Learning Complex Concepts Using Crowdsourcing: A Bayesian Approach
We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated according to (noisy) observations from experts, whose behaviors are modeled using discret
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LNAI Founding Series Editor Joerg Siekmann DFKI and Saarland University, Saarbrücken, Germany
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Ronen I. Brafman Fred S. Roberts Alexis Tsoukiàs (Eds.)
Algorithmic Decision Theory Second International Conference, ADT 2011 Piscataway, NJ, USA, October 26-28, 2011 Proceedings
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Series Editors Randy Goebel, University of Alberta, Edmonton, Canada Jörg Siekmann, University of Saarland, Saarbrücken, Germany Wolfgang Wahlster, DFKI and University of Saarland, Saarbrücken, Germany
Volume Editors Ronen I. Brafman Ben-Gurion University of the Negev Beer-Sheva, Israel E-mail: [email protected] Fred S. Roberts Rutgers University, DIMACS Piscataway, NJ, USA E-mail: [email protected] Alexis Tsoukiàs Université Paris Dauphine, CNRS - LAMSADE Paris, France E-mail: [email protected]
ISSN 0302-9743 e-ISSN 1611-3349 ISBN 978-3-642-24872-6 e-ISBN 978-3-642-24873-3 DOI 10.1007/978-3-642-24873-3 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2011938800 CR Subject Classification (1998): I.2, H.3, F.1, H.4, G.1.6, F.4.1-2, C.2 LNCS Sublibrary: SL 7 – Artificial Intelligence
© Springer-Verlag Berlin Heidelberg 2011 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. 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. Typesetting: Camera-ready by author, data conversion by Scientific Publishing Services, Chennai, India Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface
Algorithmic Decision Theory (ADT) is a new interdisciplinary research area aiming at bringing together researchers from different fields such as decision theory, discrete mathematics, theoretical computer science, economics, and artificial intelligence, in order to improve decision support in the presence of massive data bases, combinatorial structures, partial and/or uncertain information and distributed, possibly interoperating decision makers. Such problems arise in real-world decision making in areas such as humanitarian logistics, epidemiology, environmental protection, risk assessment and management, e-government, electronic commerce, protection against natural disasters, and recommender systems. In 2007, the EU-funded COST Action IC0602 on Algorithmic Decision Theory was started, networking a large number of
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