基于主观概率的复合服务全局信任评价演绎方法

Lei Li, Yan Wang
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引用次数: 34

摘要

在面向服务的计算(SOC)环境中,当服务客户机从大量服务提供者池中选择一个服务提供者时,每个服务提供者的可信度是至关重要的。服务提供者的信任值通常在[0,1]的范围内,由服务客户给出的评分来评估,它代表了服务客户对所提供服务的满意度的主观信念。因此,信任值可以理解为一种主观概率,即一方相信另一方在某种情况下可以执行某一行为的概率。因此,在信任评价中应采用主观概率论。此外,在SOC环境中,服务提供者通常可以调用来自其他服务提供者的服务,从而形成组合服务。因此,复合服务的全局信任应该基于信任的主观概率属性和复杂的调用结构来评估。在本文中,我们首先将直接服务调用引起的信任依赖解释为条件概率。然后,在信任依赖的基础上,我们提出了一种基于主观概率的演绎(SELECTIVE)方法来评估组合服务的主观全局可信度。所有这些过程都遵循主观概率论,保持了信任在评价中的主观概率性质。实验结果表明,与现有方法相比,我们提出的选择性方法可以得到更合理的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Subjective Probability Based Deductive Approach to Global Trust Evaluation in Composite Services
In Service-Oriented Computing (SOC) environments, the trustworthiness of each service provider is critical for a service client when selecting one from a large pool of service providers. The trust value of a service provider is usually in the range of [0, 1] and is evaluated from the ratings given by service clients, which represent the subjective belief of service clients on the satisfaction of delivered services. So a trust value can be taken as a subjective probability, by which one party believes that another party can perform an action in a certain situation. Hence, subjective probability theory should be adopted in trust evaluation. In addition, in SOC environments, a service provider usually can invoke the services from other service providers forming a composite service. Thus, the global trust of a composite service should be evaluated based on both the subjective probability property of trust and complex invocation structures. In this paper, we first interpret the trust dependency caused by direct service invocations as conditional probability. Then, on the basis of trust dependency, we propose a Subjective probability based deductive (SELECTIVE) approach to evaluate the subjective global trustworthiness of a composite service. All these processes follow subjective probability theory and keep the subjective probability property of trust in evaluations. Our experimental results demonstrate that when compared with existing approaches our proposed SELECTIVE approach can yield more reasonable results.
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