Learning Prime Implicant Conditions From Interpretation Transition

Tony Ribeiro (1, 2) , Katsumi Inoue (1)

1 NII - National Institute of Informatics
2 SOKENDAI - Graduate University for Advanced Studies [Hayama]

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 = TP (I), where TP 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.

Mots clés: dynamical systems; Boolean networks; attractors; supported models; learning from interpretation; Inductive Logic Programming

Domaines: Informatique [cs]; Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]; Logique en informatique [cs.LO]

Tony Ribeiro, Katsumi Inoue. Learning Prime Implicant Conditions From Interpretation Transition. The 24th International Conference on Inductive Logic Programming (ILP 2014), Sep 2015, Nancy, France. ⟨hal-01710486⟩