Learning from Interpretation Transition

Katsumi Inoue, Tony Ribeiro and Chiaki Sakama

Machine Learning, vol.94(1), pages 51--79, Springer-Verlag, 2014.

Abstract

We propose a novel 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. The learning framework can be repeatedly applied for identifying Boolean networks from basins of attraction. Two algorithms have been implemented for this learning task, and are compared using examples from the biological literature. We also show how to incorporate background knowledge and inductive biases, then apply the framework to learning transition rules of cellular automata.