一种动态博弈的多agent合作机制模型

利用博弈学习思想,以多agent系统为平台,提出一种基于动态无限博弈的多agent合作机制模型,以多阶段邀请、考核模式形成无限次重复博弈结构体.提出信任基准测度评价控制agent博弈选取的盲目性,使其理性计划各阶段决策.通过博弈结果反馈信息,动态调整agent博弈主体的收益函数,控制各agent间的协同合作优先级,实现闭环调控.将多agent组合构成智能结构体的基本单元,利用九宫格实验对该基本单元进行试验测试,验证多工况下,基于多种信任基准条件agent单元体间的协同合作机制.实验表明,信任基准可有效调整agent间的信任等级,促进系统中agent合作频率的提高.

Multi-agent Cooperation Mechanism Model Based on Dynamic Game

A multi-agent cooperation mechanism based on dynamic infinite game model was proposed in a multi-agent platform. The infinitely repeated game structure was formed with multi-stage inviting and evaluating actions. Meanwhile， the trust benchmark was proposed to control agent blindness in selection phase and to make sure it could do the rational stage decision. Through the feedback， the multi-agent cooperation mechanism could not only dynamically adjust the income function of each game-agent， but also control cooperation priority. Also， it could achieve a closed-loop control. Additionally， multi-agent were assembled as a basic component unit of intelligent structure. Nine grids experiment was used to validate cooperation mechanism between multi-agent under multiple loading conditions. Moreover， the collaboration status of the agent components unit was also tested based on different trust benchmark. Experiments show that， trust benchmark can effectively adjust the trust level between agents， and promote cooperation in the high frequency agent system.