• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学 ›› 2015, Vol. 37 ›› Issue (09): 1682-1687.

• • 上一篇    下一篇

基于演化博弈惩罚机制的多智能体协作稳定性研究

郑延斌1,2,段领玉1,李波1,梁凯1   

  1. (1.河南师范大学计算机与信息工程学院,河南 新乡 453007;
    2.智慧商务与物联网技术河南省工程实验室,河南 新乡 453007)
  • 收稿日期:2014-09-12 修回日期:2015-03-02 出版日期:2015-09-25 发布日期:2015-09-25
  • 基金资助:

    河南省重点科技攻关项目(122102210086,132102210537,132102210538)

Research on multiagent cooperation stability based #br# on the punishment mechanism of evolutionary games 

ZHENG Yanbin1,2,DUAN Lingyu1,LI Bo1,LIANG Kai1   

  1. (1.College of Computer and Information Technology,Henan Normal University,Xinxiang 453007;
    2.Engineering Laboratory of Intellectual Business and Internet of Things Technologies,Xinxiang 453007,China)
  • Received:2014-09-12 Revised:2015-03-02 Online:2015-09-25 Published:2015-09-25

摘要:

针对复杂、动态环境中多Agent协作的稳定性问题,提出了一种基于博弈论及惩罚机制的协作方法,通过效用函数来选择最优策略,实现均衡协作;为了提高协作的稳定性与成功率,引入惩罚机制,通过不断调整惩罚系数来维护多Agent协作的稳定性,并在形成协作团队时,充分考虑参与协作的Agent的信誉值。仿真结果表明,该方法能有效地降低任务完成时间,避免Agent在动态协作中随意退出,提高协作效率及协作稳定性。

关键词: 演化博弈, 协作, 惩罚机制, 信誉值, Multi-agent

Abstract:

The coordination stability problem in complex environments is one of the key problems in the research of multiagent cooperation. We present a multiagent cooperation stability method on the basis of game theory methods and punishment mechanism. To maintain the stability of multiagent cooperation and achieve a balanced cooperation, a punishment is introduced and continuous adjustment of the penalty factors is performed. Agent credit values are fully considered when the cooperation team is formed. Simulation results show that the proposal can not only reduce task completion time effectively, but also avoid agent exits in the dynamic cooperation, thus improving the cooperation efficiency and stability.

Key words: evolutionary games, cooperation, punishment mechanism, credit value, multi-agent