Comparison-based agent partitioning with learning automata: A trust model for service-oriented environments

Service oriented environments which consist of interconnected service providers and service consumers are filled by vast numbers of services of similar functionalities and differing qualities. Finding the desirable services among others is a major problem for a typical user, who can optimize its performance by utilizing services with good qualities. This problem is sometimes addressed by relying on the votes and advices about qualities of services collected from other agents in the environment. However, there is no guarantee that all agents give fair advices about all services. The presence of unfair or malicious agents, who tend to misinform others about the quality of services, makes it necessary to develop methods for distinguishing fair and unfair agents from each other. This should be done according to the previous behavior of agents that represents their reputation and trustworthiness. Among different schemes for doing so are methods based on learning automata for partitioning user-agents to fair and unfair groups based on their previous votes on services available in the environment. Here we propose a trust model in sophisticated service-oriented environments with a simple learning automata-based method for partitioning fair/unfair objects with improved performance and reliable service selection efficiency.