Cognitive Filtering of Textual Information ‎Agents Based Implementation

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. We 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. We have adopted ‎agents to improve the response time compared to a sequential algorithmic solution. To validate our filtering approach, we led ‎a set of experiments to evaluate performances of the techniques and tools we have developed