在存在勾结的情况下,提高自我信任在计算社会代理人的声誉方面的有效性
Mariantonia Cotronei1, Sofia Giuffrè1, Attilio Marcianò1
1Department of Information Engineering, Infrastructure and Sustainable Energy (DIIES), University Mediterranea of Reggio Calabria, Feo di Vito, Reggio Calabria 89122, Italy.
International journal of neural systems
|October 8, 2023
概括
本研究介绍了一种基于信任的多代理系统 (MAS) 的新策略,用于检测恶意代理,而不会改变诚实代理的声誉. 与Eigentrust相比,新方法显著减少了错误,提高了社区的有效性.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 网络科学 网络科学
背景情况:
- 基于信任的方法通过减轻恶意行为来增强多代理系统 (MAS).
- 神经网络 (NN) 可能会受到来自代理人的不可靠数据的损害.
- Eigentrust是一个用于检测恶意代理的基准算法,但有缺点.
研究的目的:
- 提出一种新的策略来检测MAS中的恶意代理.
- 引入与恶意代理进行声誉计算的有效性衡量标准.
- 通过恶意代理识别算法来定义量化声誉评分修改的指标.
主要方法:
- 开发了一种基于信任的新策略,用于恶意代理检测.
- 引入了用于声誉计算的新有效性指标.
- 定义了一种定量错误指标,以评估对诚实代理商声誉的影响.
- 在动态的多代理环境中进行数学模拟.
主要成果:
- 拟议的方法有效地检测恶意代理.
- 新战略不会改变诚实代理商的声誉价值.
- 实验模拟显示该方法优于Eigentrust.
- 拟议的方法实现了大约一千倍低于Eigentrust在中型网络上的错误率.
结论:
- 新的战略提供了一种更有效的方法来管理对MAS的信任.
- 这种方法可以保护诚实代理商声誉的完整性.
- 这些发现表明,MAS的稳定性和可靠性得到了显著改善.
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