Deep Active Inference for Partially Observable MDPs
Deep active inference has been proposed as a scalable approach to perception and action that deals with large policy and state spaces. However, current models are limited to fully observable domains. In this paper, we describe a deep active inference mode
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ctive Inference First International Workshop, IWAI 2020 Co-located with ECML/PKDD 2020 Ghent, Belgium, September 14, 2020, Proceedings
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Communications in Computer and Information Science Editorial Board Members Joaquim Filipe Polytechnic Institute of Setúbal, Setúbal, Portugal Ashish Ghosh Indian Statistical Institute, Kolkata, India Raquel Oliveira Prates Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil Lizhu Zhou Tsinghua University, Beijing, China
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More information about this series at http://www.springer.com/series/7899
Tim Verbelen Pablo Lanillos Christopher L. Buckley Cedric De Boom (Eds.) •
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Active Inference First International Workshop, IWAI 2020 Co-located with ECML/PKDD 2020 Ghent, Belgium, September 14, 2020 Proceedings
123
Editors Tim Verbelen Ghent University Ghent, Belgium Christopher L. Buckley University of Sussex Brighton, UK
Pablo Lanillos Donders Institute for Brain, Cognition and Behaviour Nijmegen, The Netherlands Cedric De Boom Ghent University Ghent, Belgium
ISSN 1865-0929 ISSN 1865-0937 (electronic) Communications in Computer and Information Science ISBN 978-3-030-64918-0 ISBN 978-3-030-64919-7 (eBook) https://doi.org/10.1007/978-3-030-64919-7 © 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 remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
Active inference is a theory of behavior and learning that originated in neuroscience. The basic assumption is that intelligent agents entertain a generative model of their environment, and their main goal is to minimize surprise or, more formally, their free energy. Active inference not only offers an interesting framework for understanding behavior and the brain, but also to develop artificial in
