A Concise Introduction to Decentralized POMDPs
Authors: Oliehoek, Frans A., Amato, Christopher
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- Suitable for researchers and graduate students in AI
- Assumes prior familiarity with agents, probability, and game theory
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- About this book
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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.
- Table of contents (9 chapters)
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Multiagent Systems Under Uncertainty
Pages 1-9
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The Decentralized POMDP Framework
Pages 11-32
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Finite-Horizon Dec-POMDPs
Pages 33-40
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Exact Finite-Horizon Planning Methods
Pages 41-53
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Approximate and Heuristic Finite-Horizon Planning Methods
Pages 55-67
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Table of contents (9 chapters)
- Download Preface 1 PDF (110 KB)
- Download Sample pages 1 PDF (202.7 KB)
- Download Table of contents PDF (108.6 KB)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- A Concise Introduction to Decentralized POMDPs
- Authors
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- Frans A. Oliehoek
- Christopher Amato
- Series Title
- SpringerBriefs in Intelligent Systems
- Copyright
- 2016
- Publisher
- Springer International Publishing
- Copyright Holder
- The Author(s)
- eBook ISBN
- 978-3-319-28929-8
- DOI
- 10.1007/978-3-319-28929-8
- Softcover ISBN
- 978-3-319-28927-4
- Series ISSN
- 2196-548X
- Edition Number
- 1
- Number of Pages
- XX, 134
- Number of Illustrations
- 14 b/w illustrations, 22 illustrations in colour
- Topics
