# Falsification of Multiple Requirements for Cyber-Physical Systems Using Online Generative Adversarial Networks and Multi-Armed Bandits

arXiv: https://arxiv.org/abs/2205.11057 · HTML: https://mlsanity.com/paper/2205.11057/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 23 May 2022.

## Builds on (most cited first, 6 of 6)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems](https://mlsanity.com/paper/2005.02979/) (2020)
- [RTAMT: Online Robustness Monitors from STL](https://mlsanity.com/paper/2005.11827/) (2020)
- [Online GANs for Automatic Performance Testing](https://mlsanity.com/paper/2104.11069/) (2021)
- [Multi-Armed Bandits for Boolean Connectives in Hybrid System Falsification (Extended Version)](https://mlsanity.com/paper/1905.07549/) (2019)

## Built on by (most cited first, 2 of 2)

- [Requirement falsification for cyber-physical systems using generative models](https://mlsanity.com/paper/2310.20493/) (2023)
- [Learning test generators for cyber-physical systems](https://mlsanity.com/paper/2410.03202/) (2024)

## Abstract (arXiv)

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 varphi1 land cdots land varphin by using a GAN for each requirement varphii 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.