Verification for Machine Learning, Autonomy, and Neural Networks Survey
Artificial Intelligence
2018-10-05 v1 Machine Learning
Abstract
This survey presents an overview of verification techniques for autonomous systems, with a focus on safety-critical autonomous cyber-physical systems (CPS) and subcomponents thereof. Autonomy in CPS is enabling by recent advances in artificial intelligence (AI) and machine learning (ML) through approaches such as deep neural networks (DNNs), embedded in so-called learning enabled components (LECs) that accomplish tasks from classification to control. Recently, the formal methods and formal verification community has developed methods to characterize behaviors in these LECs with eventual goals of formally verifying specifications for LECs, and this article presents a survey of many of these recent approaches.
Cite
@article{arxiv.1810.01989,
title = {Verification for Machine Learning, Autonomy, and Neural Networks Survey},
author = {Weiming Xiang and Patrick Musau and Ayana A. Wild and Diego Manzanas Lopez and Nathaniel Hamilton and Xiaodong Yang and Joel Rosenfeld and Taylor T. Johnson},
journal= {arXiv preprint arXiv:1810.01989},
year = {2018}
}