Learning Datalog Programs from Input and Output
نویسندگان
چکیده
We present a new framework for learning disjunctive logic programs from interpretation transitions, called LFDT. It is a nontrivial extension to Inoue, Ribeiro and Sakama’s LF1T learning framework, which learns normal logic programs from interpretation transitions. Two resolutions for disjunctive rules are also presented and used in LFDT to simplify learned disjunctive rules.
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