多Agent系统的几种规范生成机制

Strategy-Selection Rules for Developing Conventions in Multi Agent System

HCR(highest cumulative reward)是多 agent系统中的一种规范生成机制 ,但在该机制下 ,系统的规范不能随条件的变化而变化 .文章建立了规范的定义 ,分析了规范的稳定性 ,给出了用于规范生成的 HAR(highestaverage reward)和 HRR(highestrecent reward)机制 ,适于规范的演化 ,并比 HCR机制有更好的收敛速度 .

Highest cumulative reward (HCR) is a rule for developing conventions in multi agent systems. But it will keep system maintaining an emerged convention from evolving to more rational ones while conditions of system are developing. In this paper, the notion of conventions is defined, and the stability of them is analyzed. Furthermore, two rules called highest average reward (HAR) and highest recent reward (HRR) are introduced. They both guarantee the evolving process of stable conventions, and the convergence rate of them is better than that of HCR.