SGGS (Semantically-Guided Goal-Sensitive reasoning) is a conflict-driven first-order theorem-proving method which is refutationally complete and model complete in the limit. These features make it attractive as a basis for decision procedures. In this paper we show that SGGS decides the stratified fragment which generalizes EPR, the PVD fragment, and a new fragment that we dub restrained. The new class has the small model property, as the size of SGGS-generated models can be upper-bounded, and is also decided by hyperresolution and ordered resolution. We report on experiments with a termination tool implementing a restrainedness test, and with an SGGS prototype named Koala.
CITATION STYLE
Bonacina, M. P., & Winkler, S. (2020). SGGS Decision Procedures. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12166 LNAI, pp. 356–374). Springer. https://doi.org/10.1007/978-3-030-51074-9_20
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