多Agent系统的几种规范生成机制  

Strategy-Selection Rules for Developing Conventions in Multi Agent System

作　　者：王一川[1] 石纯一[1] 

机构地区：[1]清华大学计算机科学与技术系,北京100084

出　　处：《软件学报》2000年第3期342-345,共4页Journal of Software

基　　金：国家自然科学基金!(No.6 97730 2 6 ;6 97330 2 0 )

摘　　要：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.

关 键 词：多AGENT系统 突现行为 规范生成机制 HCR 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]