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Artificial Intelligence
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Details

  • 187 b/w illus. 173 exercises
  • Page extent: 682 pages
  • Size: 253 x 215 mm
  • Weight: 1.41 kg

Library of Congress

  • Dewey number: 006.3
  • Dewey version: 22
  • LC Classification: Q342 .P66 2010
  • LC Subject headings:
    • Computational intelligence--Textbooks
    • Artificial intelligence--Textbooks

Library of Congress Record

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Hardback

 (ISBN-13: 9780521519007)

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Artificial Intelligence
Cambridge University Press
9780521519007 - Artificial Intelligence - Foundations of Computational Agents - By David L. Poole and Alan K. Mackworth
Frontmatter/Prelims

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.


Artificial Intelligence

Foundations of Computational Agents

David L. Poole

University of British Columbia

Alan K. Mackworth

University of British Columbia


CAMBRIDGE UNIVERSITY PRESS
Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, 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

© 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 accuracy of URLs for external or third-party Internet Web sites referred to in this publication and does not guarantee that any content on such Web sites is, or will remain, accurate or appropriate.


To our families for their love, support, and patience Jennifer, Alexandra, and Shannon Marian and Bryn


Contents

Preface
xiii
I             Agents in the World: What Are Agents and How Can They Be Built?
1
1             Artificial Intelligence and Agents
3
1.1           What Is Artificial Intelligence?
3
1.2           A Brief History of AI
6
1.3           Agents Situated in Environments
10
1.4           Knowledge Representation
11
1.5           Dimensions of Complexity
19
1.6           Prototypical Applications
29
1.7           Overview of the Book
39
1.8           Review
40
1.9           References and Further Reading
40
1.10          Exercises
42
2             Agent Architectures and Hierarchical Control
43
2.1           Agents
43
2.2           Agent Systems
44
2.3           Hierarchical Control
50
2.4           Embedded and Simulated Agents
59
2.5           Acting with Reasoning
60
2.6           Review
65
2.7           References and Further Reading
66
2.8           Exercises
66
II            Representing and Reasoning
69
3             States and Searching
71
3.1           Problem Solving as Search
71
3.2           State Spaces
72
3.3           Graph Searching
74
3.4           A Generic Searching Algorithm
77
3.5           Uninformed Search Strategies
79
3.6           Heuristic Search
87
3.7           More Sophisticated Search
92
3.8           Review
106
3.9           References and Further Reading
106
3.10          Exercises
107
4             Features and Constraints
111
4.1           Features and States
111
4.2           PossibleWorlds, Variables, and Constraints
113
4.3           Generate-and-Test Algorithms
118
4.4           Solving CSPs Using Search
119
4.5           Consistency Algorithms
120
4.6           Domain Splitting
125
4.7           Variable Elimination
127
4.8           Local Search
130
4.9           Population-Based Methods
141
4.10          Optimization
144
4.11          Review
151
4.12          References and Further Reading
151
4.13          Exercises
152
5             Propositions and Inference
157
5.1           Propositions
157
5.2           Propositional Definite Clauses
163
5.3           Knowledge Representation Issues
174
5.4           Proving by Contradictions
185
5.5           Complete Knowledge Assumption
193
5.6           Abduction
199
5.7           Causal Models
204
5.8           Review
206
5.9           References and Further Reading
207
5.10          Exercises
208
6             Reasoning Under Uncertainty
219
6.1           Probability
219
6.2           Independence
232
6.3           Belief Networks
235
6.4           Probabilistic Inference
248
6.5           Probability and Time
266
6.6           Review
274
6.7           References and Further Reading
274
6.8           Exercises
275
III           Learning and Planning
281
7             Learning: Overview and Supervised Learning
283
7.1           Learning Issues
284
7.2           Supervised Learning
288
7.3           Basic Models for Supervised Learning
298
7.4           Composite Models
313
7.5           Avoiding Overfitting
320
7.6           Case-Based Reasoning
324
7.7           Learning as Refining the Hypothesis Space
327
7.8           Bayesian Learning
334
7.9           Review
340
7.10          References and Further Reading
341
7.11          Exercises
342
8             Planning with Certainty
349
8.1           Representing States, Actions, and Goals
350
8.2           Forward Planning
356
8.3           Regression Planning
357
8.4           Planning as a CSP
360
8.5           Partial-Order Planning
363
8.6           Review
366
8.7           References and Further Reading
367
8.8           Exercises
367
9             Planning Under Uncertainty
371
9.1           Preferences and Utility
373
9.2           One-Off Decisions
381
9.3           Sequential Decisions
386
9.4           The Value of Information and Control
396
9.5           Decision Processes
399
9.6           Review
412
9.7           References and Further Reading
413
9.8           Exercises
413
10            Multiagent Systems
423
10.1          Multiagent Framework
423
10.2          Representations of Games
425
10.3          Computing Strategies with Perfect Information
430
10.4          Partially Observable Multiagent Reasoning
433
10.5          Group Decision Making
445
10.6          Mechanism Design
446
10.7          Review
449
10.8          References and Further Reading
449
10.9          Exercises
450
11            Beyond Supervised Learning
451
11.1          Clustering
451
11.2          Learning Belief Networks
458
11.3          Reinforcement Learning
463
11.4          Review
485
11.5          References and Further Reading
486
11.6          Exercises
486
IV            Reasoning About Individuals and Relations
489
12            Individuals and Relations
491
12.1          Exploiting Structure Beyond Features
492
12.2          Symbols and Semantics
493
12.3          Datalog: A Relational Rule Language
494
12.4          Proofs and Substitutions
506
12.5          Function Symbols
512
12.6          Applications in Natural Language Processing
520
12.7          Equality
532
12.8          Complete Knowledge Assumption
537
12.9          Review
541
12.10         References and Further Reading
542
12.11         Exercises
542
13            Ontologies and Knowledge-Based Systems
549
13.1          Knowledge Sharing
549
13.2          Flexible Representations
550
13.3          Ontologies and Knowledge Sharing
563
13.4          Querying Users and Other Knowledge Sources
576
13.5          Implementing Knowledge-Based Systems
579
13.6          Review
591
13.7          References and Further Reading
591
13.8          Exercises
592
14            Relational Planning, Learning, and Probabilistic Reasoning
597
14.1          Planning with Individuals and Relations
598
14.2          Learning with Individuals and Relations
606
14.3          Probabilistic Relational Models
611
14.4          Review
618
14.5          References and Further Reading
618
14.6          Exercises
620
V             The Big Picture
623
15            Retrospect and Prospect
625
15.1          Dimensions of Complexity Revisited
625
15.2          Social and Ethical Consequences
629
15.3          References and Further Reading
632
A             Mathematical Preliminaries and Notation
633
A.1           Discrete Mathematics
633
A.2           Functions, Factors, and Arrays
634
A.3           Relations and the Relational Algebra
635
Bibliography
637
Index
653



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