Text-Learning and Related Intelligent Agents: A Survey
IEEE Intelligent Systems
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Information fusion placed over the Data fusion level prepares grounds to gain more perfect and clear results based on uncertain collected information on one subject from different aspects. Nowadays, the need for intelligent systems as personal Meta Search engine capable of supplying user by needed information from great mass of information resources is sensible. More over, the measures taken on this ground have many deficiencies. In a Meta Search engine the user's interests are received and the proper queries based upon them are transmitted to the search engines. Then, the returned results of the search engines become filtered and based on priority they are made available for the user. But, it is obvious that the different search engines have different behavior on different subjects. On the same direction in this study we try to examine a part of a customized intelligent agent which is able to extract behavioral model of search engines from different subjective clusters gradually, and according to the feedback it gets from the user.