Probabilistic Trust Evaluation with Inaccurate Reputation Reports

We are concerned with the problem of trust evaluation in the generic context of large scale open-ended systems. In such systems the truster agents have to interact with other trustee peers to achieve their goals, while the trustees may not behave as required in practice. The truster therefore has to predict the behaviors of potential trustees to identify reliable ones, based on past interaction experience. Due to the size of the system, often there is little or no past interaction between the truster and the trustee, that is, the case wherein the truster should resort to the third party agents, termed advisors here, inquiring about the reputation of the trustee. The problem is complicated by the possibility that the advisors may deliberately provide inaccurate and even misleading reputation reports to the truster. To this end, we develop techniques to take account of inaccurate reputations in modeling the behaviors of the trustee based on the Bayesian formalism. The core of the techniques is a proposed notion, termed Advisor-to-Truster relevance measure, based on which the incorrect reputation reports are rectified for use in the trust evaluation process. The benefit induced by the proposed techniques is verified by simulated experiments.