CONSTRAINED RELATIVE LEAST GENERAL GENERALIZATION FOR INDUCING CONSTRAINT LOGIC PROGRAMS

Relative least general generalization, proposed by Plotkin, is widely used for generalizing first-order clauses in Inductive Logic Programmi ng, and this paper describes an extension of Plotkin's work to allow v arious computation domains: Herbrand Universe, sets, numerical data, e tc. The theta-subsumption in Plotkin's framework is replaced by a more general constraint-based subsumption. Since this replacement is analo gous 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. Constrai nt-based subsumption, however, leads to a search on an intractably lar ge hypothesis space. We therefore provide meta-level constraints that are used as semantic bias on the hypothesis language. The constraints functional dependency and monotonicity are introduced by analyzing cla usal relationships. Finally, the advantage of the proposed method is d emonstrated through a simple layout problem, where geometric constrain ts used in space planning tasks are produced automatically.