Artificial intelligence george f luger 6th edition pdf download

SIXTH EDITION

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Structures and Strategies for Complex Problem Solving

George F Luger University of New Mexico

SIXTH EDITION

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Library of Congress Cataloging-in-Publication Data

Luger, George F. Artificial intelligence : structures and strategies for complex problem solving / George F. Luger.-- 6th ed. p. cm. Includes bibliographical references and index. ISBN-13: 978-0-321-54589-3 (alk. paper) 1. Artificial intelligence. 2. Knowledge representation (Information theory) 3. Problem solving. 4. PROLOG (Computer program language) 5. LISP (Computer program language) I. Title. Q335.L84 2008 006.3--dc22 2007050376

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ISBN-13: 978-0-321-54589-3 ISBN-10: 0-321-54589-3

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For my wife, Kathleen, and our children Sarah, David, and Peter.

Si quid est in me ingenii, judices . . .

Cicero, Pro Archia Poeta

GFL

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PREFACE vii

PREFACE

What we have to learn to do we learn by doing. . .

—ARISTOTLE, Ethics

Welcome to the Sixth Edition!

I was very pleased to be asked to produce the sixth edition of my artificial intelligence book. It is a compliment to the earlier editions, started over twenty years ago, that our approach to AI has been so highly valued. It is also exciting that, as new development in the field emerges, we are able to present much of it in each new edition. We thank our many readers, colleagues, and students for keeping our topics relevant and our presenta- tion up to date.

Many sections of the earlier editions have endured remarkably well, including the presentation of logic, search algorithms, knowledge representation, production systems, machine learning, and, in the supplementary materials, the programming techniques developed in Lisp, Prolog, and with this edition, Java. These remain central to the practice of artificial intelligence, and a constant in this new edition.

This book remains accessible. We introduce key representation techniques including logic, semantic and connectionist networks, graphical models, and many more. Our search algorithms are presented clearly, first in pseudocode, and then in the supplementary mate- rials, many of them are implemented in Prolog, Lisp, and/or Java. It is expected that the motivated students can take our core implementations and extend them to new exciting applications.

We created, for the sixth edition, a new machine learning chapter based on stochastic methods (Chapter 13). We feel that the stochastic technology is having an increasingly larger impact on AI, especially in areas such as diagnostic and prognostic reasoning, natu- ral language analysis, robotics, and machine learning. To support these emerging technol- ogies we have expanded the presentation of Bayes' theorem, Markov models, Bayesian