Oliehoek, Frans A.
A Concise Introduction to Decentralized POMDPs [electronic resource] / by Frans A. Oliehoek, Christopher Amato. - XX, 134 p. 36 illus., 22 illus. in color. online resource. - SpringerBriefs in Intelligent Systems, Artificial Intelligence, Multiagent Systems, and Cognitive Robotics, 2196-548X . - SpringerBriefs in Intelligent Systems, Artificial Intelligence, Multiagent Systems, and Cognitive Robotics, .
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. .
9783319289298
10.1007/978-3-319-28929-8 doi
Computer science.
Artificial intelligence.
Mathematical optimization.
Control engineering.
Robotics.
Mechatronics.
Computer Science.
Artificial Intelligence (incl. Robotics).
Control, Robotics, Mechatronics.
Optimization.
Q334-342 TJ210.2-211.495
006.3
A Concise Introduction to Decentralized POMDPs [electronic resource] / by Frans A. Oliehoek, Christopher Amato. - XX, 134 p. 36 illus., 22 illus. in color. online resource. - SpringerBriefs in Intelligent Systems, Artificial Intelligence, Multiagent Systems, and Cognitive Robotics, 2196-548X . - SpringerBriefs in Intelligent Systems, Artificial Intelligence, Multiagent Systems, and Cognitive Robotics, .
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. .
9783319289298
10.1007/978-3-319-28929-8 doi
Computer science.
Artificial intelligence.
Mathematical optimization.
Control engineering.
Robotics.
Mechatronics.
Computer Science.
Artificial Intelligence (incl. Robotics).
Control, Robotics, Mechatronics.
Optimization.
Q334-342 TJ210.2-211.495
006.3