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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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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Oliehoek, Frans A. (Autor), Amato, Christopher (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2016.
Edición:1st ed. 2016.
Colección:SpringerBriefs in Intelligent Systems, Artificial Intelligence, Multiagent Systems, and Cognitive Robotics,
Temas:
Acceso en línea:Texto Completo

MARC

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490 1 |a SpringerBriefs in Intelligent Systems, Artificial Intelligence, Multiagent Systems, and Cognitive Robotics,  |x 2196-5498 
505 0 |a 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. 
520 |a 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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