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CCGnet: A deep learning approach to predict Nash equilibrium of chance-constrained games.
- Source :
-
Information Sciences . May2023, Vol. 627, p20-33. 14p. - Publication Year :
- 2023
-
Abstract
- This paper proposes a novel method for efficiently finding the Nash equilibrium in a chance-constrained game (CCG). Conventional numerical solution methods require significant computational time when solving multiple instances of CCG. We introduce CCGnet, a deep learning approach that is capable of efficiently solving multiple instances of CCG in a one-shot manner. CCGnet employs a specialized network structure and training algorithm based on neurodynamic optimization. We demonstrate the strong performance of CCGnet in practice and show that our proposed method outperforms conventional methods. [ABSTRACT FROM AUTHOR]
- Subjects :
- *NASH equilibrium
*DEEP learning
*NEUROREHABILITATION
Subjects
Details
- Language :
- English
- ISSN :
- 00200255
- Volume :
- 627
- Database :
- Academic Search Index
- Journal :
- Information Sciences
- Publication Type :
- Periodical
- Accession number :
- 162255813
- Full Text :
- https://doi.org/10.1016/j.ins.2023.01.064