An Adaptive Witness Selection Method for Reputation-Based Trust Models

Abstract

In distributed multi-agent systems where agents need to cooperate with each other in order to thrive, accurately estimating a potential partner’s trustworthiness is vital to an agent’s wellbeing. Many distributed reputation-based agent trust models have been proposed. It has been agreed that testimony sharing is a useful way for agents to gain knowledge about the reputation of potential interaction partners without having to expose themselves to the risk of actually interacting with them. However, the presence of unfair testimonies adversely affects an agent’s long term wellbeing and has been an important problem in agent trust research. Many testimony filtering methods have been proposed, but they often rely on assumptions about the characteristics of the witness agent population or supporting facilities in the environment. In addition, some methods involve highly iterative approaches that consume excessive amount of computational resources.

In this paper, we propose a witness selection method, based on the principles of reinforcement learning, for distributed reputation-based agent trust models. The proposed method innovatively formulates the witness selection problem to allow reinforcement learning technique to be leveraged to solve it without making limiting assumptions on the characteristics of the witness agent population or the existence of supporting facilities. Extensive simulations have shown that it significantly outperforms existing approaches in mitigating the adverse effect of unfair testimonies, especially under conditions where trustee agents and witness agents strategically collude to boost their chances of being selected for interaction by unsuspecting truster agents.