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Authors: Marc Le Goc 1 ; Emilie Masse 2 and Corinne Curt 3

Affiliations: 1 LSIS, Laboratory for Systems and Information Sciences, UMR CNRS 6168, Aix-Marseille University, France ; 2 LSIS, Laboratory for Systems and Information Sciences, UMR CNRS 6168, Aix-Marseille University; Cemagref, Unité Ouvrages Hydrauliques et Hydrologie, France ; 3 Cemagref, Unité Ouvrages Hydrauliques et Hydrologie, France

ISBN: 978-989-8111-51-7

Keyword(s): Multi modeling, diagnosis reasoning, dynamic system.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Knowledge Acquisition ; Knowledge Engineering and Ontology Development ; Knowledge Representation ; Knowledge-Based Systems ; Symbolic Systems

Abstract: This paper presents a modelling approach of dynamic process for diagnosis that is compatible with the Stochastic Approach framework for discovering temporal knowledge from the timed observations contained in a database. The motivation is to define a multi-model formalism that is able to represent both the knowledge of these two sources. The aim is to model the process at the same level of abstraction that an expert uses to diagnose the process. The underlying idea is that at this level of abstraction, the model is simple enough to allow an efficient diagnosis. The proposed formalism represents the knowledge in four models: a structural model defining the components and the connection relations of the process, a behavioural model defining the states and the transitions states of the process, a functional model containing the logical relations between the values of the process’s variables, which are defined in the perception model. The models are linked with the process’s variables. Thi s point facilitates the analysis of the consistency of the four models and is the basis of the corresponding knowledge modelling methodology. The formalism and the methodology is illustrated with the model of a hydraulic dam of Cublize (France). (More)

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Paper citation in several formats:
Le Goc M.; Masse E.; Curt C. and (2008). MODELING PROCESSES FROM TIMED OBSERVATIONS.In Proceedings of the Third International Conference on Software and Data Technologies - Volume 1: ICSOFT, ISBN 978-989-8111-51-7, pages 249-256. DOI: 10.5220/0001884502490256

@conference{icsoft08,
author={Marc {Le Goc} and Emilie Masse and Corinne Curt},
title={MODELING PROCESSES FROM TIMED OBSERVATIONS},
booktitle={Proceedings of the Third International Conference on Software and Data Technologies - Volume 1: ICSOFT,},
year={2008},
pages={249-256},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001884502490256},
isbn={978-989-8111-51-7},
}

TY - CONF

JO - Proceedings of the Third International Conference on Software and Data Technologies - Volume 1: ICSOFT,
TI - MODELING PROCESSES FROM TIMED OBSERVATIONS
SN - 978-989-8111-51-7
AU - Le Goc, M.
AU - Masse, E.
AU - Curt, C.
PY - 2008
SP - 249
EP - 256
DO - 10.5220/0001884502490256

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