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: reinforcement learning, decision-theoretic planning for single agents, classical multiagent planning, decentralized control, and operations research. 

List of 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. 

Product details

• Authors: Christopher Amato, Frans Oliehoek, Frans A Oliehoek, Frans A. Oliehoek
• Publisher: Springer, Berlin
• Languages: English
• Product format: Paperback / Softback
• Released: 01.01.2016
• EAN: 9783319289274
• ISBN: 978-3-31-928927-4
• No. of pages: 134
• Dimensions: 157 mm x 238 mm x 6 mm
• Weight: 248 g
• Illustrations: XX, 134 p. 36 illus., 22 illus. in color.
• Series: SpringerBriefs in Intelligent Systems; SpringerBriefs in Intelligent Systems
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; Optimierung, C, Regelungstechnik, Optimization, Robotics, Artificial Intelligence, computer science, Electronic devices & materials, Mathematical optimization, Control, Robotics, Mechatronics, Control, Robotics, Automation, Control engineering, Mechatronics, Automatic control engineering, Uncertainty