Multi-agent learning: How to interact to improve collective results

The evolution from individual to collective learning opens a new dimension of solutions to address problems that appeal for gradual adaptation in dynamic and unpredictable environments. A team of individuals has the potential to outperform any sum of isolated efforts, and that potential is materialized when a good system of interaction is considered. In this paper, we describe two forms of cooperation that allow multi-agent learning: the sharing of partial results obtained during the learning activity, and the social adaptation to the stages of collective learning. We consider different ways of sharing information and different options for social reconfiguration, and apply them to the same learning problem. The results show the effects of cooperation and help to put in perspective important properties of the collective learning activity.