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Band 13824

Distributed Artificial Intelligence 4th International Conference, DAI 2022, Tianjin, China, December 15–17, 2022, Proceedings

Aus der Reihe Lecture Notes in Computer Science

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Beschreibung

This book constitutes the refereed proceedings of the 4th International Conference on Distributed Artificial Intelligence, DAI 2022, held in Tianjin, China, in December 2022.

The 5 full papers presented in this book were carefully reviewed and selected from 12 submissions. DAI aims at bringing together...

Beschreibung

This book constitutes the refereed proceedings of the 4th International Conference on Distributed Artificial Intelligence, DAI 2022, held in Tianjin, China, in December 2022.

The 5 full papers presented in this book were carefully reviewed and selected from 12 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.

 

 

Produktdetails

Einband

Taschenbuch
Erscheinungsdatum

22.03.2023
Abbildungen

IX, 103 p. 34 illus., 28 illus. in color.
Herausgeber Makoto Yokoo + weitere

Produktdetails

Einband

Taschenbuch
Erscheinungsdatum

22.03.2023
Abbildungen

IX, 103 p. 34 illus., 28 illus. in color.
Herausgeber
Verlag Springer
Seitenzahl

103
Maße (L/B/H)

23,5/15,5/0,7 cm
Gewicht

189 g
Auflage

1st ed. 2023
Sprache

Englisch
ISBN

978-3-031-25548-9
Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Distributed Artificial Intelligence
  • Produktbild: Distributed Artificial Intelligence
  • A Distributed RBF-Assisted Differential Evolution for Distributed Expensive Constrained Optimization.- A Flexi Partner Selection Model for the Emergence of Cooperation in N-person Social Dilemmas.- Efficient Deep Reinforcement Learning via Policy-extended Successor Feature Approximator.- Maximal Information  Propagation with Limited Resources.- Optimistic Exploration based on Categorical-DQN for Cooperative Markov games.