Personalizing information retrieval with multi-agent systems

In this paper we evaluate the performance of MAIS, a multi-agent system for searching the Internet. The search for interesting documents on the Internet is becoming more and more difficult. Inference engines are largely outperformed by the expansion of the Internet and quite often the user is not satisfied with the results of a search. We believe that multi-agent systems can help to improve the user's satisfaction because in a multi-agent system (MAS) agents can be specialized in different tasks (search or filtering) and can personalize information. We implemented such an approach using an open MAS containing personal assistants, library agents, filter agents and search agents. In this paper we study the elements of our MAS and compare the performance of the system with traditional search engines taking the viewpoint of the user. Our results are much more accurate than standard search engines.