Social Entropy: a New Metric for Learning Multi-Robot Teams

Abstract

As robotics research expands into multiagent tasks and learning, investigators need new tools for evaluating the artificial robot societies they study. Is it enough, for example, just to say a team is "heterogeneous?" Perhaps heterogeneity is more properly viewed on a sliding scale. To address these issues this paper presents new metrics for learning robot teams. The metrics evaluate diversity in societies of mechanically similar but behaviorally heterogeneous agents. Behavior is an especially important dimension of diversity in learning teams since, as they learn, agents choose between hetero- or homogeneity based solely on their behavior. This paper introduces metrics of behavioral difference and behavioral diversity. Behavioral difference refers to disparity between two specific agents, while diversity is a measure of an entire society. Social Entropy, inspired by Shannon's Information Entropy [5], is proposed as a metric of behavioral diversity. It captures important components of diversity including the number and size of castes in a society. The new metrics are illustrated in the evaluation of an example learning robot soccer team.