MERGING SUCCESSIVE POSSIBILITY DISTRIBUTIONS FOR TRUST ESTIMATION UNDER UNCERTAINTY IN MULTI-AGENT SYSTEMS

In social networks, estimation of the degree of trustworthiness of a target agent through the information acquired from a group of advisor agents, who had direct interactions with the target agent, is challenging. The estimation gets more difficult when, in addition, there is some uncertainty in both advisor and target agents' trust. The uncertainty is tackled when (1) the advisor agents are self-interested and provide misleading accounts of their past experiences with the target agents and (2) the outcome of each interaction between agents is multi-valued. In this paper, we propose a model for such an evaluation where possibility theory is used to address the uncertainty of an agent's trust. The trust model of a target agent is then obtained by iteratively merging the possibility distributions of: (1) the trust of the estimator agent in its advisors, and (2) the trust of the advisor agents in a target agent. Extensive experiments validate the proposed model.