Qualitative Spatial Abstraction in Reinforcement Learning
Reinforcement learning has developed as a successful learning approach for domains that are not fully understood and that are too complex to be described in closed form. However, reinforcement learning does not scale well to large and continuous problems.
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Editorial Board: A. Bundy J. G. Carbonell M. Pinkal H. Uszkoreit M. Veloso W. Wahlster M. J. Wooldridge
Advisory Board: Luigia Carlucci Aiello Franz Baader Wolfgang Bibel Leonard Bolc Craig Boutilier Ron Brachman Bruce G. Buchanan Anthony Cohn Artur d’Avila Garcez Luis Fari˜nas del Cerro Koichi Furukawa Georg Gottlob Patrick J. Hayes James A. Hendler Anthony Jameson Nick Jennings Aravind K. Joshi Hans Kamp Martin Kay Hiroaki Kitano Robert Kowalski Sarit Kraus Maurizio Lenzerini Hector Levesque John Lloyd
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Lutz Frommberger
Qualitative Spatial Abstraction in Reinforcement Learning
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Dr.-Ing. Lutz Frommberger Cognitive Systems Group Department of Mathematics and Informatics University of Bremen P.O. Box 330 440 28334 Bremen Germany [email protected] Managing Editors Prof. Dov M. Gabbay Augustus De Morgan Professor of Logic Department of Computer Science King’s College London Strand, London WC2R 2LS, UK
Prof. Dr. J¨org Siekmann Forschungsbereich Deduktions- und Multiagentensysteme, DFKI Stuhlsatzenweg 3, Geb. 43 66123 Saarbr¨ucken, Germany
This thesis was accepted as doctoral dissertation by the Department of Mathematics and Informatics, University of Bremen, under the title “Qualitative Spatial Abstraction for Reinforcement Learning”. Based on this work the author was granted the academic degree Dr.-Ing. Date of oral examination: 28th August 2009 Reviewers: Prof. Christian Freksa, Ph.D. (University of Bremen, Germany) Prof. Ramon L´opez de M´antaras, Ph.D. (Artificial Intelligence Research Institute, CSIC, Barcelona, Spain)
Cognitive Technologies ISSN 1611-2482 ISBN 978-3-642-16589-4 e-ISBN 978-3-642-16590-0 DOI 10.1007/978-3-642-16590-0 Springer Heidelberg Dordrecht London New York ACM Computing Classification: I.2 c Springer-Verlag Berlin Heidelberg 2010 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, reuse of illustrations, recitation, broadcasting, reproduction on microfilm 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. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover des
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