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How to combine data abstraction and model refinement: A methodological contribution in MACAO

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A Future for Knowledge Acquisition (EKAW 1994)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 867))

Abstract

This paper deals with methodological aspects of knowledge acquisition and modelling. We focus on how the problem solving can be modelled. Our analysis relies on two experiments where we combined MACAO and KADS to develop knowledge based systems: a technical diagnosis support application and a system that helps to assess debt recovery files. The paper reports these experiments as well as the conclusions drawn. Their evaluation underlines the advantage of combining a detailed analysis of the expert's reasoning with the selection and adaptation of generic models and problem solving methods. Moreover, from this work, we derive guidelines on how to apply practically this combination. We propose to integrate these results in MACAO and improve the methodology by this means.

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Authors and Affiliations

  1. IRIT-URA 1399 du CNRS - UPS, 118, route de Narbonne, F- 31062, Toulouse Cedex

    Aussenac-Gilles Nathalie

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  1. Aussenac-Gilles Nathalie
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Luc Steels Guus Schreiber Walter Van de Velde

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© 1994 Springer-Verlag Berlin Heidelberg

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Nathalie, AG. (1994). How to combine data abstraction and model refinement: A methodological contribution in MACAO. In: Steels, L., Schreiber, G., Van de Velde, W. (eds) A Future for Knowledge Acquisition. EKAW 1994. Lecture Notes in Computer Science, vol 867. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-58487-0_14

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  • DOI: https://doi.org/10.1007/3-540-58487-0_14

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-58487-2

  • Online ISBN: 978-3-540-49006-7

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