计算机科学 ›› 2010, Vol. 37 ›› Issue (8): 120-123.
• 计算机网络与信息安全 • 上一篇 下一篇
刘哲元,慕德俊,王晓伶
出版日期:
发布日期:
LIU Zhe-yuan,MU De-jun,WANG Xiao
Online:
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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。
关键词: 多智能体系统，经验模型，信誉模型，动态学习机制
Abstract: To accurately evaluate cooperation agents, it's necessary to use the expericnccbased 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.
Key words: MAS,Experience-based model,Reputation-based model,Dynamic learning mechanism
刘哲元,慕德俊,王晓伶. 基于经验和信誉的MAS信任评价的学习机制[J]. 计算机科学, 2010, 37(8): 120-123. https://doi.org/
LIU Zhe-yuan,MU De-jun,WANG Xiao. Learning Mechanism of Trust Evaluation of MAS Based on Experience and Reputation[J]. Computer Science, 2010, 37(8): 120-123. https://doi.org/
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