Hybrid Neural Systems
Hybrid neural systems are computational systems which are based mainly on artificial neural networks and allow for symbolic interpretation or interaction with symbolic components. This book is derived from a workshop held during the NIPS'98 in Denver, Col
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Lecture Notes in Computer Science Edited by G.Goos, J. Hartmanis, and J. van Leeuwen
1778
Berlin Heidelberg New York Barcelona Hong Kong London Milan Paris Singapore Tokyo
Stefan Wermter Ron Sun (Eds.)
Hybrid Neural Systems
Series Editors Jaime G. Carbonell, Carnegie Mellon University, Pittsburgh, PA, USA J¨org Siekmann, University of Saarland, Saarbr¨ucken, Germany Volume Editors Stefan Wermter University of Suderland Centre of Informatics, SCET St Peters Way, Sunderland, SR6 0DD, UK E-mail: [email protected] Ron Sun University of Missouri-Colombia CECS Department 201 Engineering Building West, Columbia, MO 65211-2060, USA E-mail: [email protected]
Cataloging-in-Publication Data applied for Die Deutsche Bibliothek - CIP-Einheitsaufnahme Hybrid neural systems / Stefan Wermter ; Ron Sun (ed.). - Berlin ; Heidelberg ; New York ; Barcelona ; Hong Kong ; London ; Milan ; Paris ; Singapore ; Tokyo : Springer, 2000 (Lecture notes in computer science ; Vol. 1778 : Lecture notes in artificial intelligence) ISBN 3-540-67305-9
CR Subject Classification (1991): I.2.6, F.1, C.1.3, I.2 ISBN 3-540-67305-9 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 is a company in the BertelsmannSpringer publishing group. c Springer-Verlag Berlin Heidelberg 2000 Printed in Germany Typesetting: Camera-ready by author data conversion by PTP Berlin, Stefan Sossna Printed on acid-free paper SPIN: 10719871 06/3142 543210
Preface
The aim of this book is to present a broad spectrum of current research in hybrid neural systems, and advance the state of the art in neural networks and artificial intelligence. Hybrid neural systems are computational systems which are based mainly on artificial neural networks but which also allow a symbolic interpretation or interaction with symbolic components. This book focuses on the following issues related to different types of representation: How does neural representation contribute to the success of hybrid systems? How does symbolic representation supplement neural representation? How can these types of representation be combined? How can we utilize their interaction and synergy? How can we develop neural and hybrid systems for new domains? What are the strengths and weaknesses of hybrid neural techniques? Are current principles and methodologies in hybrid neural systems useful? How can they be extended? What will be the impact of hybrid and neural techniques in the future? In order to bring toget
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