Interactive Learning from Contradictions in a Paraconsistent Logic

Abstract : In this paper, we describe a formal logical framework which we claim as essential to prove and to revise a model produced by combined ILP techniques. The dynamic process of proof embrace the supervision of the learning machine by a human, and this framework places the interpretation of contradictions in the heart of the interactive process which leads to a model which can be discussed, justified, and proven. We illustrate and validate this framework on an industrial application in the field of Drug Discovery, combining different learning processes to predict pharmaco-kinetic properties (ADME-T) and adverse side effects of therapeutic drug molecules.
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Communication dans un congrès
ILP'06: 16th international Conference on Inductive Logic Programming, pp.51, 2006, 〈http://www.dc.fi.udc.es/ilp06/accepted.html〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00274303
Contributeur : Christopher Dartnell <>
Soumis le : jeudi 17 avril 2008 - 17:42:48
Dernière modification le : jeudi 11 janvier 2018 - 06:26:23
Document(s) archivé(s) le : vendredi 21 mai 2010 - 01:50:26

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  • HAL Id : lirmm-00274303, version 1

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Jean Sallantin, Christopher Dartnell, Mohammad Afshar. Interactive Learning from Contradictions in a Paraconsistent Logic. ILP'06: 16th international Conference on Inductive Logic Programming, pp.51, 2006, 〈http://www.dc.fi.udc.es/ilp06/accepted.html〉. 〈lirmm-00274303〉

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