Quasi-Acyclic Propositional Horn Knowledge Bases: Optimal Compression

  • Authors:
  • Peter L. Hammer;Alexander Kogan

  • Affiliations:
  • -;-

  • Venue:
  • IEEE Transactions on Knowledge and Data Engineering
  • Year:
  • 1995

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Abstract

Horn knowledge bases are widely used in many applications. This paper is concerned with the optimal compression of propositional Horn production rule bases - one of the most important knowledge bases used in practice. The problem of knowledge compression is interpreted as a problem of Boolean function minimization. It was proved in [16] that the minimization of Horn functions, i.e., Boolean functions associated with Horn knowledge bases, is NP-complete.This paper deals with the minimization of quasi-acyclic Horn functions, the class of which properly includes the two practically significant classes of quadratic and of acyclic functions. A procedure is developed for recognizing in quadratic time the quasi-acyclicity of a function given by a Horn CNF, and a graph-based algorithm is proposed for the quadratic time minimization of quasi-acyclic Horn functions.