# QFUN: Towards Machine Learning in QBF

arXiv: https://arxiv.org/abs/1710.02198 · HTML: https://mlsanity.com/paper/1710.02198/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2017.

## Built on by (most cited first, 1 of 1)

- [Implicit State and Goals in QBF Encodings for Positional Games (extended version)](https://mlsanity.com/paper/2301.07345/) (2023)

## Abstract (arXiv)

This paper reports on the QBF solver QFUN that has won the non-CNF track in the recent QBF evaluation. The solver is motivated by the fact that it is easy to construct Quantified Boolean Formulas (QBFs) with short winning strategies (Skolem/Herbrand functions) but are hard to solve by nowadays solvers. This paper argues that a solver benefits from generalizing a set of individual wins into a strategy. This idea is realized on top of the competitive RAReQS algorithm by utilizing machine learning. The results of the implemented prototype are highly encouraging.