论文标题
使用在线生成对抗网络和多臂匪徒伪造网络物理系统的多种要求
Falsification of Multiple Requirements for Cyber-Physical Systems Using Online Generative Adversarial Networks and Multi-Armed Bandits
论文作者
论文摘要
我们考虑了信号时间逻辑（STL）表达的网络物理系统的伪造安全要求的问题。通过STL鲁棒性函数可以将此问题转变为优化问题。在本文中，我们的重点是伪造有多个要求的系统。我们建议使用在线生成对抗网络（GAN）作为测试生成器来解决此类连接性要求。我们的主要贡献是一种算法，该算法通过使用gan分别使用gan来分别使用gan分别使用gan来分别使用gan伪造$φ_1\ land \ cdots \ cdots \ landφ_n$。使用来自多臂强盗算法的想法，我们的算法在每个步骤都只能训练一个gan，从而节省资源。我们的实验表明，除了节省资源外，这种多武器的匪徒算法可以伪造要求，而与（i）算法培训单个GAN相比，测试系统上的执行次数较少，单个GAN单个GAN以完成完整的结合需求，并且（II）在每个步骤上总是训练$ n $ gans。
We consider the problem of falsifying safety requirements of Cyber-Physical Systems expressed in signal temporal logic (STL). This problem can be turned into an optimization problem via STL robustness functions. In this paper, our focus is in falsifying systems with multiple requirements. We propose to solve such conjunctive requirements using online generative adversarial networks (GANs) as test generators. Our main contribution is an algorithm which falsifies a conjunctive requirement $φ_1 \land \cdots \land φ_n$ by using a GAN for each requirement $φ_i$ separately. Using ideas from multi-armed bandit algorithms, our algorithm only trains a single GAN at every step, which saves resources. Our experiments indicate that, in addition to saving resources, this multi-armed bandit algorithm can falsify requirements with fewer number of executions on the system under test when compared to (i) an algorithm training a single GAN for the complete conjunctive requirement and (ii) an algorithm always training $n$ GANs at each step.
