A logical framework for default reasoning
Artificial Intelligence
The Stanford GraphBase: a platform for combinatorial computing
The Stanford GraphBase: a platform for combinatorial computing
A Simplified Format for the Model Elimination Theorem-Proving Procedure
Journal of the ACM (JACM)
A prolog Technology Theorem Prover: Implementation by an Extended Prolog Compiler
Proceedings of the 8th International Conference on Automated Deduction
Caching and Lemmaizing in Model Elimination Theorem Provers
CADE-11 Proceedings of the 11th International Conference on Automated Deduction: Automated Deduction
The Applicability of Logic Program Analysis and Transformation to Theorem Proving
CADE-12 Proceedings of the 12th International Conference on Automated Deduction
PROTEIN: A PROver with a Theory Extension INterface
CADE-12 Proceedings of the 12th International Conference on Automated Deduction
Model Elimination Without Contrapositives
CADE-12 Proceedings of the 12th International Conference on Automated Deduction
Improving search in a hypothetical reasoning system
ACSC '03 Proceedings of the 26th Australasian computer science conference - Volume 16
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We have been investigating ways in which the performance of model elimination based systems can be improved and in this paper we present some of our results. Firstly, we have investigated code improvements based on local and global analysis of the internal knowledge base used by the theorem prover. Secondly, we have looked into the use of a n lists to represent ancestor goal information to see if this gives a performance boost over the traditional two list approach. This n list representation might be thought of as a simple hash table. Thirdly, we conducted initial investigations into the effect of rule body literal ordering on performance.The results for the code improvements show them to be worthwhile, producing gains in some example problems. Using the n list representation gave mixed results: for some examples it improved execution speed, in others it degraded it. A rule body literal ordering that placed instantiated goals (including hypotheses) early in the bodies of rules showed an improvement in execution time.