Trust Estimation Using Contextual Fitness

Trust estimation is an essential process in several multi-agent systems domains. Although it is generally accepted that trust is situational, the majority of the Computational Trust and Reputation (CTR) systems existing today are not situation-aware. In this paper, we address the inclusion of the context in the trust management process. We first refer the benefits of considering context and make an overview of recently proposed situational-aware trust models. Then, we propose Contextual Fitness, a CTR component that brings context into the loop of trust management. We empirically show that this component optimizes the estimation of trustworthiness values in context-specific scenarios. Finally, we compare Contextual Fitness with another situation-aware trust approach proposed in the literature.

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