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Inductive Learning from State Transitions over Continuous Domains

  • Tony Ribeiro
  • 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)

Abstract

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.

Keywords

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

References

  1. 1.
    Allen, J.F.: An interval-based representation of temporal knowledge. IJCAI 81, 221–226 (1981)Google Scholar
  2. 2.
    Benhamou, F., Goualard, F., Granvilliers, L., Puget, J.-F.: Revising hull and box consistency. In: Logic Programming: Proceedings of the 1999 International Conference on Logic Programming, pp. 230–244. MIT press (1999)Google Scholar
  3. 3.
    do Carmo Nicoletti, M., de Sá Lisboa, F.O.S., Hruschka, E.R.: Learning temporal interval relations using inductive logic programming. In: Hruschka, E.R., Watada, J., do Carmo Nicoletti, M. (eds.) INTECH 2011. CCIS, vol. 165, pp. 90–104. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-22247-4_8 CrossRefGoogle Scholar
  4. 4.
    Emerson, E.A.: Temporal and modal logic. In: Handbook of Theoretical Computer Science, Formal Models and Sematics, vol. B-5, pp. 995–10725 (1990)zbMATHGoogle Scholar
  5. 5.
    Fauré, A., Naldi, A., Chaouiya, C., Thieffry, D.: Dynamical analysis of a generic boolean model for the control of the mammalian cell cycle. Bioinformatics 22(14), e124–e131 (2006)CrossRefGoogle Scholar
  6. 6.
    Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using Bayesian networks to analyze expression data. J. Comput. Biol. 7(3–4), 601–620 (2000)CrossRefGoogle Scholar
  7. 7.
    Inoue, K., Ribeiro, T., Sakama, C.: Learning from interpretation transition. Mach. Learn. 94(1), 51–79 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Karlebach, G., Shamir, R.: Modelling and analysis of gene regulatory networks. Nat. Rev. Mol. Cell Biol. 9(10), 770–780 (2008)CrossRefGoogle Scholar
  9. 9.
    Kim, S.Y., Imoto, S., Miyano, S.: Inferring gene networks from time series microarray data using dynamic Bayesian networks. Briefings Bioinf. 4(3), 228–235 (2003)CrossRefGoogle Scholar
  10. 10.
    Martínez Martínez, D., Ribeiro, T., Inoue, K., Alenyà Ribas, G., Torras, V.: Learning probabilistic action models from interpretation transitions. In: Proceedings of the Technical Communications of the 31st International Conference on Logic Programming (ICLP 2015), pp. 1–14 (2015)Google Scholar
  11. 11.
    Nachman, I., Regev, A., Friedman, N.: Inferring quantitative models of regulatory networks from expression data. Bioinformatics 20(suppl 1), i248–i256 (2004)CrossRefGoogle Scholar
  12. 12.
    Cloud, M.J., Moore, R.E., Kearfott, R.B.: Introduction to Interval Analysis. Society for Industrial and Applied Mathematics (2009)Google Scholar
  13. 13.
    Ribeiro, T., Magnin, M., Inoue, K., Sakama, C.: Learning delayed influences of biological systems. Front. Bioeng. Biotechnol. 2, 81 (2015)CrossRefGoogle Scholar
  14. 14.
    Rodríguez, J.J., Alonso, C.J., Boström, H.: Learning first order logic time series classifiers: rules and boosting. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 299–308. Springer, Heidelberg (2000).  https://doi.org/10.1007/3-540-45372-5_29 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Tony Ribeiro
    • 1
  • 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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