inria-00000531, version 1

Voir la fiche détaillée

BibTeX  EndNote  TEI  RefWorks %% inria-00000531, version 1 %% http://hal.inria.fr/inria-00000531/en/ @inproceedings{GRASTIEN:2005:INRIA-00000531:1, title = { {A}utomata {S}licing for {D}iagnosing {D}iscrete-{E}vent {S}ystems with {P}artially {O}rdered {O}bservations}, 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 orde red 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 automata 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.}, 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 }, booktitle = {{AIIA}'05 ({C}ongress of the {I}talian {A}ssociation for {A}rtificial {I}ntelligence) }, address = {{M}ilan / {I}taly }, audience = {non sp{\'e}cifi{\'e}e }, day = {21}, month = {09}, year = {2005}, URL = {http://hal.inria.fr/inria-00000531/en/}, URL = {http://hal.inria.fr/inria-00000531/PDF/italy.pdf}, } %F inria-00000531, version 1 %0 Conference Proceedings %U http://hal.inria.fr/inria-00000531/en/ %2 INFO:INFO_AI %T Automata Slicing for Diagnosing Discrete-Event Systems with Partially Ordered Observations %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 %X When dealing with real systems, it is unrealistic to suppose that observations can be totally ordered according to their emission dates. The partially orde red 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 automata 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 2005-09-21 %2 non spécifiée %B AIIA'05 (Congress of the Italian Association for Artificial Intelligence) %2 Milan / Italy %8 2005-09-21 <?xml version="1.0" encoding="UTF-8"?> <listBibl xmlns="http://www.tei-c.org/ns/1.0" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML"> <listBibl type="inproceedings"><head>Communications avec acte</head> <biblStruct type="inproceedings" xml:id="inria-00000531"><analytic> <author><persName><forename>Alban</forename><surname>Grastien</surname></persName> <affiliation> <orgName type="laboratory">INRIA - IRISA</orgName> <orgName type="team">DREAM</orgName> <country>FR</country> <orgName type="institution">CNRS : UMR6074</orgName> <orgName type="institution">INRIA</orgName> <orgName type="institution">Institut National des Sciences Appliquées de Rennes</orgName> <orgName type="institution">Université de Rennes 1</orgName> </affiliation> </author> <author><persName><forename>Marie-Odile</forename><surname>Cordier</surname></persName> <affiliation> <orgName type="laboratory">INRIA - IRISA</orgName> <orgName type="team">DREAM</orgName> <country>FR</country> <orgName type="institution">CNRS : UMR6074</orgName> <orgName type="institution">INRIA</orgName> <orgName type="institution">Institut National des Sciences Appliquées de Rennes</orgName> <orgName type="institution">Université de Rennes 1</orgName> </affiliation> </author> <author><persName><forename>Christine</forename><surname>Largouët</surname></persName> <affiliation> <orgName type="laboratory">INRIA - IRISA</orgName> <orgName type="team">DREAM</orgName> <country>FR</country> <orgName type="institution">CNRS : UMR6074</orgName> <orgName type="institution">INRIA</orgName> <orgName type="institution">Institut National des Sciences Appliquées de Rennes</orgName> <orgName type="institution">Université de Rennes 1</orgName> </affiliation> </author> <title type="main" level="a">Automata Slicing for Diagnosing Discrete-Event Systems with Partially Ordered Observations</title></analytic><monogr><meeting>AIIA'05 (Congress of the Italian Association for Artificial Intelligence)</meeting><imprint><pubPlace><settlement>Milan </settlement><country> Italy</country></pubPlace><biblScope type="publicationDate">2005-09-21</biblScope></imprint></monogr><idno type="HALid">http://hal.inria.fr/inria-00000531/en/</idno><idno type="HALFile">http://hal.inria.fr/inria-00000531/PDF/italy.pdf</idno></biblStruct> </listBibl> </listBibl>

Automata Slicing for Diagnosing Discrete-Event Systems with Partially Ordered Observations Alban Grastien () 1, Marie-Odile Cordier () a, 1, Christine Largouët 1 (2005)

Accès au texte intégral PDF

AIIA'05 (Congress of the Italian Association for Artificial Intelligence) (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 orde red 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 automata 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

Domaine

:

Informatique/Intelligence artificielle

inria-00000531, version 1

http://hal.inria.fr/inria-00000531/fr/

oai:hal.inria.fr:inria-00000531_v1

Contributeur : Alban Grastien <>

Soumis le : Vendredi 28 Octobre 2005, 12:19:01

Dernière modification le : Jeudi 18 Janvier 2007, 12:25:27