An approach on merging agents' trust distributions in a Possibilitic Domain

Abstract

In this paper, we propose a novel approach on merging the trust distributions of an explorer agent in its advisors with the trust distributions of the advisor agents in a target agent. These two sets of merged distributions represent the trust of different, yet connected, agents in a multi-agent system. The deduced distribution measures an approximation of the explorer agent's trust in the target agent. The proposed approach can serve as a building block for estimating the trust distribution of an agent of interest in the multi-agent systems, who is accessible indirectly through a set of sequentially connected agents. A common issue of modelling trust is the presence of uncertainty, which arises in scenarios where there is either lack of adequate information or variability in an agent's level of trustworthiness. In order to represent uncertainty, possibility distributions are used to model trust of the agents. © Springer-Verlag Berlin Heidelberg 2013.