Assurance-based Learning-enabled Cyber-Physical Systems

Recent advances in machine learning led to the appearance of Learning-Enabled Components (LECs) in Cyber-Physical Systems. LECs are being evaluated and used for various, complex functions including perception and control. However, very little tool support is available for design automation in such systems. This paper introduces an integrated toolchain that supports the architectural modeling of CPS with LECs, but also has extensive support for the engineering and integration of LECs, including support for training data collection, LEC training, LEC evaluation and verification, and system software deployment. Additionally, the toolsuite supports the modeling and analysis of safety cases – a critical part of the engineering process for mission and safety critical systems.

Citation: Charles Hartsell, Nagabhushan Mahadevan, Shreyas Ramakrishna, Abhishek Dubey, Theodore Bapty, Taylor Johnson, Xenofon Koutsoukos, Janos Sztipanovits, and Gabor Karsai. 2019. Model-based design for CPS with learning-enabled components. In Proceedings of the Workshop on Design Automation for CPS and IoT (DESTION '19). ACM, New York, NY, USA, 1-9. DOI: https://doi.org/10.1145/3313151.3313166