Conflict-Driven Satisfiability for Theory Combination: Lemmas, Modules, and Proofs

Abstract

Search-based satisfiability procedures try to build a model of the input formula by simultaneously proposing candidate models and deriving new formulae implied by the input. Conflict-driven procedures perform non-trivial inferences only when resolving conflicts between formulæ and assignments representing the candidate model. CDSAT (Conflict-Driven SATisfiability) is a method for conflict-driven reasoning in unions of theories. It combines inference systems for individual theories as theory modules within a solver for the union of the theories. This article augments CDSAT with a more general lemma learning capability and with proof generation. Furthermore, theory modules for several theories of practical interest are shown to fulfill the requirements for completeness and termination of CDSAT. Proof generation is accomplished by a proof-carrying version of the CDSAT transition system that produces proof objects in memory accommodating multiple proof formats. Alternatively, one can apply to CDSAT the LCF approach to proofs from interactive theorem proving, by defining a kernel of reasoning primitives that guarantees the correctness by construction of CDSAT proofs.

Document Details

Document Type
Pub Defense Publication
Publication Date
Sep 12, 2021
Source ID
10.1007/s10817-021-09606-y

Entities

People

  • Maria Paola Bonacina
  • Natarajan Shankar
  • StĂ©phane Graham-Lengrand

Organizations

  • Defense Advanced Research Projects Agency
  • National Science Foundation
  • University of Verona

Tags

Fields of Study

  • Computer science
  • Mathematics

Readers

  • Database Systems and Applications
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  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms