EVA: An Evolutionary Approach to Mutual Monitoring of Learning Information Agents

A learning agent system is composed of agents able to autonomously enrich their knowledge and improve their performances, using learning strategies. The idea underlying this paper is that individual improvements obtained by the learning capabilities of an agent should be exploited to advantage the other agents, and a natural way of obtaining such a result is represented by evolutionary processes. However, the biological evolutionary mechanisms are often too complex to be reproduced in a software environment. In this context, we argue the cloning, due to its very simple mechanism of reproduction, can be usefully used. In our approach, a user in a virtual community can substitute an unsatisfactory agent cloning an existing agent having both similar interests and a good reputation in the community. This mechanism induces an evolutionary process in the community, such that the less satisfactory agents are replaced by more effective agents. The key issue of this proposal is that of suitably selecting the agent to be cloned in presence of a user's request, and to this purpose we propose an evolutionary model of reputation. Our evolutionary approach has been implemented on the top of a leaning agent-based recommender system, and a number of experiments show that this novel strategy introduces significant improvements in the effectiveness of the recommendations.