@inproceedings{Kouvaros:2021:10.1007/978-3-030-90870-6_41,
author = {Kouvaros, P and Kyono, T and Leofante, F and Lomuscio, A and Margineantu, D and Osipychev, D and Zheng, Y},
doi = {10.1007/978-3-030-90870-6_41},
pages = {730--740},
publisher = {Springer International Publishing},
title = {Formal analysis of neural network-based systems in the aircraft domain},
url = {http://dx.doi.org/10.1007/978-3-030-90870-6_41},
year = {2021}
}

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TY  - CPAPER
AB  - Neural networks are being increasingly used for efficient decision making in the aircraft domain. Given the safety-critical nature of the applications involved, stringent safety requirements must be met by these networks. In this work we present a formal study of two neural network-based systems developed by Boeing. The Venus verifier is used to analyse the conditions under which these systems can operate safely, or generate counterexamples that show when safety cannot be guaranteed. Our results confirm the applicability of formal verification to the settings considered.
AU  - Kouvaros,P
AU  - Kyono,T
AU  - Leofante,F
AU  - Lomuscio,A
AU  - Margineantu,D
AU  - Osipychev,D
AU  - Zheng,Y
DO  - 10.1007/978-3-030-90870-6_41
EP  - 740
PB  - Springer International Publishing
PY  - 2021///
SN  - 0302-9743
SP  - 730
TI  - Formal analysis of neural network-based systems in the aircraft domain
UR  - http://dx.doi.org/10.1007/978-3-030-90870-6_41
UR  - https://link.springer.com/chapter/10.1007%2F978-3-030-90870-6_41
UR  - http://hdl.handle.net/10044/1/94034
ER  - 

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