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.

Inhalt
Multiagent Systems Under Uncertainty.- The Decentralized POMDP Framework.- Finite-Horizon Dec-POMDPs.- Exact Finite-Horizon Planning Methods.- Approximate and Heuristic Finite-Horizon Planning Methods.- Infinite-Horizon Dec-POMDPs.- Infinite-Horizon Planning Methods: Discounted Cumulative Reward.- Infinite-Horizon Planning Methods: Average Reward.- Further Topics.