A Concise Introduction to Decentralized POMDPs
Oliehoek, Frans A.
A Concise Introduction to Decentralized POMDPs [recurso electrónico] / 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
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 [recurso electrónico] / 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
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
