An Improved Familiarity Measurement for Formalization of Trust in E-commerce Based Multiagent Systems

Speaker: Jie Zhang

Familiarity between agents is often considered to be an important factor in determining the level of trust. In electronic marketplaces, trust is modeled, for instance, in order to allow buying agents to make effective selection of selling agents. In previous research, familiarity between two agents has been simply assumed to be the similarity between them, which is fixed for the two agents. We propose an improved familiarity measurement based on the exploration of factors that affect a human's feelings of familiarity and the mapping from those factors to the properties of agent societies. We examine the trust model in the context of a multiagent system within an e-commerce framework. We also carry out experiments to compare the stability of the system using the trust model with the improved familiarity measurement and that with the fixed familiarity values. Experimental results show that the stability of the system is increased by 33.47% through the improved familiarity measurement.