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A Disjunctive Positive Refinement of Model Elimination and its Application to Subsumption Deletion
 JOURNAL OF AUTOMATED REASONING
, 1995
"... The Model Elimination (ME) calculus is a refutational complete, goaloriented calculus for firstorder clause logic. In this paper, we introduce a new variant called disjunctive positive ME (DPME); it improves on Plaisted's positive refinement of ME in that reduction steps are allowed only w ..."
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The Model Elimination (ME) calculus is a refutational complete, goaloriented calculus for firstorder clause logic. In this paper, we introduce a new variant called disjunctive positive ME (DPME); it improves on Plaisted's positive refinement of ME in that reduction steps are allowed only with positive literals stemming from disjunctive clauses. DPME is motivated by
PTTP+GLiDeS: Guiding Linear Deductions with Semantics
 Advanced Topics in Arti Intelligence: 12th Australian Joint Conference on Arti Intelligence, AI'99, number 1747 in LNAI
, 1999
"... This paper describes PTTP+GLiDeS, a PTTP style prover augmented with a semantic pruning mechanism, GLiDeS. PTTP+GLiDeS combines modified versions of Stickel's PTTP style prover [6] and the model generator MACE [4]. ..."
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This paper describes PTTP+GLiDeS, a PTTP style prover augmented with a semantic pruning mechanism, GLiDeS. PTTP+GLiDeS combines modified versions of Stickel's PTTP style prover [6] and the model generator MACE [4].
The applicability of logic program analysis and transformation to theorem proving
 Automated Deduction—CADE12
, 1994
"... Analysis and transformation techniques developed for logic programming can be successfully applied to automatic theorem proving. In this paper we demonstrate how these techniques can be used to infer useful information that can speed up theorem provers, assist in the identi cation of necessary infer ..."
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Analysis and transformation techniques developed for logic programming can be successfully applied to automatic theorem proving. In this paper we demonstrate how these techniques can be used to infer useful information that can speed up theorem provers, assist in the identi cation of necessary inference rules for solving speci c problems, how failure branches can be eliminated from the proof tree and how a nonterminating deduction in a proof system can be turned into failure. In addition, this method also provides su cient conditions for identifying Casefree Theories [26]. The specialisation techniques developed in this paper are independent of the proof system and can therefore be applied to theorem provers for any logic written as logic programs. 2 1
Title System Description: PTTP+GLiDeS Semantically Guided PTTP
, 2000
"... Abstract PTTP+GLiDeS is a semantically guided linear deduction theorem prover, built from PTTP and MACE. It takes problems in clause normal form, generates semantic information about the clauses, and then uses the semantic information to guide its search for a proof. This paper outlines the semantic ..."
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Abstract PTTP+GLiDeS is a semantically guided linear deduction theorem prover, built from PTTP and MACE. It takes problems in clause normal form, generates semantic information about the clauses, and then uses the semantic information to guide its search for a proof. This paper outlines the semantic guidance strategy used by PTTP+GLiDeS and describes its implementation. The system's performance is evaluated against that of its parent system PTTP and its strengths and weaknesses are discussed. Introduction PTTP+GLiDeS is a semantically guided linear deduction theorem prover, built from PTTP [7] and MACE [5]. It takes problems in clause normal form (CNF), generates semantic information about the clauses, and then uses the semantic information to guide its search for a proof.