Bargaining Game using Artificial agent based on Evolution Computation

진화계산 기반 인공에이전트를 이용한 교섭게임

  • Seong, Myoung-Ho (Dept. of Computer Science & Engineering, Kongju National University) ;
  • Lee, Sang-Yong (Div. of Computer Science & Engineering, Kongju National University)
  • 성명호 (공주대학교 컴퓨터공학과) ;
  • 이상용 (공주대학교 컴퓨터공학부)
  • Received : 2016.07.02
  • Accepted : 2016.08.20
  • Published : 2016.08.28


Analysis of bargaining games utilizing evolutionary computation in recent years has dealt with important issues in the field of game theory. In this paper, we investigated interaction and coevolution process among heterogeneous artificial agents using evolutionary computation in the bargaining game. We present three kinds of evolving-strategic agents participating in the bargaining games; genetic algorithms (GA), particle swarm optimization (PSO) and differential evolution (DE). The co-evolutionary processes among three kinds of artificial agents which are GA-agent, PSO-agent, and DE-agent are tested to observe which EC-agent shows the best performance in the bargaining game. The simulation results show that a PSO-agent is better than a GA-agent and a DE-agent, and that a GA-agent is better than a DE-agent with respect to co-evolution in bargaining game. In order to understand why a PSO-agent is the best among three kinds of artificial agents in the bargaining game, we observed the strategies of artificial agents after completion of game. The results indicated that the PSO-agent evolves in direction of the strategy to gain as much as possible at the risk of gaining no property upon failure of the transaction, while the GA-agent and the DE-agent evolve in direction of the strategy to accomplish the transaction regardless of the quantity.


Bargaining Game;Co-evolution;Genetic Algorithm;Particle Swarm Optimization;Differential Evolution


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