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Title: Sherlock - A tool for verification of neural network feedback systems: demo abstract
We present an approach for the synthesis and verification of neural network controllers for closed loop dynamical systems, modelled as an ordinary differential equation. Feedforward neural networks are ubiquitous when it comes to approximating functions, especially in the machine learning literature. The proposed verification technique tries to construct an over-approximation of the system trajectories using a combination of tools, such as, Sherlock and Flow*. In addition to computing reach sets, we incorporate counter examples or bad traces into the synthesis phase of the controller as well. We go back and forth between verification and counter example generation until the system outputs a fully verified controller, or the training fails to terminate in a neural network which is compliant with the desired specifications. We demonstrate the effectiveness of our approach over a suite of benchmarks ranging from 2 to 17 variables.
Authors:
; ; ; ;
Award ID(s):
1740079 1750009
Publication Date:
NSF-PAR ID:
10119090
Journal Name:
AAAI Symposium on Verification of Neural Networks
Volume:
2019
Page Range or eLocation-ID:
262
Sponsoring Org:
National Science Foundation
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