Distributed Artificial Intelligence Second International Conference, DAI 2020, Nanjing, China, October 24–27, 2020, Proceedings /
Distributed Artificial Intelligence Second International Conference, DAI 2020, Nanjing, China, October 24–27, 2020, Proceedings / [electronic resource] :
edited by Matthew E. Taylor, Yang Yu, Edith Elkind, Yang Gao.
- 1st ed. 2020.
- IX, 141 p. 5 illus. online resource.
- Lecture Notes in Artificial Intelligence, 12547 2945-9141 ; .
- Lecture Notes in Artificial Intelligence, 12547 .
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.
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.
9783030640965
10.1007/978-3-030-64096-5 doi
Artificial intelligence.
Computers, Special purpose.
Application software.
Computers.
Computer networks .
Computer science--Mathematics.
Mathematical statistics.
Artificial Intelligence.
Special Purpose and Application-Based Systems.
Computer and Information Systems Applications.
Computing Milieux.
Computer Communication Networks.
Probability and Statistics in Computer Science.
Q334-342 TA347.A78
006.3
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.
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.
9783030640965
10.1007/978-3-030-64096-5 doi
Artificial intelligence.
Computers, Special purpose.
Application software.
Computers.
Computer networks .
Computer science--Mathematics.
Mathematical statistics.
Artificial Intelligence.
Special Purpose and Application-Based Systems.
Computer and Information Systems Applications.
Computing Milieux.
Computer Communication Networks.
Probability and Statistics in Computer Science.
Q334-342 TA347.A78
006.3