The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Verisig: verifying safety properties of hybrid systems with neural network controllers
Abstract
This paper presents Verisig, a hybrid system approach to verifying safety properties of closed-loop systems using neural networks as controllers. We focus on sigmoid-based networks and exploit the fact that the sigmoid is the solution to a quadratic differential equation, which allows us to transform the neural network into an equivalent hybrid system. By composing the network's hybrid system with the plant's, we transform the problem into a hybrid system verification problem which can be solved using state-of-the-art reachability tools. We show that reachability is decidable for networks with one hidden layer and decidable for general networks if Schanuel's conjecture is true. We evaluate the applicability and scalability of Verisig in two case studies, one from reinforcement learning and one in which the neural network is used to approximate a model predictive controller.
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Cites
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Understanding deep learning requires rethinking generalization
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Cited by
Case study: verifying the safety of an autonomous racing car with a neural network controller
Ivanov, R., Carpenter, T. J., Weimer, J., Alur, R., Pappas, G. J., & Lee, I. (2020) · Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control · doi:10.1145/3365365.3382216 · Cited by 68
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Metadata
- Authors
- Ivanov, R., Weimer, J., Alur, R., Pappas, G. J., & Lee, I.
- Venue
- Proceedings of the 22nd ACM International Conference on Hybrid Systems: Computation and Control
- Publisher
- ACM
- Published
- 2019-04-16
- Pages
- 169-178
- OpenAlex
- W2964130946
- Cited by
- 216
- References
- 8
- FWCI
- 32.06
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Ivanov, R., Weimer, J., Alur, R., Pappas, G. J., & Lee, I. (2019). Verisig: verifying safety properties of hybrid systems with neural network controllers. Proceedings of the 22nd ACM International Conference on Hybrid Systems: Computation and Control. https://doi.org/10.1145/3302504.3311806
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