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Diagnosis of Event Sequences with LFIT


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.
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Dates and versions

hal-03689936 , version 1 (07-06-2022)
hal-03689936 , version 2 (21-11-2022)


  • HAL Id : hal-03689936 , version 1


Tony Ribeiro, Maxime Folschette, Morgan Magnin, Kotaro Okazaki, Lo Kuo-Yen, et al.. Diagnosis of Event Sequences with LFIT. 2022. ⟨hal-03689936v1⟩
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