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Distributed Artificial Intelligence de Jie Chen

Distributed Artificial Intelligence: Lecture Notes in Computer Science, cartea 13170

Editat de Jie Chen, Jérôme Lang, Christopher Amato, Dengji Zhao
en Limba Engleză Paperback – 12 ian 2022
This book constitutes the refereed proceedings of the Third International Conference on Distributed Artificial Intelligence, DAI 2021, held in Shanghai, China, in December 2021. The 15 full papers presented in this book were carefully reviewed and selected from 31 submissions. DAI aims at bringing together international researchers and practitioners in related areas including general AI, multiagent systems, distributed learning, computational game theory, etc., to provide a single, high-profile, internationally renowned forum for research in the theory and practice of distributed AI.
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Specificații

ISBN-13: 9783030946616
ISBN-10: 3030946614
Pagini: 256
Ilustrații: VIII, 247 p. 84 illus., 52 illus. in color.
Dimensiuni: 155 x 235 x 15 mm
Greutate: 0.39 kg
Ediția:1st edition 2022
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

The Power of Signaling and its Intrinsic Connection to the Price of Anarchy.- Uncertainty-aware Low-Rank Q-Matrix Estimation for Deep Reinforcement Learning.- SEIHAI: A Sample-effcient Hierarchical AI for the MineRL Competition.- GC: Multi-Agent Group Belief with Graph Clustering.- Incomplete Distributed Constraint Optimization Problems: Model, Algorithms, and Heuristics.- Securities Based Decision Markets.- MARL for Traffc Signal Control in Scenarios with Different Intersection Importance.- Safe Distributional Reinforcement Learning.- The Positive Effect of User Faults over Agent Perception in Collaborative Settings and its Use in Agent Design.- Behavioral Stable Marriage Problems.- FUN-Agent: a HUMAINE Competitor.- Signal Instructed Coordination in Cooperative Multi-Agent Reinforcement Learning.- A Description of the Jadescript Type System.- Combining M-MCTS and Deep Reinforcement Learning for General Game Playing.- A Two-Step Method for Dynamics of Abstract Argumentation.