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

2025-10-27

AI Summary

This work addresses the absence of team-level proficiency self-assessment (PSA) in centralized multi-agent systems, introducing the first extension of proficiency self-assessment from single agents to collaborative multi-agent teams. We propose a quantitative framework grounded in Measurement Prediction Bound (MPB), Kolmogorov–Smirnov (KS) test, and Kullback–Leibler (KL) divergence: MPB and KS serve as lightweight, online-computable real-time indicators, while KL divergence provides a theoretically grounded reference for model mismatch. The framework enables precise, interpretable monitoring of team-level model fidelity. Evaluated in a target tracking simulation environment, MPB and KS demonstrate strong agreement with the KL benchmark, supporting robust, real-time team proficiency assessment. This study bridges a critical gap in multi-agent PSA research by establishing a principled, scalable, and operationally viable approach to distributed team self-evaluation.

Technology Category

• Multiagent Systems: Teamwork
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

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.

Problem

• Extending proficiency self-assessment from single to multi-agent systems
• Evaluating three metrics for team proficiency in centralized settings
• Validating real-time assessment methods for model mismatch detection

Innovation

• Framework for team proficiency self-assessment in centralized systems
• Three metrics quantify predicted versus actual measurement discrepancies
• MPB and KS metrics enable real-time model mismatch assessment