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Mining definitions in Kissat with Kittens

Bounded variable elimination is one of the most important preprocessing techniques in SAT solving. It benefits from discovering functional dependencies in the form of definitions encoded in the CNF. While the common approach pioneered in SatELite relies on syntactic pattern matching, our new approac...

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Detalles Bibliográficos
Autores principales: Fleury, Mathias, Biere, Armin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10564679/
https://www.ncbi.nlm.nih.gov/pubmed/37829794
http://dx.doi.org/10.1007/s10703-023-00421-2
Descripción
Sumario:Bounded variable elimination is one of the most important preprocessing techniques in SAT solving. It benefits from discovering functional dependencies in the form of definitions encoded in the CNF. While the common approach pioneered in SatELite relies on syntactic pattern matching, our new approach uses cores produced by an embedded SAT solver, Kitten. In contrast to a similar semantic technique implemented in Lingeling based on BDD algorithms to generate irredundant CNFs, our new approach is able to generate DRAT proofs. We further discuss design choices for our embedded SAT solver Kitten. Experiments with Kissat show the effectiveness of this approach.