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arXiv · 2212.01415

Measuring Competency of Machine Learning Systems and Enforcing Reliability

M. Planer·J. M. Sierchio·for BAE Systems
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Abstract

We explore the impact of environmental conditions on the competency of machine learning agents and how real-time competency assessments improve the reliability of ML agents. We learn a representation of conditions which impact the strategies and performance of the ML agent enabling determination of actions the agent can make to maintain operator expectations in the case of a convolutional neural network that leverages visual imagery to aid in the obstacle avoidance task of a simulated self-driving vehicle.

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M. Planer, J. M. Sierchio, for BAE Systems. 2022-12-02. Measuring Competency of Machine Learning Systems and Enforcing Reliability. https://arxiv.org/abs/2212.01415

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