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.
Publisher: Springer International Publishing AG
Number of pages: 134
Weight: 2467 g
Dimensions: 235 x 155 x 8 mm
Edition: 1st ed. 2016