Diagnosis of Event Sequences with LFIT

Ribeiro, Tony; Folschette, Maxime; Magnin, Morgan; Okazaki, Kotaro; Kuo-Yen, Lo; Inoue, Katsumi

Type de document :

Pré-publication ou Document de travail

Titre :

Diagnosis of Event Sequences with LFIT

Auteur(s) :

Ribeiro, Tony [Auteur]
Chercheur indépendant
Folschette, Maxime [Auteur]

Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Magnin, Morgan [Auteur]
Laboratoire des Sciences du Numérique de Nantes [LS2N]
Okazaki, Kotaro [Auteur]
Kuo-Yen, Lo [Auteur]
Inoue, Katsumi [Auteur]
National Institute of Informatics [NII]

Mot(s)-clé(s) en anglais :

dynamic systems
logical modeling
explainable artificial intelligence

Discipline(s) HAL :

Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Apprentissage [cs.LG]

Résumé en anglais : [en]

Diagnosis of the traces of executions of discrete event systems is of interest to understand dynamical behaviors of a wide range of real world problems like real-time systems or biological networks. In this paper, we propose ...
Lire la suite >Diagnosis of the traces of executions of discrete event systems is of interest to understand dynamical behaviors of a wide range of real world problems like real-time systems or biological networks. In this paper, we propose to address this challenge by extending Learning From Interpretation Transition (LFIT), an Inductive Logic Programming framework that automatically constructs a model of the dynamics of a system from the observation of its state transitions. As a way to tackle diagnosis, we extend the theory of LFIT to model event sequences and their temporal properties. It allows to learn logic rules that exploit those properties to explain sequences of interest. We show how it can be done in practice through a case study.Lire moins >

Langue :

Anglais

Collections :

Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

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