# Two-Layered Falsification of Hybrid Systems guided by Monte Carlo Tree Search

arXiv: https://arxiv.org/abs/1803.06276 · HTML: https://mlsanity.com/paper/1803.06276/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2018.

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

- [Part-X: A Family of Stochastic Algorithms for Search-Based Test Generation with Probabilistic Guarantees](https://mlsanity.com/paper/2110.10729/) (2021)
- [Adaptive Branch-and-Bound Tree Exploration for Neural Network Verification](https://mlsanity.com/paper/2505.00963/) (2025)

## Abstract (arXiv)

Few real-world hybrid systems are amenable to formal verification, due to their complexity and black box components. Optimization-based falsification---a methodology of search-based testing that employs stochastic optimization---is attracting attention as an alternative quality assurance method. Inspired by the recent works that advocate coverage and exploration in falsification, we introduce a two-layered optimization framework that uses Monte Carlo tree search (MCTS), a popular machine learning technique with solid mathematical and empirical foundations. MCTS is used in the upper layer of our framework; it guides the lower layer of local hill-climbing optimization, thus balancing exploration and exploitation in a disciplined manner.