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A Concise Introduction to Decentralized POMDPs [electronic resource] / by Frans A. Oliehoek, Christopher Amato.

By: Oliehoek, Frans A [author.].
Contributor(s): Amato, Christopher [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: SpringerBriefs in Intelligent Systems, Artificial Intelligence, Multiagent Systems, and Cognitive Robotics: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2016Description: XX, 134 p. 36 illus., 22 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319289298.Subject(s): Computer science | Artificial intelligence | Mathematical optimization | Control engineering | Robotics | Mechatronics | Computer Science | Artificial Intelligence (incl. Robotics) | Control, Robotics, Mechatronics | OptimizationAdditional physical formats: Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: 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. .
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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.

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. .

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