Inductive Learning from State Transitions over Continuous Domains

  • Tony RibeiroEmail author
  • Sophie Tourret
  • Maxime Folschette
  • Morgan Magnin
  • Domenico Borzacchiello
  • Francisco Chinesta
  • Olivier Roux
  • Katsumi Inoue
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10759)


Learning from interpretation transition (LFIT) automatically constructs a model of the dynamics of a system from the observation of its state transitions. So far, the systems that LFIT handles are restricted to discrete variables or suppose a discretization of continuous data. However, when working with real data, the discretization choices are critical for the quality of the model learned by LFIT. In this paper, we focus on a method that learns the dynamics of the system directly from continuous time-series data. For this purpose, we propose a modeling of continuous dynamics by logic programs composed of rules whose conditions and conclusions represent continuums of values.


Continuous logic programming Learning from interpretation transition Dynamical systems Inductive logic programming 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Tony Ribeiro
    • 1
    Email author
  • Sophie Tourret
    • 2
  • Maxime Folschette
    • 3
  • Morgan Magnin
    • 1
  • Domenico Borzacchiello
    • 5
  • Francisco Chinesta
    • 6
  • Olivier Roux
    • 1
  • Katsumi Inoue
    • 4
  1. 1.Laboratoire des Sciences du Numérique de Nantes (LS2N)NantesFrance
  2. 2.Max-Planck-Institut für InformatikSaarland Informatics CampusSaarbrückenGermany
  3. 3.Univ Rennes, Inria, CNRS, IRISA, IRSETRennesFrance
  4. 4.National Institute of InformaticsTokyoJapan
  5. 5.Institut de Calcul IntensifNantesFrance
  6. 6.PIMM, ENSAM ParisTechParisFrance

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