An evolutionary model for constructing robust trust networks

In reputation systems for multiagent-based e-marketplaces, buying agents model the reputation of selling agents based on ratings shared by other buyers (called advisors). With the existence of unfair rating attacks from dishonest advisors, the effectiveness of reputation systems thus heavily relies on whether buyers can accurately determine which advisors to include in trust networks and their trustworthiness. In this paper, we propose a novel multiagent evolutionary trust model (MET) where each buyer evolves its trust network. In each generation, each buyer acquires trust network information from its advisors and generates a candidate trust network using evolutionary operators. Only trust networks providing more accurate seller reputation estimation shall survive to the next generation. Experimental results demonstrate MET is more robust than the state-ofthe-art trust models against various unfair rating attacks.