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Discussion sur la causalité imparfaite et son application aux sciences expérimentales

Lucie Jacquin 1 Aurore Lomet 1 Jean-Philippe Poli 2
1 LS2D - Laboratoire Sciences des Données et de la Décision
DM2I - Département Métrologie Instrumentation & Information : DRT/LIST/DM2I
2 LI3A - Intelligence Artificielle et Apprentissage Automatique
DM2I - Département Métrologie Instrumentation & Information : DRT/LIST/DM2I
Abstract : Material discovery is mainly based on the analysis of experimental data obtained by varying the parameters of the manufacturing processes and by observing the properties of the created materials. Artificial Intelligence (AI) has been applied to this field, providing models that can predict the performance of new materials. Our work rather ims at an application of AI in connection with materials experts to provide them with knowledge extracted from their data. An important first step is to determine the causal relations between parameters, properties and performance. In this article, we position our interest on causality extraction within the framework of fuzzy logic.
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https://hal-cea.archives-ouvertes.fr/cea-03406885
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Submitted on : Thursday, October 28, 2021 - 10:30:09 AM
Last modification on : Monday, November 8, 2021 - 4:58:14 PM

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  • HAL Id : cea-03406885, version 1

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Lucie Jacquin, Aurore Lomet, Jean-Philippe Poli. Discussion sur la causalité imparfaite et son application aux sciences expérimentales. Rencontres francophones sur la Logique Floue et ses Applications, Université La Sorbonne, Oct 2021, PARIS, France. pp.187-190. ⟨cea-03406885⟩

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