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- W2973413658 abstract "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." @default.
- W2973413658 created "2019-09-26" @default.
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- W2973413658 date "2019-01-01" @default.
- W2973413658 modified "2023-09-24" @default.
- W2973413658 title "A Cube Distribution Approach to QBF Solving and Certificate Minimization" @default.
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- W2973413658 doi "https://doi.org/10.1007/978-3-030-30048-7_31" @default.
- W2973413658 hasPublicationYear "2019" @default.
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