A Rough Sets-based Agent Trust Management Framework

Abstract

In a virtual society, which consists of several autonomous agents, trust helps agents to deal with the openness of the system by identifying the best agents capable of performing a specific task, or achieving a special goal. In this paper, we introduce ROSTAM, a new approach for agent trust management based on the theory of Rough Sets. ROSTAM is a generic trust management framework that can be applied to any types of multi agent systems. However, the features of the application domain must be provided to ROSTAM. These features form the trust attributes. By collecting the values for these attributes, ROSTAM is able to generate a set of trust rules by employing the theory of Rough Sets. ROSTAM then uses the trust rules to extract the set of the most trusted agents and forwards the user's request to those agents only. After getting the results, the user must rate the interaction with each trusted agent. The rating values are subsequently utilized for updating the trust rules. We applied ROSTAM to the domain of cross-language Web search. The resulting Web search system recommends to the user the set of the most trusted pairs of translator and search engine in terms of the pairs that return the results with the highest precision of retrieval.