Constrained relative least general generalization for Inducing Constraint Logic Programs View Full Text


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Article Info

DATE

1995-12

AUTHORS

Fumio Mizoguchi, Hayato Ohwada

ABSTRACT

Relative least general generalization proposed by Plotkin, is widely used for generalizing first-order clauses in Inductive Logic Programming, and this paper describes an extension of Plotkin’s work to allow various computation domains: Herbrand Universe, sets, numerical data, ect. The ϕ-subsumption in Plotkin’s framework is replaced by a more general constraint-based subsumption. Since this replacement is analogous to that of unification by constraint solving in Constraint Logic Programming, the resultant method can be viewed as a Constraint Logic Programming version of relative least general generalization. Constraint-based subsumption, however, leads to a search on an intractably large hypothesis space. We therefore providemeta-level constraints that are used as semantic bias on the hypothesis language. The constraintsfunctional dependency andmonotonicity are introduced by analyzing clausal relationships. Finally, the advantage of the proposed method is demonstrated through a simple layout problem, where geometric constraints used in space planning tasks are produced automatically. More... »

PAGES

335-368

References to SciGraph publications

  • 1990-08. Learning logical definitions from relations in MACHINE LEARNING
  • 1991-02. Inductive logic programming in NEW GENERATION COMPUTING
  • 1993-09. Projecting CLPR constraints in NEW GENERATION COMPUTING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/bf03037230

    DOI

    http://dx.doi.org/10.1007/bf03037230

    DIMENSIONS

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