论文标题
对网络物理系统的强大和安全控制器的对抗性学习
Adversarial Learning of Robust and Safe Controllers for Cyber-Physical Systems
论文作者
论文摘要
我们介绍了一种基于学习的新方法，以合成自动网络物理系统的安全和健壮的控制器，同时生成具有挑战性的测试。此过程结合了用于模型验证的形式方法与生成对抗网络。该方法学习了两个神经网络：第一个旨在为控制器生成令人不安的方案，而第二个则旨在实施安全限制。我们在各种案例研究上测试了提出的方法。
We introduce a novel learning-based approach to synthesize safe and robust controllers for autonomous Cyber-Physical Systems and, at the same time, to generate challenging tests. This procedure combines formal methods for model verification with Generative Adversarial Networks. The method learns two Neural Networks: the first one aims at generating troubling scenarios for the controller, while the second one aims at enforcing the safety constraints. We test the proposed method on a variety of case studies.
