How Expert Confidence Can Improve Collective Decision-Making in Contextual Multi-Armed Bandit Problems
In collective decision-making (CDM) a group of experts with a shared set of values and a common goal must combine their knowledge to make a collectively optimal decision. Whereas existing research on CDM primarily focuses on making binary decisions, we fo
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Ngoc Thanh Nguyen · Bao Hung Hoang · Cong Phap Huynh · Dosam Hwang · Bogdan Trawin´ski · Gottfried Vossen (Eds.)
Computational Collective Intelligence 12th International Conference, ICCCI 2020 Da Nang, Vietnam, November 30 – December 3, 2020 Proceedings
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Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science
Series Editors Randy Goebel University of Alberta, Edmonton, Canada Yuzuru Tanaka Hokkaido University, Sapporo, Japan Wolfgang Wahlster DFKI and Saarland University, Saarbrücken, Germany
Founding Editor Jörg Siekmann DFKI and Saarland University, Saarbrücken, Germany
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More information about this series at http://www.springer.com/series/1244
Ngoc Thanh Nguyen Bao Hung Hoang Cong Phap Huynh Dosam Hwang Bogdan Trawiński Gottfried Vossen (Eds.) •
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Computational Collective Intelligence 12th International Conference, ICCCI 2020 Da Nang, Vietnam, November 30 – December 3, 2020 Proceedings
123
Editors Ngoc Thanh Nguyen Department of Applied Informatics Wrocław University of Science and Technology Wroclaw, Poland
Bao Hung Hoang Thua Thien Hue Center of Information Technology Hue, Vietnam
Faculty of Information Technology Nguyen Tat Thanh University Ho Chi Minh, Vietnam
Dosam Hwang Department of Computer Engineering Yeungnam University Gyeungsan, Korea (Republic of)
Cong Phap Huynh Vietnam - Korea University of Information and Communication Technology University of Da Nang Da Nang, Vietnam
Gottfried Vossen Department of Information Systems University of Münster Münster, Germany
Bogdan Trawiński Department of Applied Informatics Wrocław University of Science and Technology Wroclaw, Poland
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Artificial Intelligence ISBN 978-3-030-63006-5 ISBN 978-3-030-63007-2 (eBook) https://doi.org/10.1007/978-3-030-63007-2 LNCS Sublibrary: SL7 – Artificial Intelligence © Springer Nature Switzerland AG 2020 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, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher rem
