A-ordered tableaux

In resolution proof procedures refinements based on A-orderings of literals have a long tradition and are well investigated. In tableau proof procedures such refinements were only recently introduced by the authors of the present paper. In this paper we prove the following results: we give a completeness proof of A-ordered ground clause tableaux which is a lot easier to follow than the previous one. The technique used in the proof is extended to the non-clausal case as well as to the non-ground case and we introduce an ordered version of Hintikka sets that shares the model existence property of standard Hintikks sets. We show that A-ordered tableaux are a proof confluent refinement of tableaux and that A-ordered tableaux together with the connection refinement yield an incomplete proof procedure. We introduce A-ordered first-order NNF tableaux, prove their completeness, and we briefly discuss implementation issues

Deduction by combining semantic tableaux and integer programming

. In this paper we propose to extend the current capabilities of automated reasoning systems by making use of techniques from integer programming. We describe the architecture of an automated reasoning system based on a Herbrand procedure (enumeration of formula instances) on clauses. The input are arbitrary sentences of first-order logic. The translation into clauses is done incrementally and is controlled by a semantic tableau procedure using unification. This amounts to an incremental polynomial CNF transformation which at the same time encodes part of the tableau structure and, therefore, tableau-specific refinements that reduce the search space. Checking propositional unsatisfiability of the resulting sequence of clauses can either be done with a symbolic inference system such as the Davis-Putnam procedure or it can be done using integer programming. If the latter is used a number of advantages become apparent. Introduction In this paper we propose to extend the current capabilit..

Model generation theorem proving with interval constraints

We investigate how the deduction paradigm of model generation theorem proving can be enhanced with interval-and extraval-based constraints leading to more efficient model generation in for some finite domain problems