TRUST MANAGEMENT THROUGH FUZZY REPUTATION

Abstract

Open electronic communities may bring together people geographically and culturally unrelated to each other. In this context, taking costly decisions depends on the expectations created according to past behaviour of others. This kind of information is usually called reputation and it is one of the most significant factors to trust merchants and recommenders in electronic commerce interactions. When agents are acting on behalf of humans in such commercial scenarios, they should represent and reason about trust and reputation as humans do. In this paper a trust management mechanism tackles the vague, subjective and uncertain information about others using fuzzy sets. The operations defined over such fuzzy sets updates the reputation of merchants according to the general situation faced. This trust management mechanism is applied to a multiagent system of merchants, recommenders and buyers, where collaborative recommendations coexist with competitive intentions. The developed multi-agent system is used to compare the level of success of predictions obtained from the fuzzy computations with some of the most well known (crisp) reputation mechanisms: ebay, bizrate, sporas and regret when the behaviour of merchants change in different degrees. Finally, the potential benefits of using fuzzy sets to manage reputation in multi-agent systems are analyzed according to the excellent experimental results shown.