Learning Prime Implicant Conditions from Interpretation Transition - Ribeiro, Tony; Inoue, Katsumi (Inductive Logic Programming, 2015)
In a previous work we proposed a framework for learning normal logic programs from transitions of interpretations. Given a set of pairs of interpretations (I, J) such that $$J=T_P(I)$$ , where $$T_P$$ is the immediate consequence operator, we infer the program P. Here we propose a new learning approach that is more efficient in terms of output quality. This new approach relies on specialization in place of generalization. It generates hypotheses by specialization from the most general clauses until no negative transition is covered. Contrary to previous approaches, the output of this method does not depend on variables/transitions ordering. The new method guarantees that the learned rules are minimal, that is, the body of each rule constitutes a prime implicant to infer the head.