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Autonomous agents require trust and reputation concepts in order to identify communities of agents with which to interact reliably in ways analogous to humans. This paper defines a class of attacks called witness-based collusion attacks designed to exploit trust and reputation models. Empirical results demonstrate that unidimensional trust models are vulnerable to witness-based collusion attacks while...
Existing fuzzy and neural-fuzzy systems in the literature can be classified into three main categories, i.e. Mamdani, Takagi-Sugeno (T-S) or Tsukamoto systems based on their implemented fuzzy rule structures. Furthermore, depending on the intended modeling objective, there are two main approaches to fuzzy and neural-fuzzy modeling; namely: linguistic fuzzy modeling (LFM) and precise fuzzy modeling...
Museum visitors are being overloaded with increasing amount and variety of information that heavens their burden to locate what is really interesting. Development of personalized service for museum visitors makes a promising effort to alleviate the problem. In this paper, a recommendation framework and the related algorithms are proposed for intelligent museum. Using both the explicit and implicit...
This article proposes and compares different interaction models for reinforcement learning based on multi-agent system. The cooperation during the learning process is crucial to guarantee the convergence to a good policy. The exchange of rewards among the agents during the interaction is a complex task and if it is inadequate it may cause delays in learning or generate unexpected transitions, making...
The advent of pervasive computing puts forward a new challenge for individual information research. With the explosion of information on the Internet, finding information relevant to a user's interest can be a time-consuming and tedious task. User interest learning plays an important role in information personalization. In this paper, a learning approach to acquire and update user interest is proposed...
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