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A trust-based service suggestion system using human plausible reasoning

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Abstract

Nowadays, there is a growing need to manage trust in open systems as they may contain untrustworthy service providers. Agent Trust Management (ATM) tries to address the problem of finding a set of the most trusted agents in multi agent systems. This paper presents ScubAA, a novel generic ATM framework based on the theory of Human Plausible Reasoning (HPR). For each user’s request, ScubAA determines a ranked list of the most trusted service agents, within the context of the request, and forwards the request to those trusted services only. ScubAA determines an agent’s degree of trust in terms of a single personalized value derived from several types of evidences such as user’s feedback, history of user’s interactions, context of the submitted request, references from third party users as well as from third party service agents, and structure of the society of agents. ScubAA is able to utilize more trust evidences towards a more accurate value of trust. We also propose a function to figure out how similar two users are in a given context. We apply the proposed HPR-based ATM framework to the domain of Web search. The resulting ATM system recommends to the user a list of the most trusted search engines ranked according to the retrieval precision of documents returned in response to the user’s query as well as the degree of trust of the search engines have gained by interacting with other related users within the context of the query. In addition, we conduct a statistical analysis of ScubAA based on ANOVA and by using a data set of forty queries in different domains. This analysis clearly reveals that ScubAA is able to successfully assess the trustworthiness of service agents.

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  1. University of Regina, Regina, SK, Canada

    Sadra Abedinzadeh & Samira Sadaoui

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  1. Sadra Abedinzadeh
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  2. Samira Sadaoui
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Correspondence to Samira Sadaoui.

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Abedinzadeh, S., Sadaoui, S. A trust-based service suggestion system using human plausible reasoning. Appl Intell 41, 55–75 (2014). https://doi.org/10.1007/s10489-013-0495-8

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