A new similarity difference measure in multi agent systems

In this paper, we present a new measure for evaluating similarity changes in a multiagent system. The similarity measure of the agents changes during the learning process. The similarity differences are because of any composition or decomposition of some agent sets. The presented measure, defines the changes of homogeneity of agents by composition and decomposition. The utility of the metrics is demonstrated in the experimental evaluation of multiagent foraging. The results show that while the similarity difference gets a positive value, the performance grow rapidly.