Addressing Concept Drift in Reputation Assessment

In this paper, we address the limitations of existing methods to select representative data for trust assessment when agent behaviours can change at varying speeds and times across a system. We propose a method that uses concept drift detection to identify and exclude unrepresentative past experiences, and show that our approach is more robust to dynamic agent behaviours.