Architecture design of an intelligent information fusion agent for information retrieval from dynamic environments

Nowadays, the need for intelligent software systems as personal meta search engines which are capable of supplying user by needed information from massive information resources is sensible. Moreover, the current tools have many deficiencies. In a meta search engine, proper queries based upon user's interests are sent to different search engines (or in general, information servers). Then, the returned results are refined and made available for the user based on their priorities. On the same direction in this study we try to design, implement and examine a complete architecture for a customized software intelligent agent which is able to retrieve information from multiple sources based on user interests. The designed architecture is able to use information fusion methods to achieve this goal. Here, the OWA, which is a member of the fuzzy integral operators family, has been used. This agent performs fusion in data, feature, and decision levels. Also the agent is able to extract the behavioral model of each information server against various subjective clusters to increase the intelligence of searching methods and obtain higher quality results in searching process.