Preference Learning
The topic of preferences is a new branch of machine learning and data mining, and it has attracted considerable attention in artificial intelligence research in recent years. Representing and processing knowledge in terms of preferences is appealing as it
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Johannes Fürnkranz
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Eyke Hüllermeier
Editors
Preference Learning
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Editors Prof. Dr. Johannes Fürnkranz Knowledge Engineering Group Fachbereich Informatik Technische Universität Darmstadt Hochschulstr. 10 64289 Darmstadt Germany [email protected]
Prof. Dr. Eyke Hüllermeier Knowledge Engineering & Bioinformatics Fachbereich Mathematik und Informatik Philipps-Universität Marburg Hans-Meerwein-Str. 35032 Marburg Germany [email protected]
ISBN 978-3-642-14124-9 e-ISBN 978-3-642-14125-6 DOI 10.1007/978-3-642-14125-6 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2010937568 ACM Computing Classification (1998): I.2.6, H.2.8 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 design: KuenkelLopka GmbH Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface
The topic of preferences has attracted considerable attention in Artificial Intelligence (AI) research in previous years. Recent special issues of the AI Magazine (December 2008) and the Artificial Intelligence Journal (announced for 2010), both devoted to preferences, highlight the increasing importance of this area for AI. Representing and processing knowledge in terms of preferences is appealing as it allows one to specify desires in a declarative way, to combine qualitative and quantitative modes of reasoning, and to deal with inconsistencies and exceptions in a quite flexible manner. Like in other subfields of AI, including autonomous agents, nonmonotonic reasoning, constraint satisfaction, planning and qualitative decision theory, researchers in machine learning have started to pay increasing attention to the topic of preferences. In fact, as witnessed by a number of dedicated events, notably several workshops on preferences, ranking, and related topics (held, e.g., at NIPS 2004 and 2005, ECML/PKDD 2008 and 2009, SIGIR 2008 and 2009), we currently observe the formation of “preference learning” as a new branch of machine learning and data mining. For the time being, there is still no stipulated demarcation of this emerging subfield, neither in terms of a list of relevant topics nor in terms of an intentional “d
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