Hyperresolution for guarded formulae

AbstractThis paper investigates the use of hyperresolution as a decision procedure and model builder for guarded formulae. In general, hyperresolution is not a decision procedure for the entire guarded fragment. However we show that there are natural fragments of the guarded fragment which can be decided by hyperresolution. In particular, we prove decidability of hyperresolution with or without splitting for the fragment GF1− and point out several ways of extending this fragment without losing decidability. As hyperresolution is closely related to various tableaux methods the present work is also relevant for tableaux methods. We compare our approach to hypertableaux, and mention the relationship to other clausal classes which are decidable by hyperresolution

Semantically-guided goal-sensitive reasoning: decision procedures and the Koala prover

The main topic of this article are SGGS decision procedures for fragments of first-order logic without equality. SGGS (Semantically-Guided Goal-Sensitive reasoning) is an attractive basis for decision procedures, because it generalizes to first-order logic the Conflict-Driven Clause Learning (CDCL) procedure for propositional satisfiability. As SGGS is both refutationally complete and model-complete in the limit, SGGS decision procedures are model-constructing. We investigate the termination of SGGS with both positive and negative results: for example, SGGS decides Datalog and the stratified fragment (including Effectively PRopositional logic) that are relevant to many applications. Then we discover several new decidable fragments, by showing that SGGS decides them. These fragments have the small model property, as the cardinality of their SGGS-generated models can be upper bounded, and for most of them termination tools can be applied to test a set of clauses for membership. We also present the first implementation of SGGS - the Koala theorem prover - and we report on experiments with Koala

Saturation-based Boolean conjunctive query answering and rewriting for the guarded quantification fragments

Query answering is an important problem in AI, database and knowledge representation. In this paper, we develop saturation-based Boolean conjunctive query answering and rewriting procedures for the guarded, the loosely guarded and the clique-guarded fragments. Our query answering procedure improves existing resolution-based decision procedures for the guarded and the loosely guarded fragments and this procedure solves Boolean conjunctive query answering problems for the guarded, the loosely guarded and the clique-guarded fragments. Based on this query answering procedure, we also introduce a novel saturation-based query rewriting procedure for these guarded fragments. Unlike mainstream query answering and rewriting methods, our procedures derive a compact and reusable saturation, namely a closure of formulas, to handle the challenge of querying for distributed datasets. This paper lays the theoretical foundations for the first automated deduction decision procedures for Boolean conjunctive query answering and the first saturation-based Boolean conjunctive query rewriting in the guarded, the loosely guarded and the clique-guarded fragments