Improving the performance of automated forward deduction system EnCal
Automated forward deduction system is a very important component of many application systems such as theorem finding systems, active databases, and learning systems where the automated forward deduction system plays a key role as an autonomous reasoning engine. The performance of automated forward deduction system is crucial to its successful applications in practices. This paper presents some techniques which are effective in improving the performance of EnCal, an automated forward deduction system. We show how to keep many logical theorem schemata (LTSs for short) in low memory and how to avoid unnecessary pattern matching in forward deduction by using hash table and cache. We also show that these techniques can be applied to other automated forward deduction systems, and that some of them can be applied to almost all applications which deal with logical formulas.
KeywordsEntailment calculus Forward deduction Hashing Common sub formula sharing
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