Distributed artificial intelligence : second international conference, DAI 2020, Nanjing, China, October 24-27, 2020, proceedings /

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Bibliographic Details
Meeting name:International Conference on Distributed Artificial Intelligence (2nd : 2020 : Online)
Imprint:Cham : Springer, [2020]
Description:1 online resource (149 pages).
Language:English
Series:Lecture notes in computer science. Lecture notes in artificial intelligence ; 12547
Lecture notes in computer science. Lecture notes in artificial intelligence.
Lecture notes in computer science ; 12547.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/12609221
Hidden Bibliographic Details
Other authors / contributors:Taylor, Matthew E., editor.
Yu, Yang, editor.
Elkind, Edith, 1976- editor.
Gao, Yang, editor.
ISBN:9783030640965
3030640965
9783030640958
Digital file characteristics:text file
PDF
Notes:International conference proceedings.
Includes author index.
Description based upon print version of record.
Summary:This 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.
Other form:Print version: Taylor, Matthew E. Distributed Artificial Intelligence : Second International Conference, DAI 2020, Nanjing, China, October 24-27, 2020, Proceedings Cham : Springer International Publishing AG,c2020 9783030640958
Standard no.:10.1007/978-3-030-64096-5

MARC

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505 0 |a Parallel 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. 
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