Model Proficiency in Centralized Multi-Agent Systems: A Performance Study
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909871252701184 |
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| author | Guerra, Anna Guidi, Francesco Closas, Pau Dardari, Davide Djuric, Petar M. |
| author_facet | Guerra, Anna Guidi, Francesco Closas, Pau Dardari, Davide Djuric, Petar M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23447 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Model Proficiency in Centralized Multi-Agent Systems: A Performance Study Guerra, Anna Guidi, Francesco Closas, Pau Dardari, Davide Djuric, Petar M. Applications Multiagent Systems 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. |
| title | Model Proficiency in Centralized Multi-Agent Systems: A Performance Study |
| topic | Applications Multiagent Systems |
| url | https://arxiv.org/abs/2510.23447 |