Scalable Uncertainty Management 11th International Conference, SUM 2
This book constitutes the refereed proceedings of the 11th International Conference on Scalable Uncertainty Management, SUM 2017, which was held in Granada, Spain, in October 2017. The 24 full and 6 short papers presented in this volume were careful
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Serafín Moral · Olivier Pivert Daniel Sánchez · Nicolás Marín (Eds.)
Scalable Uncertainty Management 11th International Conference, SUM 2017 Granada, Spain, October 4–6, 2017 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
Serafín Moral Olivier Pivert Daniel Sánchez Nicolás Marín (Eds.) •
•
Scalable Uncertainty Management 11th International Conference, SUM 2017 Granada, Spain, October 4–6, 2017 Proceedings
123
Editors Serafín Moral University of Granada Granada Spain
Daniel Sánchez University of Granada Granada Spain
Olivier Pivert University of Rennes I Lannion France
Nicolás Marín University of Granada Granada Spain
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Artificial Intelligence ISBN 978-3-319-67581-7 ISBN 978-3-319-67582-4 (eBook) DOI 10.1007/978-3-319-67582-4 Library of Congress Control Number: 2017953397 LNCS Sublibrary: SL7 – Artificial Intelligence © Springer International Publishing AG 2017 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. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
Managing uncertainty and inconsistency has been extensively explored in the field of artificial intelligence over a number of years. Now, with the advent of massive amounts of data and knowledge from distributed, heterogeneous, and potentially con
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