SCL: Clause Learning from Simple Models

Alberto Fiori (1, 2) , Christoph Weidenbach (1, 2)

1 VERIDIS - Modeling and Verification of Distributed Algorithms and Systems
2 MPII - Max-Planck-Institut für Informatik

Several decision procedures for the Bernays-Schoenfinkel (BS) fragment of first-order logic rely on explicit model assumptions. In particular, the procedures differ in their respective model representation formalisms. We introduce a new decision procedure SCL deciding the BS fragment. SCL stands for clause learning from simple models. Simple models are solely built on ground literals. Nevertheless, we show that SCL can learn exactly the clauses other procedures learn with respect to more complex model representation formalisms. Therefore, the overhead of complex model representation formalisms is not always needed. SCL is sound and complete for full first-order logic without equality.

Domaines: Logique en informatique [cs.LO]

Alberto Fiori, Christoph Weidenbach. SCL: Clause Learning from Simple Models. 27th International Conference on Automated Deduction, 2019, Natal, Brazil. pp.233-249, ⟨10.1007/978-3-030-29436-6_14⟩. ⟨hal-02405550⟩