Learning by Answer Sets
Chiaki Sakama
Proceedings of the AAAI Spring Symposium on Answer Set Programming,
Technical Report SS-01-01, AAAI Press, 2001.
Abstract
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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