Authors:
- First book dedicated to this topic
- Suitable for researchers and graduate students in AI
- Assumes prior familiarity with agents, probability, and game theory
- Includes supplementary material: sn.pub/extras
Part of the book series: SpringerBriefs in Intelligent Systems (BRIEFSINSY)
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Table of contents (9 chapters)
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Front Matter
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Back Matter
About this book
This book introduces multiagent planning under uncertainty as formalized by decentralized partially observable Markov decision processes (Dec-POMDPs). The intended audience is researchers and graduate students working in the fields of artificial intelligence related to sequential decision making: reinforcement learning, decision-theoretic planning for single agents, classical multiagent planning, decentralized control, and operations research.
Authors and Affiliations
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School of Elect Eng, Electr & CS, University of Liverpool, Liverpool, United Kingdom
Frans A. Oliehoek
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Intelligence Lab, G472, MIT, Comp Sci & Artificial, Cambridge, USA
Christopher Amato
Bibliographic Information
Book Title: A Concise Introduction to Decentralized POMDPs
Authors: Frans A. Oliehoek, Christopher Amato
Series Title: SpringerBriefs in Intelligent Systems
DOI: https://doi.org/10.1007/978-3-319-28929-8
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s) 2016
Softcover ISBN: 978-3-319-28927-4Published: 14 June 2016
eBook ISBN: 978-3-319-28929-8Published: 03 June 2016
Series ISSN: 2196-548X
Series E-ISSN: 2196-5498
Edition Number: 1
Number of Pages: XX, 134
Number of Illustrations: 14 b/w illustrations, 22 illustrations in colour
Topics: Artificial Intelligence, Control, Robotics, Mechatronics, Optimization