Distributed Artificial Intelligence: Second International Conference, DAI 2020, Nanjing, China, October 24–27, 2020, Proceedings
Author:   Matthew E. Taylor ,  Yang Yu ,  Edith Elkind ,  Yang Gao
Publisher:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
Volume:   12547
ISBN:  

9783030640958


Pages:   141
Publication Date:   25 November 2020
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Distributed Artificial Intelligence: Second International Conference, DAI 2020, Nanjing, China, October 24–27, 2020, Proceedings


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Overview

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.

Full Product Details

Author:   Matthew E. Taylor ,  Yang Yu ,  Edith Elkind ,  Yang Gao
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
Volume:   12547
Weight:   0.454kg
ISBN:  

9783030640958


ISBN 10:   3030640957
Pages:   141
Publication Date:   25 November 2020
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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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