A Concise Introduction to Decentralized POMDPs
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Beschreibung
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.
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.
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
Autor*in
Frans A. Oliehoek
Themen in »A Concise Introduction to Decentralized POMDPs«
Agents Decentralized Control Dynamic Programming Heuristics Markov Decision Processes Multiagent Planning Multiagent Systems Operations Research Optimization POMDPs Reinforcement Learning Search Uncertainty