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Induction from answer sets in nonmonotonic logic programs
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DOI：10.1145/1055686.1055687.png)
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Abstract
En 中文
Inductive logic programming
(ILP) realizes inductive machine learning in computational logic. However, the present ILP mostly handles classical clausal programs, especially Horn logic programs, and has limited applications to learning
nonmonotonic logic programs
. This article studies a method for realizing induction in nonmonotonic logic programs. We consider an
extended logic program
as a background theory, and introduce techniques for inducing new rules using
answer sets
of the program. The produced new rules explain positive/negative examples in the context of inductive logic programming. The proposed methods extend the present ILP techniques to a syntactically and semantically richer framework, and contribute to a theory of nonmonotonic ILP.
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