By Yu Zhang (auth.), Ziad Kobti, Dan Wu (eds.)
This e-book constitutes the refereed complaints of the 20 th convention of the Canadian Society for Computational reports of Intelligence, Canadian AI 2007, held in Montreal, Canada, in might 2007.
The forty six revised complete papers offered have been conscientiously reviewed and chosen from 260 submissions. The papers are prepared in topical part on brokers, bioinformatics, class, constraint delight, info mining, wisdom illustration and reasoning, studying, traditional language, and planning.
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Additional resources for Advances in Artificial Intelligence: 20th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2007, Montreal, Canada, May 28-30, 2007. Proceedings
Moreover, we also achieve an efficiency of distributed computing for building item-item similarity model by employing trust-based collaborative filtering scheme which is added some useful functionality of mobile agents. However, there still remains certain issue: trust decay. It means that the trust relationship becomes weaker as it forwards to its successors [2, 3]. In our system, although a mobile agent finds trust neighbors who are in the closest-distance from a model owner first, the neighbors, even on the different levels, are regarded as the same.
4. Comparison of robustness on manipulated users In accordance with increases in the number of manipulated users, we find that the proposed method showed better results than random model building in both measurements. Fig. 4 illustrates that the proposed method significantly outperforms others in resistance against the profile injection attack. 0639. Efficiency of similarity model building. Finally, we focused on the time required for model building. Forwarded along the path, a mobile agent only sends computed results to the personal agent of the model owner by the proposed methods whereas in the random scheme request/response messages have to be exchanged between the model owner and each random user for similarity model building.
C Springer-Verlag Berlin Heidelberg 2007 Competition and Coordination in Stochastic Games 27 be used to model inter-agent interactions in such environments. n ), where n is the number of agents, S is the set of states s ∈ S now represented as vectors, Aj is the set of actions aj ∈ Aj available to agent j, A is the joint action space A1 × . . × An , T is the transition function: S × A × S → [0, 1], Rj is the reward function for agent j: S × A → R and s0 ∈ S is the initial state. Further, in the article we will refer to a game theoretic terminology, therefore let’s introduce some useful notions of the Game Theory here.
Advances in Artificial Intelligence: 20th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2007, Montreal, Canada, May 28-30, 2007. Proceedings by Yu Zhang (auth.), Ziad Kobti, Dan Wu (eds.)