Del 12547 i serien Lecture Notes in Computer Science

Distributed Artificial Intelligence

Second International Conference, DAI 2020, Nanjing, China, October 24–27, 2020, Proceedings

Häftad, Engelska, 2020

AvMatthew E. Taylor,Yang Yu,Edith Elkind,Yang Gao

729 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt för medlemmar vid köp för minst 249 kr.

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.

Produktinformation

Utgivningsdatum2020-11-25
Mått155 x 235 x 9 mm
Vikt242 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor141
FörlagSpringer Nature Switzerland AG
ISBN9783030640958

Tillhör följande kategorier

Tillämpad datateknik inom Data och IT
Informationsteknik: allmänt inom Data och IT
Systemvetenskap och AI inom Data och IT
Artificiell intelligens inom Data och IT

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.