全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

多Agent系统中信任预测的SRL模型

DOI: 10.13190/jbupt.201006.112.lixy, PP. 112-115

Keywords: 多Agent系统,信任模型,Sarsa强化学习

Full-Text   Cite this paper   Add to My Lib

Abstract:

针对多Agent系统(MAS)中信任关系管理的需求,将Sarsa强化学习(SRL)理论应用于构建MAS中基于Agent行为的信任关系预测模型.首先根据Agent之间交互的时间顺序,构建了基于时间戳的行为状态空间结构,然后应用SRL理论,建立了基于直接可信度和反馈可信度相融合的总体信任关系预测模型.新模型充分利用SRL理论较强的动态适应能力,解决了传统预测模型对环境的动态变化适应能力不足的问题.累计误差方面的实验结果表明,与已有模型相比,新模型能显著提高信任决策的准确性.

References

[1]  Sims M, Corkill D, Lesser V. Automated organization design for multi-agent systems [J]. Autonomous Agents and Multi-Agent Systems, 2008, 16(2): 151-185.
[2]  Stefan S, Robert S. Fuzzy trust evaluation and credibility development in multi-agent systems [J]. Applied Soft Computing, 2007, 7(2): 492-505.
[3]  罗鑫, 杨义先, 胡正名. 开放网络环境中信任管理框架 [J]. 北京邮电大学学报, 2009, 32(1): 126-130. Luo Xin, Yang Yixian, Hu Zhengming. Trust-management famework in open network[J]. Journal of Beijing University of Posts and Telecommunications, 2009, 32(1): 126-130.
[4]  Li Xiaoyong , Gui Xiaolin, Tree-trust: a novel and scalable P2P reputation model based on human cognitive psychology[J]. International Journal of Innovative Computing, Information and Control, 2009, 5A(11): 3797-3807.
[5]  李小勇, 桂小林, 毛倩, 等. 基于行为监控的自适应动态信任度测模型 [J]. 计算机学报, 2009, 32(4): 664-674. Li Xiaoyong, Gui Xiaolin, Mao Qian, et al. Adaptive dynamic trust measurement and prediction model based on behavior monitoring[J]. Chinese Journal of Computers, 2009, 32(4): 664-674.
[6]  Andrew G. Recent advances in hierarchical reinforcement learning[J]. Discrete Event Dynamic Systems, 2004, 13(4): 341-379.
[7]  高阳, 陈世福, 陆鑫. 强化学习研究综述[J]. 自动化学报, 2004, 30(1):86-100. GaoYang, Cheng Shifu, Lu Xin. Research on reinforcement learning technology: a review[J]. Acta Automatica Sinica, 2004, 30(1): 86-100.
[8]  Wilensky U. NetLogo version 4.1.1. .
[9]  Liang Zhengqiang , Shi Weisong. Analysis of recommendations on trust inference in open environment[J]. Journal of Performance Evaluation, 2008, 65(2): 99-128.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133