An Approach for Reducing the Graphical Model and Genetic Algorithm for Computing Approximate Nash Equilibrium in Static Games

Authors:
Wei-Yi Liu;Kun Yue;Jin Li;Ning Song;Li Ding
Affiliations:
School of Information Science and Engineering, Yunnan University, Kunming, People's Republic of China 650091;School of Information Science and Engineering, Yunnan University, Kunming, People's Republic of China 650091;School of Information Science and Engineering, Yunnan University, Kunming, People's Republic of China 650091 and School of Software, Yunnan University, Kunming, People's Republic of China 650091;Faculty of Materials and Metallurgical Engineering, Kunming University of Science and Technology, Kunming, People's Republic of China 650093;School of Information Science and Engineering, Yunnan University, Kunming, People's Republic of China 650091
Venue:
Journal of Intelligent and Robotic Systems
Year:
2010

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Abstract

In this paper, an approach for reducing the graphical model and a genetic algorithm for computing the approximate Nash equilibrium in a static multi-agent game is studied. In order to describe the relationship between strategies of various agents, the concepts of the influence degree and the strategy dependency are presented. Based on these concepts, an approach for reducing a graphical model is given. For discretized mixed strategies, the relationship between the discrete degree and the approximate degree is developed. Based on the regret degree, a genetic algorithm for computing the approximate Nash equilibrium is given. Experimental results indicate the genetic algorithm has high efficiency and few equilibrium errors.