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arxiv: 1710.02198 · v1 · submitted 2017-10-05 · 💻 cs.LO

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QFUN: Towards Machine Learning in QBF

Mikol\'a\v{s} Janota
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classification 💻 cs.LO
keywords solverlearningmachineqfunalgorithmarguesbenefitsboolean
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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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