![]() ![]() Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Notices Knowledge and best practice in this field are constantly changing. Details on how to seek permission, further information about the Publisher’s permissions policies, and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency can be found at our Web site: This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. 225 Wyman Street, Waltham, MA 02451, USA This book is printed on acid-free paper. Morgan Kaufmann is an imprint of ElsevierĪcquiring Editor: Rachel Roumeliotis Development Editor: David Bevans Project Manager: Sarah Binns Designer: Joanne Blank Morgan Kaufmann Publishers is an imprint of Elsevier. Real-world case studies and chapter ending exercises help to create a full and realized picture of how search fits into the world of artificial intelligence and the one around us.*Provides real-world success stories and case studies for heuristic search algorithms *Includes many AI developments not yet covered in textbooks such as pattern databases, symbolic search, and parallel processing units ![]() While no previous familiarity with heuristic search is necessary the reader should have a basic knowledge of algorithms, data structures, and calculus. Heuristic search as a problem solving tool is demonstrated in applications for puzzle solving, game playing, constraint satisfaction and machine learning. Current developments in search such as pattern databases and search with efficient use of external memory and parallel processing units on main boards and graphics cards are detailed. The authors present a thorough overview of heuristic search with a balance of discussion between theoretical analysis and efficient implementation and application to real-world problems. Search has been vital to artificial intelligence from the very beginning as a core technique in problem solving.
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