A Situation-Aware Computational Trust Model for Selecting Partners

Abstract. Trust estimation is a fundamental process in several multi-agent systems domains, from social networks to electronic business sce-narios. However, the majority of current computational trust systems is still too simplistic and is not situation-aware, jeopardizing the accu-racy of the predicted trustworthiness values of agents. In this paper, we address the inclusion of context in the trust management process. We first overview recently proposed situation-aware trust models, all based on the predefinition of similarity measures between situations. Then, we present our computational trust model, and we focus on Contextual Fit-ness, a component of the model that adds a contextual dimensional to existing trust aggregation engines. This is a dynamic and incremental technique that extracts tendencies of behavior from the agents in eval-uation and that does not imply the predefinition of similarity measures between contexts. Finally, we evaluate our trust model and compare it with other trust approaches in an agent-based, open market trading sim-ulation scenario. The results obtained show that our dynamic and incre-mental technique outperforms the other approaches in open and dynamic environments. By analyzing examples derived from the experiments, we show why our technique get better results than situation-aware trust models that are based on predefined similarity measures