摘要
为合理利用MAS中存在的经验和信誉两种信任评价资源,准确评价合作agent,提出了根据活动因子的学习结果动态评价MAS的学习机制。采用该机制,MAS根据活动因子的取值赋予不同信任资源以不同权值,动态计算可信度,评价合作目标,使得MAS取得的总体报酬最优。仿真结果验证了学习机制的有效性。
To accurately evaluate cooperation agents, it ~ s necessary to use the experience-based and reputation-based models existed in MAS. Therefore, a learning mechanism of MAS according to the learning result of active parameter was proposed. MAS using this mechanism gives different trust model the corresponding weight, dynamically calculates the reliability and evaluates the cooperation objects to make the optimal rewards. Simulation shows the efficiency of the learning mechanism.
出处
《计算机科学》
CSCD
北大核心
2010年第8期120-123,共4页
Computer Science
关键词
多智能体系统
经验模型
信誉模型
动态学习机制
MAS, Experience-based model, Reputation-based model, Dynamic learning mechanism