A Concise Introduction to Decentralized POMDPs
Springer | Artificial Intelligence | July 5, 2016 | ISBN-10: 3319289276 | 134 pages | pdf | 2.32 mb
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.
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