Model Proficiency in Centralized Multi-Agent Systems: A Performance Study
Abstract
Autonomous agents are increasingly deployed in dynamic environments where their ability to perform a given task depends on both individual and team-level 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 presenting a framework for team PSA in centralized settings. We investigate three metrics for centralized team PSA: the measurement prediction bound (MPB), the Kolmogorov-Smirnov (KS) statistic, and the Kullback-Leibler (KL) divergence. These metrics quantify the discrepancy between predicted and actual measurements. We use the KL divergence as a reference metric since it compares the true and predictive distributions, whereas the MPB and KS provide efficient indicators for in situ assessment. Simulation results 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.
Cite
@article{arxiv.2510.23447,
title = {Model Proficiency in Centralized Multi-Agent Systems: A Performance Study},
author = {Anna Guerra and Francesco Guidi and Pau Closas and Davide Dardari and Petar M. Djuric},
journal= {arXiv preprint arXiv:2510.23447},
year = {2025}
}