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A Concise Introduction to Decentralized POMDPs

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: re...

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Main Authors: Oliehoek, Frans A. (Author, http://id.loc.gov/vocabulary/relators/aut), Amato, Christopher. (http://id.loc.gov/vocabulary/relators/aut)
Corporate Author: SpringerLink (Online service)
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2016.
Edition:1st ed. 2016.
Series:SpringerBriefs in Intelligent Systems, Artificial Intelligence, Multiagent Systems, and Cognitive Robotics,
Subjects:
Artificial intelligence.
Control engineering.
Robotics.
Mechatronics.
Mathematical optimization.
Artificial Intelligence.
Control, Robotics, Mechatronics.
Optimization.
Online Access:https://doi.org/10.1007/978-3-319-28929-8
Summary: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. .
Physical Description:XX, 134 p. 36 illus., 22 illus. in color. online resource.
ISBN:9783319289298
ISSN:2196-548X

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