# Multi-Armed Bandits for Boolean Connectives in Hybrid System Falsification (Extended Version)

arXiv: https://arxiv.org/abs/1905.07549 · HTML: https://mlsanity.com/paper/1905.07549/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2019.

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

- [Reinforcement Learning with Formal Performance Metrics for Quadcopter Attitude Control under Non-nominal Contexts](https://mlsanity.com/paper/2107.12942/) (2021)
- [Falsification of Multiple Requirements for Cyber-Physical Systems Using Online Generative Adversarial Networks and Multi-Armed Bandits](https://mlsanity.com/paper/2205.11057/) (2022)

## Abstract (arXiv)

Hybrid system falsification is an actively studied topic, as a scalable quality assurance methodology for real-world cyber-physical systems. In falsification, one employs stochastic hill-climbing optimization to quickly find a counterexample input to a black-box system model. Quantitative robust semantics is the technical key that enables use of such optimization. In this paper, we tackle the so-called scale problem regarding Boolean connectives that is widely recognized in the community: quantities of different scales (such as speed [km/h] vs. RPM, or worse, RPH) can mask each other's contribution to robustness. Our solution consists of integration of the multi-armed bandit algorithms in hill climbing-guided falsification frameworks, with a technical novelty of a new reward notion that we call hill-climbing gain. Our experiments show our approach's robustness under the change of scales, and that it outperforms a state-of-the-art falsification tool.