and president of the Canadian Society for Computational Studies of Intelligence (CSCSI). He has served as president of the AAAI.
He also served as the founding director of the UBC Laboratory for Computational
Intelligence. He is a Fellow of the Canadian Institute for Advanced Research, AAAI,
and the Royal Society of Canada.
© in this web service Cambridge University Press
www.cambridge.org
Cambridge University Press
978-0-521-51900-7 - Artificial Intelligence: Foundations of Computational Agents
David L. Poole and Alan K. Mackworth
Frontmatter
More information
Artificial Intelligence
Foundations of Computational Agents
David L. Poole
University of British Columbia
Alan K. Mackworth
University of British Columbia
© in this web service Cambridge University Press
www.cambridge.org
Cambridge University Press
978-0-521-51900-7 - Artificial Intelligence: Foundations of Computational Agents
David L. Poole and Alan K. Mackworth
Frontmatter
More information
CAMBRIDGE UNIVERSITY PRESS
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São Paulo, Delhi, Dubai, Tokyo
Cambridge University Press
32 Avenue of the Americas, New York, NY 10013-2473, USA
www.cambridge.org
Information on this title: www.cambridge.org/9780521519007
C David L. Poole and Alan K. Mackworth 2010
This publication is in copyright. Subject to statutory exception
and to the provisions of relevant collective licensing agreements,
no reproduction of any part may take place without the written
permission of Cambridge University Press.
First published 2010
Printed in the United States of America
A catalog record for this publication is available from the British Library.
Library of Congress Cataloging in Publication data
Poole, David L. (David Lynton), 1958–
Artificial intelligence : foundations of computational agents / David L. Poole, Alan K. Mackworth.
p. cm.
Includes bibliographical references and index.
ISBN 978-0-521-51900-7 (hardback)
1. Computational intelligence – Textbooks. 2. Artificial intelligence – Textbooks. I. Mackworth, Alan K.
II. Title.
Q342.P66 2010
006.3 – dc22
2009039895
ISBN 978-0-521-51900-7 Hardback
Cambridge University Press has no responsibility for the persistence or
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this publication and does not guarantee that any content on such Web sites is,
or will remain, accurate or appropriate.
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Cambridge University Press
978-0-521-51900-7 - Artificial Intelligence: Foundations of Computational Agents
David L. Poole and Alan K. Mackworth
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To our families for their love, support, and patience
Jennifer, Alexandra, and Shannon
Marian and Bryn
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Cambridge University Press
978-0-521-51900-7 - Artificial Intelligence: Foundations of Computational Agents
David L. Poole and Alan K. Mackworth
Frontmatter
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Contents
Preface
xiii
I Agents in the World: What Are Agents and How Can They Be
Built?
1 Artificial Intelligence and Agents
1.1
What Is Artificial Intelligence? .
1.2
A Brief History of AI . . . . . . .
1.3
Agents Situated in Environments
1.4
Knowledge Representation . . .
1.5
Dimensions of Complexity . . . .
1.6
Prototypical Applications . . . .
1.7
Overview of the Book . . . . . .
1.8
Review . . . . . . . . . . . . . . .
1.9
References and Further Reading
1.10 Exercises . . . . . . . . . . . . . .
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Agent Systems . . . . . . . . . . . . . . .
2.3
Hierarchical Control . . . . . . . . . . .
2.4
Embedded and Simulated Agents . . .
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Acting with Reasoning . . . . . . . . . .
2.6
Review . . . . . . . . . . . . . . . . . . .
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© in this web service Cambridge University Press
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Cambridge University Press
978-0-521-51900-7 - Artificial Intelligence: Foundations of Computational Agents
David L. Poole and Alan K. Mackworth
Frontmatter
More information
viii
Contents
2.7
2.8
References and Further Reading . . . . . . . . . . . . . . . . .
Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
II Representing and Reasoning
3 States and Searching
3.1
Problem Solving as Search . . . .
3.2
State Spaces . . . . . . . . . . . .
3.3
Graph Searching . . . . . . . . .
3.4
A Generic Searching Algorithm .
3.5
Uninformed Search Strategies . .
3.6
Heuristic Search . . . . . . . . . .
3.7
More Sophisticated Search . . . .
3.8
Review . . . . . . . . . . . . . . .
3.9
References and Further Reading
3.10 Exercises . . . . . . . . . . . . . .
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4 Features and Constraints
4.1
Features and States . . . . . . . . . . . . . .
4.2
Possible Worlds, Variables, and Constraints
4.3
Generate-and-Test Algorithms . . . . . . .
4.4
Solving CSPs Using Search . . . . . . . . .
4.5
Consistency Algorithms . . . . . . . . . . .
4.6
Domain Splitting . . . . . . . . . . . . . . .
4.7
Variable Elimination . . . . . . . . . . . . .
4.8
Local Search . . . . . . . . . . . . . . . . . .
4.9
Population-Based Methods . . . . . . . . .
4.10 Optimization . . . . . . . . . . . . . . . . .
4.11 Review . . . . . . . . . . . . . . . . . . . . .
4.12 References and Further Reading . . . . . .
4.13 Exercises . . . . . . . . . . . . . . . . . . . .
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5 Propositions and Inference
5.1
Propositions . . . . . . . . . . . . .
5.2
Propositional Definite Clauses . .
5.3
Knowledge Representation Issues
5.4
Proving by Contradictions . . . . .
5.5
Complete Knowledge Assumption
5.6
Abduction . . . . . . . . . . . . . .
5.7
Causal Models . . . . . . . . . . . .
5.8
Review . . . . . . . . . . . . . . . .
5.9
References and Further Reading .
5.10 Exercises . . . . . . . . . . . . . . .
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Cambridge University Press
978-0-521-51900-7 - Artificial Intelligence: Foundations of Computational Agents
David L. Poole and Alan K. Mackworth
Frontmatter
More information
ix
Contents
6 Reasoning Under Uncertainty
6.1
Probability . . . . . . . . . . . . .
6.2
Independence . . . . . . . . . . .
6.3
Belief Networks . . . . . . . . . .
6.4
Probabilistic Inference . . . . . .
6.5
Probability and Time . . . . . . .
6.6
Review . . . . . . . . . . . . . . .
6.7
References and Further Reading
6.8
Exercises . . . . . . . . . . . . . .
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III Learning and Planning
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7 Learning: Overview and Supervised Learning
7.1
Learning Issues . . . . . . . . . . . . . . . .
7.2
Supervised Learning . . . . . . . . . . . . .
7.3
Basic Models for Supervised Learning . . .
7.4
Composite Models . . . . . . . . . . . . . .
7.5
Avoiding Overfitting . . . . . . . . . . . . .
7.6
Case-Based Reasoning . . . . . . . . . . . .
7.7
Learning as Refining the Hypothesis Space
7.8
Bayesian Learning . . . . . . . . . . . . . .
7.9
Review . . . . . . . . . . . . . . . . . . . . .
7.10 References and Further Reading . . . . . .
7.11 Exercises . . . . . . . . . . . . . . . . . . . .
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8 Planning with Certainty
8.1
Representing States, Actions, and Goals
8.2
Forward Planning . . . . . . . . . . . . .
8.3
Regression Planning . . . . . . . . . . .
8.4
Planning as a CSP . . . . . . . . . . . . .
8.5
Partial-Order Planning . . . . . . . . . .
8.6
Review . . . . . . . . . . . . . . . . . . .
8.7
References and Further Reading . . . .
8.8
Exercises . . . . . . . . . . . . . . . . . .
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9 Planning Under Uncertainty
9.1
Preferences and Utility . . . . . . . . .
9.2
One-Off Decisions . . . . . . . . . . . .
9.3
Sequential Decisions . . . . . . . . . .
9.4
The Value of Information and Control
9.5
Decision Processes . . . . . . . . . . .
9.6
Review . . . . . . . . . . . . . . . . . .
9.7
References and Further Reading . . .
9.8
Exercises . . . . . . . . . . . . . . . . .
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Cambridge University Press
978-0-521-51900-7 - Artificial Intelligence: Foundations of Computational Agents
David L. Poole and Alan K. Mackworth
Frontmatter
More information
x
Contents
10 Multiagent Systems
10.1 Multiagent Framework . . . . . . . . . . . . . .
10.2 Representations of Games . . . . . . . . . . . .
10.3 Computing Strategies with Perfect Information
10.4 Partially Observable Multiagent Reasoning . .
10.5 Group Decision Making . . . . . . . . . . . . .
10.6 Mechanism Design . . . . . . . . . . . . . . . .
10.7 Review . . . . . . . . . . . . . . . . . . . . . . .
10.8 References and Further Reading . . . . . . . .
10.9 Exercises . . . . . . . . . . . . . . . . . . . . . .
11 Beyond Supervised Learning
11.1 Clustering . . . . . . . . . . . . .
11.2 Learning Belief Networks . . . .
11.3 Reinforcement Learning . . . . .
11.4 Review . . . . . . . . . . . . . . .
11.5 References and Further Reading
11.6 Exercises . . . . . . . . . . . . . .
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458
463
485
486
486
IV Reasoning About Individuals and Relations
12 Individuals and Relations
12.1 Exploiting Structure Beyond Features . . . . .
12.2 Symbols and Semantics . . . . . . . . . . . . .
12.3 Datalog: A Relational Rule Language . . . . .
12.4 Proofs and Substitutions . . . . . . . . . . . . .
12.5 Function Symbols . . . . . . . . . . . . . . . . .
12.6 Applications in Natural Language Processing .
12.7 Equality . . . . . . . . . . . . . . . . . . . . . .
12.8 Complete Knowledge Assumption . . . . . . .
12.9 Review . . . . . . . . . . . . . . . . . . . . . . .
12.10 References and Further Reading . . . . . . . .
12.11 Exercises . . . . . . . . . . . . . . . . . . . . . .
13 Ontologies and Knowledge-Based Systems
13.1 Knowledge Sharing . . . . . . . . . . . . . . . .
13.2 Flexible Representations . . . . . . . . . . . . .
13.3 Ontologies and Knowledge Sharing . . . . . .
13.4 Querying Users and Other Knowledge Sources
13.5 Implementing Knowledge-Based Systems . . .
13.6 Review . . . . . . . . . . . . . . . . . . . . . . .
13.7 References and Further Reading . . . . . . . .
13.8 Exercises . . . . . . . . . . . . . . . . . . . . . .
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549
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David L. Poole and Alan K. Mackworth
Frontmatter
More information
xi
Contents
14 Relational Planning, Learning, and Probabilistic Reasoning
14.1 Planning with Individuals and Relations . . . . . . . .
14.2 Learning with Individuals and Relations . . . . . . . .
14.3 Probabilistic Relational Models . . . . . . . . . . . . . .
14.4 Review . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14.5 References and Further Reading . . . . . . . . . . . . .
14.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . .
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597
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V The Big Picture
623
15 Retrospect and Prospect
15.1 Dimensions of Complexity Revisited . . . . . . . . . . . . . . .
15.2 Social and Ethical Consequences . . . . . . . . . . . . . . . . .
15.3 References and Further Reading . . . . . . . . . . . . . . . . .
625
625
629
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A Mathematical Preliminaries and Notation
A.1
Discrete Mathematics . . . . . . . . . . . . . . . . . . . . . . . .
A.2
Functions, Factors, and Arrays . . . . . . . . . . . . . . . . . .
A.3
Relations and the Relational Algebra . . . . . . . . . . . . . . .
633
633
634
635
Bibliography
637
Index
653
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Cambridge University Press
978-0-521-51900-7 - Artificial Intelligence: Foundations of Computational Agents
David L. Poole and Alan K. Mackworth
Frontmatter
More information
Preface
Artificial Intelligence: Foundations of Computational Agents is a book about the
science of artificial intelligence (AI). The view we take is that AI is the study
of the design of intelligent computational agents. The book is structured as a
textbook, but it is designed to be accessible to a wide audience.
We wrote this book because we are excited about the emergence of AI as an
integrated science. As with any science worth its salt, AI has a coherent, formal
theory and a rambunctious experimental wing. Here we balance theory and
experiment and show how to link them intimately together. We develop the
science of AI together with its engineering applications. We believe the adage
“There is nothing so practical as a good theory.” The spirit of our approach
is captured by the dictum “Everything should be made as simple as possible,
but not simpler.” We must build the science on solid foundations; we present
the foundations, but only sketch, and give some examples of, the complexity
required to build useful intelligent systems. Although the resulting systems
will be complex, the foundations and the building blocks should be simple.
The book works as an introductory text on AI for advanced undergraduate or graduate students in computer science or related disciplines such as
computer engineering, philosophy, cognitive science, or psychology. It will
appeal more to the technically minded; parts are technically challenging, focusing on learning by doing: designing, building, and implementing systems.
Any curious scientifically oriented reader will benefit from studying the book.
Previous experience with computational systems is desirable, but prior study
of the foundations on which we build, including logic, probability, calculus, and control theory, is not necessary, because we develop the concepts as
required.
xiii
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David L. Poole and Alan K. Mackworth
Frontmatter
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Preface
The serious student will gain valuable skills at several levels ranging from
expertise in the specification and design of intelligent agents to skills for implementing, testing, and improving real software systems for several challenging
application domains. The thrill of participating in the emergence of a new science of intelligent agents is one of the attractions of this approach. The practical
skills of dealing with a world of ubiquitous, intelligent, embedded agents are
now in great demand in the marketplace.
The focus is on an intelligent agent acting in an environment. We start with
simple agents acting in simple, static environments and gradually increase the
power of the agents to cope with more challenging worlds. We explore nine
dimensions of complexity that allow us to introduce, gradually and with modularity, what makes building intelligent agents challenging. We have tried to
structure the book so that the reader can understand each of the dimensions
separately, and we make this concrete by repeatedly illustrating the ideas with
four different agent tasks: a delivery robot, a diagnostic assistant, a tutoring
system, and a trading agent.
The agent we want the student to envision is a hierarchically designed
agent that acts intelligently in a stochastic environment that it can only partially observe – one that reasons about individuals and the relationships among
them, has complex preferences, learns while acting, takes into account other
agents, and acts appropriately given its own computational limitations. Of
course, we can’t start with such an agent; it is still a research question to build
such agents. So we introduce the simplest agents and then show how to add
each of these complexities in a modular way.
We have made a number of design choices that distinguish this book from
competing books, including the earlier book by the same authors:
• We have tried to give a coherent framework in which to understand AI.
We have chosen not to present disconnected topics that do not fit together. For example, we do not present disconnected logical and probabilistic views of AI, but we have presented a multidimensional design
space in which the students can understand the big picture, in which
probabilistic and logical reasoning coexist.
• We decided that it is better to clearly explain the foundations on which
more sophisticated techniques can be built, rather than present these
more sophisticated techniques. This means that a larger gap exists between what is covered in this book and the frontier of science. It also
means that the student will have a better foundation to understand current and future research.
• One of the more difficult decisions we made was how to linearize the
design space. Our previous book (Poole, Mackworth, and Goebel, 1998)
presented a relational language early and built the foundations in terms
of this language. This approach made it difficult for the students to
appreciate work that was not relational, for example, in reinforcement
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David L. Poole and Alan K. Mackworth
Frontmatter
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Preface
xv
learning that is developed in terms of states. In this book, we have chosen
a relations-late approach. This approach probably reflects better the research over the past few decades in which there has been much progress
in feature-based representations. It also allows the student to understand
that probabilistic and logical reasoning are complementary. The book,
however, is structured so that an instructor can present relations earlier.
This book uses examples from AIspace.org (http://www.aispace.org), a collection of pedagogical applets that we have been involved in designing. To
gain further experience in building intelligent systems, a student should also
experiment with a high-level symbol-manipulation language, such as LISP or
Prolog. We also provide implementations in AILog, a clean logic programming
language related to Prolog, designed to demonstrate many of the issues in this
book. This connection is not essential to an understanding or use of the ideas
in this book.
Our approach, through the development of the power of the agent’s capabilities and representation language, is both simpler and more powerful than
the traditional approach of surveying and cataloging various applications of
AI. However, as a consequence, some applications, such as the details of computational vision or computational linguistics, are not covered in this book.
We have chosen not to present an encyclopedic view of AI. Not every major idea that has been investigated is presented here. We have chosen some
basic ideas on which other, more sophisticated, techniques are based and
have tried to explain the basic ideas in detail, sketching how these can be
expanded.
Figure 1 (page xvi) shows the topics covered in the book. The solid lines
give prerequisites. Often the prerequisite structure does not include all subtopics. Given the medium of a book, we have had to linearize the topics. However, the book is designed so that the topics can be taught in any order satisfying the prerequisite structure.
The references given at the end of each chapter are not meant to be comprehensive: we have referenced works that we have directly used and works that
we think provide good overviews of the literature, by referencing both classic
works and more recent surveys. We hope that no researchers feel slighted by
their omission, and we are happy to have feedback where someone feels that
an idea has been misattributed. Remember that this book is not a survey of AI
research.
We invite you to join us in an intellectual adventure: building a science of
intelligent agents.
David Poole
Alan Mackworth
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David L. Poole and Alan K. Mackworth
Frontmatter
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Preface
1: AI &
Agents
2: Architecture
& Control
3: States &
Searching
4: Features &
Constraints
5: Propositions
& Inference
6: Uncertainty
7: Supervised
Learning
8: Planning
9: Planning Under
Uncertainty
10: Multi agent
systems
11: Beyond
Supervised Learning
12: Individuals
& Relations
13: Ontologies
& KBS
14: Relational Planning
Learning & Probability
Figure 1: Overview of chapters and dependencies
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David L. Poole and Alan K. Mackworth
Frontmatter
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xvii
Acknowledgments
Thanks to Randy Goebel for valuable input on this book. We also gratefully acknowledge the helpful comments on earlier drafts of this book received from Giuseppe Carenini, Cristina Conati, Mark Crowley, Pooyan
Fazli, Holger Hoos, Manfred Jaeger, Mohammad Reza Khojasteh, Jacek
Kisyński, Bob Kowalski, Kevin Leyton-Brown, Marian Mackworth, Gabriel
Murray, Alessandro Provetti, Marco Valtorta, and the anonymous reviewers.
Thanks to the students who pointed out many errors in earlier drafts.
Thanks to Jen Fernquist for the web site design, and to Tom Sgouros for
hyperlatex fixes. We are grateful to James Falen for permission to quote his
poem on constraints. Thanks to our editor Lauren Cowles and the staff at Cambridge University Press for all their support, encouragement, and help. All the
mistakes remaining are ours.
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