Scalable Uncertainty Management 10th International Conference, SUM 2
This book constitutes the refereed proceedings of the 10th International Conference on Scalable Uncertainty Management, SUM 2016, held in Nice, France, in September 2016. The 18 regular papers and 5 short papers were carefully reviewed and selected from 3
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Steven Schockaert Pierre Senellart (Eds.)
Scalable Uncertainty Management 10th International Conference, SUM 2016 Nice, France, 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
Steven Schockaert Pierre Senellart (Eds.) •
Scalable Uncertainty Management 10th International Conference, SUM 2016 Nice, France, September 21–23, 2016 Proceedings
123
Editors Steven Schockaert Cardiff University Cardiff UK
Pierre Senellart Télécom ParisTech Paris France
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Artificial Intelligence ISBN 978-3-319-45855-7 ISBN 978-3-319-45856-4 (eBook) DOI 10.1007/978-3-319-45856-4 Library of Congress Control Number: 2016949633 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
Research areas such as Artificial Intelligence and Databases increasingly rely on principled methods for representing and manipulating large amounts of uncertain information. To meet this challenge, researchers in these fields are drawing from a wide range of different methodologies and uncertainty models. While Bayesian methods remain the default choice in most disciplines, sometimes there is a need for more cautious approaches, relying for instance on imprecise probabilities, ordinal uncertainty representations, or even purely qualitative models. The International Confer
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