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arXiv · 1710.02198

QFUN: Towards Machine Learning in QBF

Mikoláš Janota
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Abstract

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.

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Mikoláš Janota. 2017-10-05. QFUN: Towards Machine Learning in QBF. https://arxiv.org/abs/1710.02198

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