arxiv:2212.01415

Measuring Competency of Machine Learning Systems and Enforcing Reliability

Published on Dec 2, 2022

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Abstract

Convolutional neural networks leveraging visual imagery for obstacle avoidance in simulated self-driving vehicles can adapt their strategies based on environmental condition representations to maintain performance reliability.

AI-generated summary

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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