A Concise Introduction to Decentralized POMDPs

dc.contributor.author	Oliehoek, Frans A.	en_US
dc.contributor.author	Amato, Christopher	en_US
dc.date.accessioned	2017-06-26T03:28:16Z
dc.date.available	2017-06-26T03:28:16Z
dc.date.issued	2016	en_US
dc.identifier.isbn	978-3-319-28927-4	en_US
dc.identifier.isbn	978-3-319-28929-8	en_US
dc.identifier.other	HPU5160195	en_US
dc.identifier.uri	https://lib.hpu.edu.vn/handle/123456789/25973
dc.description.abstract	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.	en_US
dc.format.extent	146 p.	en_US
dc.format.mimetype	application/pdf	en_US
dc.language.iso	en	en_US
dc.publisher	Springer	en_US
dc.subject	Decentralized POMDPs	en_US
dc.subject	Decentralized partially observable Markov decision processes	en_US
dc.subject	Intelligent Systems	en_US
dc.title	A Concise Introduction to Decentralized POMDPs	en_US
dc.type	Book	en_US
dc.size	2,372Kb	en_US
dc.department	Technology	en_US