Supporting Evolution in Learning Information Agents

Learning agents can autonomously improve both knowledge and performances by using learning strategies. Recently, a strategy based on a cloning process has been proposed to obtain more effective recommendations, obtaining advantages for the whole agent community through individual improvements. In particular, users can substitute unsatisfactory agents with others provided of a good reputation and associated with users having similar interests. This approach is able to support an evolutionary behaviour in the community that allows the better agents to predominate over the less productive agents. However, it is user-centric requiring a user's request to clone an agent and, as consequence, changes in the agent population can happen slowly. To speed up this evolutive process, in this paper it is proposed a proactive mechanism where the system autonomously identifies for each user those agents that in the community have a good reputation and share the same interests. The user can check the clones of such suggested agents in order to evaluate their performances and to adopt them. The results of preliminary experiments show significant advantages introduced by the proposed approach