Agent-Based Hybrid Intelligent Systems An Agent-Based Framework for
Solving complex problems in real-world contexts, such as financial investment planning or mining large data collections, involves many different sub-tasks, each of which requires different techniques. To deal with such problems, a great diversity of intel
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Subseries of Lecture Notes in Computer Science
2938
3
Berlin Heidelberg New York Hong Kong London Milan Paris Tokyo
Zili Zhang Chengqi Zhang
Agent-Based Hybrid Intelligent Systems An Agent-Based Framework for Complex Problem Solving
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Series Editors Jaime G. Carbonell, Carnegie Mellon University, Pittsburgh, PA, USA J¨org Siekmann, University of Saarland, Saarbr¨ucken, Germany Authors Zili Zhang Southwest China Normal University Faculty of Computer and Information Science Chongqing 400715, China E-mail: [email protected] Chengqi Zhang Deakin University School of Information Technology Geelong, VIC 3217, Australia E-mail: [email protected]
Cataloging-in-Publication Data applied for A catalog record for this book is available from the Library of Congress. Bibliographic information published by Die Deutsche Bibliothek Die Deutsche Bibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data is available in the Internet at . CR Subject Classification (1998): I.2, D.2, H.2.8, J.1, F.1 ISSN 0302-9743 ISBN 3-540-20908-5 Springer-Verlag Berlin Heidelberg New York This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, re-use of illustrations, recitation, broadcasting, reproduction on microfilms or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag. Violations are liable for prosecution under the German Copyright Law. Springer-Verlag is a part of Springer Science+Business Media springeronline.com c Springer-Verlag Berlin Heidelberg 2004 Printed in Germany Typesetting: Camera-ready by author, data conversion by Olgun Computergrafik Printed on acid-free paper SPIN: 10981160 06/3142 543210
Preface
Many complex problems, such as financial investment planning, involve many different components or sub-tasks, each of which requires different types of processing. To solve such complex problems, a great diversity of intelligent techniques, including traditional hard computing techniques (e.g., expert systems) and soft computing techniques (e.g., fuzzy logic, neural networks, and genetic algorithms), are required. These techniques are complementary rather than competitive, and thus must be used in combination and not exclusively. This results in systems called hybrid intelligent systems. In other words, hybrid solutions are crucial for complex problem solving and decision-making. However, the design and development of hybrid intelligent systems is difficult because they have a large number of parts or components that have many interactions. Existing software development techniques cannot manage these complex interactions efficiently as these interactions may occur at unpredictable times, for unpredictable reasons, and between unpredictable components. An a
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