| 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(highest average reward)和HRR(highest recent reward)机制,适于规范的演化,并比HCR机制有更好的收敛速度. |
| 关键词: 多agent系统,协调,规范,突现行为,演化. |
| DOI： |
| 分类号: |
| 基金项目:本文研究得到国家自然科学基金(No.69773026,69733020)资助. |
|
| Strategy-Selection Rules for Developing Conventions in Multi-Agent System |
|
WANG Yi-chuan,SHI Chun-yi
|
| Abstract: |
| 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. |
| Key words: Multi-Agent system,coordination,convention,emergent behavior,evolution. |
