Taylor / Yu / Elkind Distributed Artificial Intelligence

Second International Conference, DAI 2020, Nanjing, China, October 24–27, 2020, Proceedings
1. Auflage 2020
ISBN: 978-3-030-64096-5
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 141 Seiten, eBook

Reihe: Lecture Notes in Artificial Intelligence

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.

Zielgruppe

Research

Autoren/Hrsg.

Taylor, Matthew E.

Yu, Yang

Elkind, Edith

Gao, Yang

Inhaltsverzeichnis

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.