Inductive learning from state transitions over continuous domains

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. © Springer International Publishing AG, part of Springer Nature 2018.
Type de document :
Communication dans un congrès
Lachiche N.Vrain C. ILP 2017 - 27th International Conference on Inductive Logic Programming, Sep 2017, Orléans, France. Springer Verlag, ILP 2017: Inductive Logic Programming, 10759, pp.124-139, 2018, LNAI. 〈10.1007/978-3-319-78090-0_9〉
Liste complète des métadonnées

https://hal-univ-rennes1.archives-ouvertes.fr/hal-01888952
Contributeur : Xavier Chard-Hutchinson <>
Soumis le : vendredi 5 octobre 2018 - 14:55:31
Dernière modification le : mardi 4 décembre 2018 - 17:02:04

Lien texte intégral

Identifiants

Citation

Tony Ribeiro, Sophie Tourret, Maxime Folschette, Morgan Magnin, Domenico Borzacchiello, et al.. Inductive learning from state transitions over continuous domains. Lachiche N.Vrain C. ILP 2017 - 27th International Conference on Inductive Logic Programming, Sep 2017, Orléans, France. Springer Verlag, ILP 2017: Inductive Logic Programming, 10759, pp.124-139, 2018, LNAI. 〈10.1007/978-3-319-78090-0_9〉. 〈hal-01888952〉

Partager

Métriques

Consultations de la notice

118