Model Proficiency in Centralized Multi-Agent Systems: A Performance Study

Autonomous agents are increasingly deployed in dynamic environments where their ability to perform a given task depends on both individual and collective proficiency. While proficiency self-assessment (PSA) has been studied for single agents, its extension to a team of agents remains underexplored. This letter addresses this gap by introducing a framework for team PSA in centralized settings. Specifically, we investigate two metrics for centralized team PSA: the measurement prediction bound (MPB) and the Kolmogorov-Smirnov (KS) statistic. These metrics quantify the in-situ discrepancy between predicted and actual measurements. Then, we use the Kullback-Leibler (KL) divergence as a reference metric. Simulations in a target tracking scenario demonstrate that both MPB and KS metrics accurately capture model mismatches, align with the KL divergence reference, and enable real-time proficiency assessment.