arXiv Open Access 2022

Measuring Competency of Machine Learning Systems and Enforcing Reliability

M. Planer J. M. Sierchio for BAE Systems

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Abstrak

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.

Topik & Kata Kunci

cs.LG

Penulis (3)

M. Planer

J. M. Sierchio

for BAE Systems

Format Sitasi

APA MLA BibTeX

Planer, M., Sierchio, J.M., Systems, f.B. (2022). Measuring Competency of Machine Learning Systems and Enforcing Reliability. https://arxiv.org/abs/2212.01415

Akses Cepat

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Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓