Incremental Diagnosis of Discrete-Event Systems

Alban Grastien 1 Marie-Odile Cordier 2 Christine Largouët 2
2 DREAM - Diagnosing, Recommending Actions and Modelling
Inria Rennes – Bretagne Atlantique , IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
Abstract : This paper formalizes the incremental computation of diagnosis for discrete-event systems. In this framework, the system model and the observations are generally represented as finite-state machines (or automata) and the diagnosis formally defined as the synchronised composition of the model with the observations. Rather than globally considering all the observations to elaborate the global diagnosis, this approach lies in slicing the observations and in computing the diagnosis slices explaining each observation slices. In order to reach this objective we introduce the concept of automata chain and the computation of the diagnosis using this chain, that can be obtained first in a modular way and then, more efficiently, in an incremental way. These results can be extended to the case where observations are sliced according to temporal windows. This study is done in an off-line context and is a first necessary step before dealing with the on-line context introduced in the conclusion.
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https://hal.inria.fr/inria-00511109
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Submitted on : Monday, August 23, 2010 - 5:43:10 PM
Last modification on : Thursday, June 20, 2019 - 12:04:05 PM

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  • HAL Id : inria-00511109, version 1

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Alban Grastien, Marie-Odile Cordier, Christine Largouët. Incremental Diagnosis of Discrete-Event Systems. IJCAI'05 (International Joint Conference on Artificial Intelligence), 2005, Edinburgh, Scotland, United Kingdom. pp.1564--1565. ⟨inria-00511109⟩

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