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200 00$aDistributed artificial intelligence $esecond international conference, DAI 2020, Nanjing, China, October 24-27, 2020, proceedings /$fMatthew E. Taylor [and three others] editors
205   $a1st ed. 2020.
210  1$aCham, Switzerland :$cSpringer,$d[2020]
210  4$d©2020
215   $a1 online resource (IX, 141 p. 5 illus.) 
225 1 $aLecture Notes in Artificial Intelligence ;$v12547
311   $a3-030-64095-7 
327   $aParallel Algorithm for Nash Equilibrium in Multiplayer Stochastic Games with Application to Naval Strategic Planning -- LAC-Nav: Collision-Free Multiagent Navigation Based on The Local ActionCells -- MGHRL: Meta Goal-generation for Hierarchical Reinforcement Learning -- D3PG: Decomposed Deep Deterministic Policy Gradient for Continuous Control -- Lyapunov-Based Reinforcement Learning for Decentralized Multi-Agent Control -- Hybrid Independent Learning in Cooperative Markov Games -- Efficient Exploration By Novelty-Pursuit -- Context-aware Multi-Agent Coordination with Loose Couplings and Repeated Interaction -- Battery Management for Automated Warehouses via Deep Reinforcement Learning.
330   $aThis book constitutes the refereed proceedings of the Second International Conference on Distributed Artificial Intelligence, DAI 2020, held in Nanjing, China, in October 2020. The 9 full papers presented in this book were carefully reviewed and selected from 22 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. Due to the Corona pandemic this event was held virtually.
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702   $aTaylor$b Matthew E.
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