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Constrained relative least general generalization for Inducing Constraint Logic Programs

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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.

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Authors and Affiliations

  1. Science University of Tokyo, Noda, 278, Chiba, Japan

    Fumio Mizoguchi & Hayato Ohwada

Authors
  1. Fumio Mizoguchi
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  2. Hayato Ohwada
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Fumio Mizoguchi: He is a Professor and Director of Intelligent System Laboratory, Science University of Tokyo. He received the B. S degree in 1966, the MS degree in 1968 in Industrial Chemistry from Science University of Tokyo and Ph. D in 1978 from U. C. The University of Tokyo. He has been working on Artificial Intelligence with various approaches. The most recent approaches are the use of constraint Logic Programming and Inductive Logic Programming for real world problems.

Hayato Ohwada, Ph.D.: He is an Assistant Professor of Industrial Administration, Science University of Tokyo. He received the B. S. degree in 1983, the M. S. degree in 1985 and the Dr. Eng. degree in 1988 from Science University of Tokyo. He has been working on inductive logic programming, constraint logic programming and intelligent decision support systems.

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Mizoguchi, F., Ohwada, H. Constrained relative least general generalization for Inducing Constraint Logic Programs. NGCO 13, 335–368 (1995). https://doi.org/10.1007/BF03037230

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  • DOI: https://doi.org/10.1007/BF03037230

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