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Distributed Artificial Intelligence: 4th International Conference, DAI 2022, Tianjin, China, December 1517, 2022, Proceedings 1st ed. 2023 [Pehme köide]

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  • Formaat: Paperback / softback, 103 pages, kõrgus x laius: 235x155 mm, kaal: 191 g, 28 Illustrations, color; 6 Illustrations, black and white; IX, 103 p. 34 illus., 28 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 13824
  • Ilmumisaeg: 22-Mar-2023
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031255488
  • ISBN-13: 9783031255489
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Distributed Artificial Intelligence: 4th International Conference, DAI 2022, Tianjin, China, December 1517, 2022, Proceedings 1st ed. 2023Suurem pilt
  • Formaat: Paperback / softback, 103 pages, kõrgus x laius: 235x155 mm, kaal: 191 g, 28 Illustrations, color; 6 Illustrations, black and white; IX, 103 p. 34 illus., 28 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 13824
  • Ilmumisaeg: 22-Mar-2023
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3031255488
  • ISBN-13: 9783031255489
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783031255489.html
Märksõnad:
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.

 





 
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.