Learning by Answer Sets

Chiaki Sakama

This paper presents a novel application of answer set programming to concept learning in nonmonotonic logic programs. Given an extended logic program as a background theory, we 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 result of this paper combines techniques of two important fields of logic programming in the context of nonmonotonic inductive logic programming.


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