Learning from Interpretation Transition
Katsumi Inoue, Tony Ribeiro and Chiaki Sakama
Machine Learning, vol.94(1), pages 51--79, Springer-Verlag, 2014.
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.
Full Paper (pdf 295K)
(The original publication is available at www.springerlink.com)