具有可信度的多智能体系统的综合信任管理方法

B. Khosravifar, J. Bentahar, Maziar Gomrokchi, R. Alam
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引用次数: 7

摘要

在多智能体系统中,安全是一个重要的概念,智能体动态地进入和离开系统。已经提出了不同的信任模型,以帮助代理决定是否与服务提供者不知道(或不太了解)的请求者进行交互。为此,本文对基于代理的系统的安全性进行了深入的研究,并将其嵌入到服务提供者的信任评估中。代理是自主软件,配备了先进的通信(使用基于公共对话游戏的协议和如何使用这些协议的私人策略)和推理能力。服务提供者代理获得由可信代理(关于直接交互历史)和裁判代理(以推荐的形式)提供的报告,并结合许多度量,例如交互数量和及时相关性,以提供对特定代理的可能行为的总体估计。请求此代理(称为目标代理)提供它与每个代理的交互次数,服务提供者将惩罚那些谎称拥有信任评估过程信息的代理。此外,经过一段周期后,将目标代理的实际行为与其他代理提供的信息进行比较。这种比较既可以调整信任评估中贡献主体的可信度,又可以通过最小化估计误差来改进系统的信任评估。总体而言,所提出的框架可以帮助代理有效地进行交互代理的信任估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An approach to comprehensive trust management in multi-agent systems with credibility
Security is a substantial concept in multi-agent systems where agents dynamically enter and leave the system. Different models of trust have been proposed to assist agents in deciding whether to interact with requesters who are not known (or not very well known) by the service provider. To this end, in this paper we progress our work on security for agent-based systems, which is embedded in service providerpsilas trust evaluation of the counter part. Agents are autonomous software equipped with advanced communication (using public dialogue game-based protocols and private strategies on how to use these protocols) and reasoning capabilities. The service provider agent obtains reports provided by trustworthy agents (regarding to direct interaction histories) and referee agents (in the form of recommendations) and combines a number of measurements, such as number of interactions and timely relevance, to provide an overall estimation of a particular agentpsilas likely behavior. Requesting this agent, called the target agent, to provide the number of interactions it had with each agent, the service provider penalizes the agents who lied about having information for trust evaluation process. In addition, after a periodic time, the actual behavior of the target agent is compared against the information provided by others. This comparison leads to both adjusting the credibility of the contributing agents in trust evaluation and improving the system trust evaluation by minimizing the estimation error. Overall the proposed framework is shown to assist agents effectively perform the trust estimation of interacting agents.
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