Learning from interpretation transition

@article{Inoue2013LearningFI,
  title={Learning from interpretation transition},
  author={Katsumi Inoue and Tony Ribeiro and Chiaki Sakama},
  journal={Machine Learning},
  year={2013},
  volume={94},
  pages={51-79}
}
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=T P (I), where T P 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… CONTINUE READING