Improving the performance of automated forward deduction system EnCal

  • Kazunori Nishi
  • Jingde Cheng
  • Kazuo Ushijima
VII Poster Session Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1336)


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.


Entailment calculus Forward deduction Hashing Common sub formula sharing 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Kazunori Nishi
    • 1
  • Jingde Cheng
    • 1
  • Kazuo Ushijima
    • 1
  1. 1.Department of Computer Science and Communication EngineeringKyushu UniversityHigashi-ku, FukuokaJapan

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