HAL - INRIA :: [inria-00000121, version 1] Incremental Diagnosis of Discrete-Event Systems

inria-00000121, version 1

%% inria-00000121, version 1
%% http://hal.inria.fr/inria-00000121/en/
@inproceedings{GRASTIEN:2005:INRIA-00000121:1,
	title = { {I}ncremental {D}iagnosis of {D}iscrete-{E}vent {S}ystems},
	author = {{G}rastien, {A}lban and {C}ordier, {M}arie-{O}dile and {L}argou{\"e}t, {C}hristine},
	abstract = {{W}hen dealing with real systems, it is unrealistic to suppose that observations can be totally ordered according to their emission dates. {T}he partially ordered observations and the system are thus both represented as finite-state machines (or automata) and the diagnosis formally defined as the synchronized composition of the model with the observations. {T}he problem we deal with in this paper is that, taking into account partially ordered observations rather than sequential ones, it becomes difficult to consider the observations one after the other and to incrementally compute the global diagnosis. {I}n this paper, we rely on a 'slicing' of the observation automaton and propose to compute diagnosis slices (for each observation slice) before combining them to get the global diagnosis. {I}n order to reach this objective, we introduce the concept of 'automata chain' and define the computation of the diagnosis using this chain, first in a modular way and then, more efficiently, in an incremental way. {T}hese results are then extended to the case where observations are sliced according to temporal windows. {T}his study is done in an off-line context. {I}t is a first and necessary step before considering the on-line context which is discussed in the conclusion.},
	keywords = {{D}iagnosis ; {D}iscrete-{E}vent {S}ystems ; {I}ncremental {D}iagnosis ; {A}utomata chain},
	language = {{A}nglais},
	affiliation = {{DREAM} - {INRIA} - {IRISA} - {CNRS} : {UMR}6074 - {INRIA} - {I}nstitut {N}ational des {S}ciences {A}ppliqu{\'e}es de {R}ennes - {U}niversit{\'e} de {R}ennes 1 - {E}quipe de {R}echerche en {I}nformatique et {M}ath{\'e}matiques - {ERIM} - {U}niversit{\'e} de la {N}ouvelle-{C}al{\'e}donie },
	booktitle = {{DX} },
	address = {{P}acific {G}rove, {C}alifornia, {USA} },
	organization = {{R}ichard {D}earden et {S}riram {N}arasimhan },
	audience = {non sp{\'e}cifi{\'e}e },
	day = {01},
	month = {06},
	year = {2005},
	URL = {http://hal.inria.fr/inria-00000121/en/},
}

%F inria-00000121, version 1
%0 Conference Proceedings
%U http://hal.inria.fr/inria-00000121/en/
%2 INFO:INFO_AI
%T Incremental Diagnosis of Discrete-Event Systems
%A Grastien, Alban
%A Cordier, Marie-Odile
%A Largouët, Christine
%A Grastien, A.
%A Cordier, M.-O.
%A Largouët, C.
%A Grastien A. et al
%+ DREAM - INRIA - IRISA - CNRS : UMR6074 - INRIA - Institut National des Sciences Appliquées de Rennes - Université de Rennes 1 - Equipe de Recherche en Informatique et Mathématiques - ERIM - Université de la Nouvelle-Calédonie
%X When dealing with real systems, it is unrealistic to suppose that observations can be totally ordered according to their emission dates. The partially ordered observations and the system are thus both represented as finite-state machines (or automata) and the diagnosis formally defined as the synchronized composition of the model with the observations. The problem we deal with in this paper is that, taking into account partially ordered observations rather than sequential ones, it becomes difficult to consider the observations one after the other and to incrementally compute the global diagnosis. In this paper, we rely on a 'slicing' of the observation automaton and propose to compute diagnosis slices (for each observation slice) before combining them to get the global diagnosis. In order to reach this objective, we introduce the concept of 'automata chain' and define the computation of the diagnosis using this chain, first in a modular way and then, more efficiently, in an incremental way. These results are then extended to the case where observations are sliced according to temporal windows. This study is done in an off-line context. It is a first and necessary step before considering the on-line context which is discussed in the conclusion.
%G Anglais
%8 2005-06-01
%2 non spécifiée
%B DX
%2 Pacific Grove, California, USA
%8 2005-06-01
%2 Richard Dearden et Sriram Narasimhan
%K Diagnosis ; Discrete-Event Systems ; Incremental Diagnosis ; Automata chain

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Incremental Diagnosis of Discrete-Event Systems

Alban Grastien (

) ¹, Marie-Odile Cordier (

)^a^, 1, Christine Largouët ^1, 2

(2005)

Accès au texte intégral
PDF

DX (2005)

Résumé : When dealing with real systems, it is unrealistic to suppose that observations can be totally ordered according to their emission dates. The partially ordered observations and the system are thus both represented as finite-state machines (or automata) and the diagnosis formally defined as the synchronized composition of the model with the observations. The problem we deal with in this paper is that, taking into account partially ordered observations rather than sequential ones, it becomes difficult to consider the observations one after the other and to incrementally compute the global diagnosis. In this paper, we rely on a 'slicing' of the observation automaton and propose to compute diagnosis slices (for each observation slice) before combining them to get the global diagnosis. In order to reach this objective, we introduce the concept of 'automata chain' and define the computation of the diagnosis using this chain, first in a modular way and then, more efficiently, in an incremental way. These results are then extended to the case where observations are sliced according to temporal windows. This study is done in an off-line context. It is a first and necessary step before considering the on-line context which is discussed in the conclusion.

a –	Université Rennes I
1 :	DREAM (INRIA - IRISA)
	CNRS : UMR6074 – INRIA – Institut National des Sciences Appliquées de Rennes – Université de Rennes 1
2 :	Equipe de Recherche en Informatique et Mathématiques (ERIM)
	Université de la Nouvelle-Calédonie

Domaine

Informatique/Intelligence artificielle

Mots-clés : Diagnosis – Discrete-Event Systems – Incremental Diagnosis – Automata chain


inria-00000121, version 1
http://hal.inria.fr/inria-00000121/fr/
oai:hal.inria.fr:inria-00000121_v1
Contributeur : Alban Grastien <>
Soumis le : Mardi 21 Juin 2005, 18:05:24
Dernière modification le : Mardi 23 Janvier 2007, 10:00:46