Measuring Competency of Machine Learning Systems and Enforcing Reliability

• URL: http://arxiv.org/abs/2212.01415v1
• Date: Fri, 2 Dec 2022 19:37:37 GMT
• Title: Measuring Competency of Machine Learning Systems and Enforcing Reliability
• Authors: M. Planer, J. M. Sierchio, for BAE Systems
• Abstract summary: We explore the impact of environmental conditions on the competency of machine learning agents. We learn a representation of conditions which impact the strategies and performance of the ML agent.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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