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Morgan Kaufmann Knowledge Representation and Reasoning.pdf

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    Knowledge Representation and Reasoning
    Copyright Page
    Contents
    Preface
    Acknowledgments
    Chapter 1. Introduction
    1.1 The Key Concepts: Knowledge, Representation, and Reasoning
    1.2 Why Knowledge Representation and Reasoning?
    1.3 The Role of Logic
    1.4 Bibliographic Notes
    1.5 Exercises
    Chapter 2. The Language of First-Order Logic
    2.1 Introduction
    2.2 The Syntax
    2.3 The Semantics
    2.4 The Pragmatics
    2.5 Explicit and Implicit Belief
    2.6 Bibliographic Notes
    2.7 Exercises
    Chapter 3. Expressing Knowledge
    3.1 Knowledge Engineering
    3.2 Vocabulary
    3.3 Basic Facts
    3.4 Complex Facts
    3.5 Terminological Facts
    3.6 Entailments
    3.7 Abstract Individuals
    3.8 Other Sorts of Facts
    3.9 Bibliographic Notes
    3.10 Exercises
    Chapter 4. Resolution
    4.1 The Propositional Case
    4.2 Handling Variables and Quantifiers
    4.3 Dealing with Computational Intractability
    4.4 Bibliographic Notes
    4.5 Exercises
    Chapter 5. Reasoning with Horn Clauses
    5.1 Horn Clauses
    5.2 SLD Resolution
    5.3 Computing SLD Derivations
    5.4 Bibliographic Notes
    5.5 Exercises
    Chapter 6. Procedural Control of Reasoning
    6.1 Facts and Rules
    6.2 Rule Formation and Search Strategy
    6.3 Algorithm Design
    6.4 Specifying Goal Order
    6.5 Committing to Proof Methods
    6.6 Controlling Backtracking
    6.7 Negation as Failure
    6.8 Dynamic Databases
    6.9 Bibliographic Notes
    6.10 Exercises
    Chapter 7. Rules in Production Systems
    7.1 Production Systems: Basic Operation
    7.2 Working Memory
    7.3 Production Rules
    7.4 A First Example
    7.5 A Second Example
    7.6 Conflict Resolution
    7.7 Making Production Systems More Efficient
    7.8 Applications and Advantages
    7.9 Some Significant Production Rule Systems
    7.10 Bibliographic Notes
    7.11 Exercises
    Chapter 8. Object-Oriented Representation
    8.1 Objects and Frames
    8.2 A Basic Frame Formalism
    8.3 An Example: Using Frames to Plan a Trip
    8.4 Beyond the Basics
    8.5 Bibliographic Notes
    8.6 Exercises
    Chapter 9. Structured Descriptions
    9.1 Descriptions
    9.2 A Description Language
    9.3 Meaning and Entailment
    9.4 Computing Entailments
    9.5 Taxonomies and Classification
    9.6 Beyond the Basics
    9.7 Bibliographic Notes
    9.8 Exercises
    Chapter 10. Inheritance
    10.1 Inheritance Networks
    10.2 Strategies for Defeasible Inheritance
    10.3 A Formal Account of Inheritance Networks
    10.4 Bibliographic Notes
    10.5 Exercises
    Chapter 11. Defaults
    11.1 Introduction
    11.2 Closed-World Reasoning
    11.3 Circumscription
    11.4 Default Logic
    11.5 Autoepistemic Logic
    11.6 Conclusion
    11.7 Bibliographic Notes
    11.8 Exercises
    Chapter 12. Vagueness, Uncertainty, and Degrees of Belief
    12.1 Noncategorical Reasoning
    12.2 Objective Probability
    12.3 Subjective Probability
    12.4 Vagueness
    12.5 Bibliographic Notes
    12.6 Exercises
    Chapter 13. Explanation and Diagnosis
    13.1 Diagnosis
    13.2 Explanation
    13.3 A Circuit Example
    13.4 Beyond the Basics
    13.5 Bibliographic Notes
    13.6 Exercises
    Chapter 14. Actions
    14.1 The Situation Calculus
    14.2 A Simple Solution to the Frame Problem
    14.3 Complex Actions
    14.4 Bibliographic Notes
    14.5 Exercises
    Chapter 15. Planning
    15.1 Planning in the Situation Calculus
    15.2 The STRIPS Representation
    15.3 Planning as a Reasoning Task
    15.4 Beyond the Basics
    15.5 Bibliographic Notes
    15.6 Exercises
    Chapter 16. The Tradeoff between Expressiveness and Tractability
    16.1 A Description Logic Case Study
    16.2 Limited Languages
    16.3 What Makes Reasoning Hard?
    16.4 Vivid Knowledge
    16.5 Beyond Vivid
    16.6 Bibliographic Notes
    16.7 Exercises
    Bibliography
    Index
    In Praise of Knowledge Representation and Reasoning This book clearly and concisely distills decades of work in AI on representing information in an efficient and general manner. The information is valuable not only for AI researchers, but also for people working on logical databases, XML, and the semantic web: read this book, and avoid reinventing the wheel! Henry Kautz, University of Washington A concise and lucid exposition of the major topics in knowledge representation, from two of the leading authorities in the field. It provides a thorough grounding, a wide variety of useful examples and exercises, and some thought-provoking new ideas for the expert reader. Stuart Russell, UC Berkeley Brachman and Levesque describe better than I have seen elsewhere, the range of formalisms between full first order logic at its most expressive and formalisms that compromise expressiveness for computation speed. Theirs are the most even-handed explanations I have seen. John McCarthy, Stanford University No other text provides a clearer introduc- tion to the use of logic in knowledge representation, reasoning, and planning, while also covering the essential ideas underlying practical methodologies such as production systems, description logic-based systems, and Bayesian networks. Lenhart Schubert, University of Rochester This textbook makes teaching my KR course much easier. It provides a solid foundation and starting point for further studies. While it does not (and cannot) cover all the topics that I tackle in an advanced course on KR, it provides the basics and the background assumptions behind KR research. Together with current research literature, it is the perfect choice for a graduate KR course. Bernhard Nebel, University of Freiburg Brachman and Levesque have laid much of the foundations of the field of knowledge representation and reasoning. This textbook provides a lucid and comprehensive introduction to the field. It is written with the same clarity and gift for exposition as their many research publications. The text will become an invaluable resource for students and researchers alike. Bart Selman, Cornell University This is a superb, clearly written, com- prehensive overview of nearly all the major issues, ideas, and techniques of this important branch of artificial intelligence, written by two of the masters of the field. The examples are well chosen, and the explanations are illuminating. Thank you for giving me this opportunity to review and praise a book that has sorely been needed by the KRR community. William J. Rapaport, State University of New York at Buffalo KR&R is known as “core AI” for a reason — it embodies some of the most basic con- ceptualizations and technical approaches in the field. And no researchers are more qualified to provide an in-depth introduction to the area than Brachman and Levesque, who have been at the forefront of KR&R for two decades. The book is clearly written, and is intelligently comprehensive. This is the definitive book on KR&R, and it is long overdue. Yoav Shoham, Stanford University
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    KNOWLEDGE REPRESENTATION AND REASONING
    About the Authors Ron Brachman has been doing influential work in knowledge representation since the time of his Ph.D. thesis at Harvard in 1977, the result of which was the KL-ONE system, which initiated the entire line of research on description logics. For the majority of his career he served in research management at AT&T, first at Bell Labs and then at AT&T Labs, where he was Communications Services Research Vice President, and where he built one of the premier research groups in the world in Artificial Intelligence. He is a Founding Fellow of the American Association for Artificial Intelligence (AAAI), and also a Fellow of the Association for Computing Machinery (ACM). He is currently President of the AAAI. He served as Secretary- Treasurer of the International Joint Conferences on Artificial Intelligence (IJCAI) for nine years. With more than 60 technical publications in knowledge representation and related areas to his credit, he has led a number of important knowledge representation systems efforts, includingthe CLASSIC projectatAT&T,whichresultedin acommerciallydeployedsystemthat processed more than $5 billion worth of equipment orders. Brachman is currently Director of the Information Processing Technology Office at the U.S. Defense Advanced Research Projects Agency (DARPA), where he is leading a new national-scale initiative in cognitive systems. Hector Levesque has been teaching knowledge representation and reasoning at the Univer- sity of Toronto since joining the faculty there in 1984. He has published over 60 research papers in the area, including three that have won best-paper awards. He has also co-authored a book on the logic of knowledge bases and the widely used TELL–ASK interface that he pioneered in his Ph.D. thesis. He and his collaborators have initiated important new lines of research on a number of topics, including implicit and explicit belief, vivid reasoning, new methods for satisfiability, and cognitive robotics. In 1985, he became the first non-American to receive the Computers and Thought Award given by IJCAI. He was the recipient of an E.W.R. Steacie Memorial Fellowship from the Natural Sciences and Engineering Research Council of Canada for 1990–1991. He was also a Fellow of the Canadian Institute for Advanced Research from 1984 to 1995, and is a Founding Fellow of the AAAI. He was elected to the Executive Council of the AAAI, and is on the editorial board of five journals. In 2001, Levesque was the Conference Chair of the IJCAI-01 conference, and is currently Past President of the IJCAI Board of Trustees. Brachman and Levesque have been working together on knowledge representation and rea- soning for more than 25 years. In their early collaborations at BBN and Schlumberger, they produced widely read work on key issues in the field, as well as several well-known knowledge representation systems, including KL-ONE, KRYPTON, and KANDOR. They presented a tutorial on knowledge representation at the International Joint Conference on Artificial Intelligence in 1983. In 1984, they coauthored a prize-winning paper at the National Conference on Artificial Intelligence that is generally regarded as the impetus for an explosion of work in description logics and which inspired many new research efforts on the tractability of knowledge rep- resentation systems, including hundreds of research papers. The following year, they edited a popular collection, Readings in Knowledge Representation, the first text in the area. With Ray Reiter, they founded and chaired the international conferences on Principles of Knowl- edge Representation and Reasoning in 1989; these conferences continue on to this day. Since 1992, they have worked together on the course in knowledge representation at the University of Toronto that is the basis for this book.
    KNOWLEDGE REPRESENTATION AND REASONING ■ ■ ■ Ronald J. Brachman Hector J. Levesque with a contribution by Maurice Pagnucco AMSTERDAM • BOSTON • HEIDELBERG • LONDON NEW YORK • OXFORD • PARIS • SAN DIEGO SAN FRANCISCO • SINGAPORE • SYDNEY • TOKYO Morgan Kaufmann is an imprint of Elsevier
    Publishing Director: Diane Cerra Senior Editor: Denise E. M. Penrose Publishing Services Manager: Andre Cuello Production Manager: Brandy Palacios Production Management: Graphic World Publishing Services Editorial Assistant: Valerie Witte Design Manager: Cate Barr Cover Design: Dick Hannus, Hannus Design Associates Cover Image: “Trout River Hills 6: The Storm Passing”, 1999, Oil on board, 80" × 31¾". Private Collection. Copyright Christopher Pratt Text Design: Graphic World Publishing Services Composition: Cepha Imaging Pvt. Ltd. Technical Illustration: Graphic World Publishing Services Copyeditor: Graphic World Publishing Services Proofreader: Graphic World Publishing Services Indexer: Graphic World Publishing Services Printer: Maple Press Cover Printer: Phoenix Color Morgan Kaufmann Publishers is an Imprint of Elsevier 500 Sansome Street, Suite 400, San Francisco, CA 94111 This book is printed on acid-free paper. © 2004 by Elsevier, Inc. All rights reserved. Designations used by companies to distinguish their products are often claimed as trademarks or registered trademarks. In all instances in which Morgan Kaufmann Publishers is aware of a claim, the product names appear in initial capital or all capital letters. Readers, however, should contact the appropriate companies for more complete information regarding trademarks and registration. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means—electronic, mechanical, photocopying, or otherwise—without written permission of the publishers. Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone: (+44) 1865 843830, fax: (+44) 1865 853333, e-mail: permissions@elsevier.com.uk. You may also complete your request on-line via the Elsevier homepage (http://elsevier.com) by selecting “Customer Support” and then “Obtaining Permissions.” Library of Congress Cataloging-in-Publication Data Brachman, Ronald J., 1949- Knowledge representation and reasoning / Ronald J. Brachman, Hector J. Levesque. p. cm. Includes bibliographical references and index. ISBN: 1-55860-932-6 1. Knowledge representation (Information theory) 2. Reasoning. I. Levesque, Hector J., 1951- II. Title. Q387.B73 2003 —dc22 006.3 32 For information on all Morgan Kaufmann publications, visit our website at www.mkp.com Printed in the United States of America 04 05 06 07 5 4 3 2 1 2004046573
    To Gwen, Rebecca, and Lauren; and Pat, Michelle, and Marc — because a reasoning mind still needs a loving heart.
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