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 situationalaware 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 contextspecific scenarios. Finally, we compare Contextual Fitness with another situation-aware trust approach proposed in the literature.
Joana Urbano, Ana Paula Rocha, Eugénio C. O