A Cube Distribution Approach to QBF Solving and Certificate Minimization

Quantified Boolean Formulas (QBFs) are powerful expressions to naturally and concisely encode many decision problems in computer science, such as robotic planning, hardware/software synthesis and verification, among others. Their effective solving and certificate (in terms of model and countermodel) generation play crucial roles to enable practical applications. In this work, we give a new view on QBF solving and certificate generation by the cube distribution interpretation. It provides a largely increased flexibility for QBF reasoning and allows compact certificate derivation with don’t cares. Through this interpretation, we develop a QBF solver based on the prior clause selection framework. Experimental results demonstrate the superiority of our solver in both solving performance and certificate size compared to other state-of-the-art solvers with certificate generation ability.

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