zTrust: adaptive decentralized trust model for QoS selection in electronic marketplaces

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University of New Brunswick


In the absence of legal enforcement procedures for the participants of an open e-marketplace, trust and reputation systems are central to resisting threats from malicious agents. Such systems also provide mechanisms for identifying participants who disseminate unfair ratings. This thesis research is in the area of modeling trust in multi-agent systems where self-interested agents intelligently interact to maximize their benefits. The thesis presents an adaptive intelligent trust model, called zTrust, with multidisciplinary approaches well-suited for cooperative and competitive electronic commerce in such a way that individuals could make optimal decisions in selecting business transaction partners. zTrust has two essential elements: 1) a two-layered filtering algorithm, the Prob-Cog model, with an adaptive threshold evaluation procedure, which combines cognitive and probabilistic views of trust to classify participants through modelling their subjectivity, behavioural characteristics and environmental conditions in order to detect unfair advisers, and 2) a two-layered Trust-Oriented Service Selection (TOSS) framework to assist consumers in discovering providers who maximize their utility by achieving sufficient trust and fulfilling consumers' preferences on product quality. We also propose a trust-oriented mechanism built on a game-theoretic basis well-suited for competitive e-marketplaces where providers might have limited inventory. The characteristics of such an environment make consumers concerned with the possibility of losing the opportunity to do business with good providers whilst providing truthful reputation information about providers. The proposed mechanism provides consumers with a means to strategically determine their reporting behaviour by establishing a balance between the possibility of losing business opportunities because of truthful reporting and the possibility of not receiving truthful provider information from advisers if the consumers report untruthfully. We provide a series of experimental results in a simulated dynamic environment where agents may be arriving and departing. We illustrate the efficacy and the robustness of our approaches in important conditions of the cooperative and competitive marketplaces, as well as in comparison with other approaches. We observe that zTrust enables consumers to access more accurate information, even in a hostile environment, and enjoy high profits as a result of interacting with high-quality providers. Furthermore, our mechanism promotes honesty amongst providers as dishonest providers would be isolated and would lose the opportunity to be selected as transaction partners.