Belief Functions: Theory and Applications 4th International Conferen
This book constitutes the thoroughly refereed proceedings of the 4th International Conference on Belief Functions, BELIEF 2016, held in Prague, Czech Republic, in September 2016. The 25 revised full papers presented in this book were carefully selected an
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Jirina Vejnarová Václav Kratochvíl (Eds.)
Belief Functions: Theory and Applications 4th International Conference, BELIEF 2016 Prague, Czech Republic, September 21–23, 2016 Proceedings
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Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science
LNAI Series Editors Randy Goebel University of Alberta, Edmonton, Canada Yuzuru Tanaka Hokkaido University, Sapporo, Japan Wolfgang Wahlster DFKI and Saarland University, Saarbrücken, Germany
LNAI Founding Series Editor Joerg Siekmann DFKI and Saarland University, Saarbrücken, Germany
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More information about this series at http://www.springer.com/series/1244
Jiřina Vejnarová Václav Kratochvíl (Eds.) •
Belief Functions: Theory and Applications 4th International Conference, BELIEF 2016 Prague, Czech Republic, September 21–23, 2016 Proceedings
123
Editors Jiřina Vejnarová Institute of Information Theory and Automation Czech Academy of Sciences Prague Czech Republic
Václav Kratochvíl Institute of Information Theory and Automation Czech Academy of Sciences Prague Czech Republic
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Artificial Intelligence ISBN 978-3-319-45558-7 ISBN 978-3-319-45559-4 (eBook) DOI 10.1007/978-3-319-45559-4 Library of Congress Control Number: 2016949600 LNCS Sublibrary: SL7 – Artificial Intelligence © Springer International Publishing Switzerland 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, 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. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG Switzerland
Preface
The theory of belief functions, also referred to as evidence theory or Dempster-Shafer theory, is a well-established general framework for reasoning with uncertainty. It has well-understood connections to other frameworks, such as probability, possibility, and imprecise probability theories. First introduced by Arthur P. Dempster in the context of statistical inference, the theory was later developed
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