Pervasive computing applications involve the interaction between autonomous entities for performing complex tasks and producing knowledge. Autonomous entities can interact to exchange data and knowledge to fulfil applications requirements. Intelligent Agents (IAs) ‘activated’ in various devices offer a lot of advantages when representing such entities due to their autonomous nature that enables them to perform the desired tasks in a distributed way. However, in such open and dynamic environments, IAs should be based on an efficient mechanism for trusting unknown entities when exchanging data. The trust level of an entity should be automatically calculated based on an efficient methodology. Each entity is uncertain for the characteristics and the intentions of the others. Fuzzy Logic (FL) seems to be the appropriate tool for handling such kind of uncertainty. In this paper, we present a model for trust calculation under the principles of FL. Our scheme takes into consideration the social dimension of trust as well as personal experiences of entities before they decide interactions with an IA. The proposed model is a two-level system involving three FL sub-systems to calculate (a) the social trust (based on experiences retrieved by the community), (b) the individual trust (based on personal experiences) and (c) the final trust. We present our results by evaluating the proposed system compared to other models and reveal its significance.

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