最小汎化の組合せ探索による帰納推論

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Inductive Logic Programming (ILP), a variation of inductive inference, has been gaining attention as a way to synthesize logic programs from examples. One of the important tasks of ILP is to search the hypothesis space for definite clauses which explain examples under the background knowledge. Many researchers have studied methods which perform the task, and developed the method which employs refinement operators to generate definite clauses, the one which calculates a least general generalization (lgg) of examples relative to the background knowledge which consists of ground atoms, etc. In this paper we describe computing-lggs-first strategy, that is to say, to search the hypothesis space, we first calculate possible lggs of atoms provable from the background knowledge, then combine them to form clauses. It is more efficient than the method employing refinement operators, and can use non-ground atoms as the background knowledge unlike the method calculating an lgg relative to the background knowledge. Practically, we have to employ the strategy with some restrictions. We have been developing an inductive inference system KISS. It calculates lggs of atoms in the background knowledge, instead of calculating lggs of provable atoms. Due to this simple strategy, it becomes possible to find various clauses such as those which are necessary to synthesize logic programs and those which represent regularities of predicates.
社団法人人工知能学会の論文
1996-01-01

最小汎化の組合せ探索による帰納推論

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