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The study presented in this paper has multiple objectives. The first objective is to automate the information filtering process by taking into account the relative importance of information and resources needed for its treatment. The second one is to demonstrate the applicability and contribution of an agents based implementation to automatic information filtering. The third one is to show how learning can improve the effectiveness of filtering and that automatic learning is necessary in the design of automatic information filtering systems. The authors propose an open, dynamic and evolving solution that offers to the filtering process the opportunity to learn, exploit the learned knowledge and adapt itself to the application environment.
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