| Description |
1 online resource (xx, 134 pages) : illustrations (some color) |
| Series |
SpringerBriefs in intelligent systems, Artificial intelligence, multiagent systems, and cognitive robotics, 2196-548X |
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SpringerBriefs in intelligent systems. Artificial intelligence, multiagent systems, and cognitive robotics
|
| 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 |
| 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 |
| Bibliography |
Includes bibliographical references |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed June 15, 2016) |
| Subject |
Decision making -- Data processing
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Multiagent systems.
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BUSINESS & ECONOMICS -- Industrial Management.
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BUSINESS & ECONOMICS -- Management.
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BUSINESS & ECONOMICS -- Management Science.
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BUSINESS & ECONOMICS -- Organizational Behavior.
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Decision making -- Data processing
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Multiagent systems
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| Form |
Electronic book
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| Author |
Amato, Christopher, author
|
| ISBN |
9783319289298 |
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3319289292 |
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3319289276 |
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9783319289274 |
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