Assessing Model Proficiency in Autonomous Agents: A Signal Processing Perspective

Autonomous agents play a crucial role in applications such as emergency response and urban security, where they are often required to operate independently in critical situations without direct human supervision. A key aspect of autonomy is the agent's ability to assess its proficiency in carrying out tasks and making decisions based on this assessment. This paper introduces a state-space model to present a novel framework for assessing the proficiency of autonomous agents. The proposed metric is based on statistical model assessment, enabling agents to evaluate their model performance in real-time. More specifically, we focus on the proficiency of the model used for measurement predictions. We validate the effectiveness of our metric through simulations with synthetic data. Future work will explore the potential of this framework to enhance decision-making accuracy and improve task performance.