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We introduce a novel semantic approach for bounded variable elimination in SAT solving, utilizing embedded SAT solver cores to discover functional dependencies. This method generates DRAT proofs, outperforming syntactic techniques.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Algorithm Design

Background:

  • Bounded variable elimination is a key preprocessing step in SAT solving.
  • Existing methods often rely on syntactic pattern matching.
  • Discovering functional dependencies aids in simplifying Boolean formulas.

Purpose of the Study:

  • To develop a new semantic approach for bounded variable elimination.
  • To leverage cores from an embedded SAT solver for dependency discovery.
  • To enable the generation of DRAT proofs.

Main Methods:

  • Utilized an embedded SAT solver, Kitten, to identify cores.
  • Applied semantic analysis of these cores to find functional dependencies.
  • Implemented a technique capable of generating DRAT proofs.

Main Results:

  • The new approach effectively discovers functional dependencies.
  • DRAT proof generation was successfully achieved.
  • Experimental results with Kissat demonstrated the approach's effectiveness.

Conclusions:

  • The semantic approach using embedded SAT solver cores is effective for bounded variable elimination.
  • This method offers advantages over traditional syntactic techniques.
  • The ability to generate DRAT proofs enhances its utility in SAT solving.