Inductive Learning from State Transitions over Continuous Domains

Tony Ribeiro; Sophie Tourret; Maxime Folschette; Morgan Magnin; Domenico Borzacchiello; Francisco Chinesta; Olivier Roux; Katsumi Inoue

doi:10.1007/978-3-319-78090-0_9

Inductive Learning from State Transitions over Continuous Domains

Tony Ribeiro ¹ Sophie Tourret ² Maxime Folschette ^{3, 4} Morgan Magnin ^{1, 5} Domenico Borzacchiello ⁶ Francisco Chinesta ⁷ Olivier Roux ^{1, 5} Katsumi Inoue ⁸

1 MéForBio - Méthodes Formelles pour la Bioinformatique

LS2N - Laboratoire des Sciences du Numérique de Nantes

2 MPII - Max-Planck-Institut für Informatik

3 Dyliss - Dynamics, Logics and Inference for biological Systems and Sequences

Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE

4 Irset - Institut de recherche en santé, environnement et travail

5 LS2N - Laboratoire des Sciences du Numérique de Nantes

6 ICI - Institut de Calcul Intensif

7 PIMM - Procédés et Ingénierie en Mécanique et Matériaux [Paris]

8 NII - National Institute of Informatics

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 modelling of continuous dynamics by logic programs composed of rules whose conditions and conclusions represent continuums of values.

Keywords : dynamical systems learning from interpretation transition Inductive Logic Programming Continuous Logic Programming

Document type :

Book sections

Domain :

Computer Science [cs] / Modeling and Simulation

Computer Science [cs] / Bioinformatics [q-bio.QM]

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https://hal.archives-ouvertes.fr/hal-01655644
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Submitted on : Wednesday, January 8, 2020 - 4:02:33 PM
Last modification on : Monday, January 13, 2020 - 1:12:04 AM

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