Abstract
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.
Work partly supported by the DARPA Assured Autonomy programme (FA8750-18-C-0095). The views, opinions and/or findings expressed are those of the author and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. Approved for Public Release, Distribution Unlimited.
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Notes
- 1.
A linearly definable set is a set that can be expressed as a finite set of linear constraints over real-valued and integer variables.
- 2.
Note that Venus does not support the Sigmoid function used in the output layer; we therefore use its inverse to compute the preimage of \(\lambda \) and compare this value with the pre-activation value of the output node.
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Kouvaros, P. et al. (2021). Formal Analysis of Neural Network-Based Systems in the Aircraft Domain. In: Huisman, M., Păsăreanu, C., Zhan, N. (eds) Formal Methods. FM 2021. Lecture Notes in Computer Science(), vol 13047. Springer, Cham. https://doi.org/10.1007/978-3-030-90870-6_41
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