A measure of heterogeneity in multi-agent systems

Heterogeneous multi-agent systems have previously been studied and deployed to solve a number of different tasks. Despite this, we still lack a basic understanding of just what “heterogeneity” really is. For example, what makes one team of agents more heterogeneous than another? In this paper, we address this issue by proposing a measure of heterogeneity. This measure takes both the complexity and disparity of a system into account by combining different notions of entropy. The result is a formulation that is both easily computable and makes intuitive sense. An overview is given of existing metrics for diversity found in various fields such as biology, economics, as well as robotics, followed by a discussion of their relative merits and demerits. We show how our proposed measure of heterogeneity overcomes problematic issues identified across the previous metrics. Finally, we discuss how to apply the new measure of heterogeneity specifically to multi-agent systems by using the notion of a common task-space to compare agents with different capabilities.