Knowledge base reformation: Preparing first-order theories for efficient propositional reasoning

Helmut Prendinger, Mitsuru Ishizuka, Gerhard Schurz

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

We present an approach to knowledge compilation that transforms a function-free first-order Horn knowledge base to propositional logic. This form of compilation is important since the most efficient reasoning methods are defined for propositional logic, while knowledge is most conveniently expressed within a first-order language. To obtain compact propositional representations, we employ techniques from (ir)relevance reasoning as well as theory transformation via unfold/fold transformations. Application areas include diagnosis, planning, and vision. Preliminary experiments with a hypothetical reasoner indicate that our method may yield significant speed-ups.

Original languageEnglish
Pages (from-to)35-57
Number of pages23
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume14
Issue number1
DOIs
Publication statusPublished - 2000 Feb
Externally publishedYes

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering

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Knowledge base reformation : Preparing first-order theories for efficient propositional reasoning. / Prendinger, Helmut; Ishizuka, Mitsuru; Schurz, Gerhard.

In: International Journal of Pattern Recognition and Artificial Intelligence, Vol. 14, No. 1, 02.2000, p. 35-57.

Research output: Contribution to journalArticle

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