Journal of Automated Reasoning
Journal of Automated Reasoning
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
Automatic Theorem Proving With Renamable and Semantic Resolution
Journal of the ACM (JACM)
Symbolic Logic and Mechanical Theorem Proving
Symbolic Logic and Mechanical Theorem Proving
RRTP - A Replacement Rule Theorem Prover
Journal of Automated Reasoning
Proceedings of the 10th International Conference on Automated Deduction
Semantically Guided First-Order Theorem Proving using Hyper-Linking
CADE-12 Proceedings of the 12th International Conference on Automated Deduction
The Search Efficiency of Theorem Proving Strategies
CADE-12 Proceedings of the 12th International Conference on Automated Deduction
A proof method for quantification theory: its justification and realization
IBM Journal of Research and Development
Replacement Rules with Definition Detection
Selected Papers from Automated Deduction in Classical and Non-Classical Logics
A Confluent Connection Calculus
CADE-16 Proceedings of the 16th International Conference on Automated Deduction: Automated Deduction
IJCAR '01 Proceedings of the First International Joint Conference on Automated Reasoning
The model evolution calculus as a first-order DPLL method
Artificial Intelligence
Logical Engineering with Instance-Based Methods
CADE-21 Proceedings of the 21st international conference on Automated Deduction: Automated Deduction
A taxonomy of theorem-proving strategies
Artificial intelligence today
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In this paper, we present a novel first order theorem proving strategy - ordered semantic hyper linking. Ordered semantic hyper linking (OSHL) is an instance-based refutational theorem proving strategy. It is sound and complete. OSHL has an efficient propositional decision procedure. It solves first order problems by reducing them to propositional problems. It uses natural semantics of an input problem to guide its search. It also incorporates term rewriting to handle equality. The propositional efficiency, semantic guidance and equality support allow OSHL to solve problems that are difficult for many other strategies. The efficiency of OSHL is supported by experimental study as well as complexity analysis.