Abstract
We present a formalism and a method that allow to learn structured representations in a noisy knowledge base. The formalism fills the gap between Artificial Intelligence and Data Analysis research's domains. It describes a kind of structured modal object called “hoard”, that can express various semantics as probability, possibility and belief. The method aims at growing and refining a knowledge base, composed of hoards, by the incremental use of a data base, composed of hoards subparts. Two levels of knowledge are involved: structured hoards represent classes, and non structured individuals represent data. The main goals of the method are to match quickly both levels by automatic rules generation, and to acquire new knowledge in the structured level, by using the flat data base. Some applications are, for example, situation understanding, image analysis, adaptation to temporal variable process, negotiation.
Preview
Unable to display preview. Download preview PDF.
References
Bisson G. “Learning of rule systems by combining clustering and generalization”, in Proc. of Symbolic-Numeric Data Analysis and Learning, eds. E. Diday, Y. Lechevallier, Nova Science Publishers, New York, pp. 399–415.
Brito P., Diday E. “Pyramidal representation of symbolic objects”, in Proc. of the NATO advanced Workshop on data and computed-assisted decisions, Hamburg 3–5 Sep. 1989, eds. M. Schader, W. Gaul, Springer-Verlag.
Brito P. Analyse de données symboliques. Pyramides d'héritage, Thèse de l'Université Paris IX Dauphine, 1991.
De Carvalho F.A.T. “Histogrammes de variables d'objets assertion booléens”, in Traitement des connaissances Symboliques-Numériques, Paris 14–15 Mai 1992, pp. 65–81, ed. LISE-CEREMADE, Université Paris IX Dauphine.
Diday E. “Introduction à l'approche symbolique en analyse des données”, in Actes des journées Symboliques-Numériques pour l'apprentissage de connaissances à partir des données, eds. E. Diday, Y. Kodratoff, CEREMADE-Université Paris IX Dauphine, 1987, pp. 21–56.
Diday E. “Introduction à l'analyse des données symboliques: objets symboliques modaux et implicite”, in Actes des 2èmes journées Symboliques-Numériques pour l'apprentissage de connaissances à partir d'observations, eds. E. Diday, Y. Kodratoff, LRI-Université de Paris-Sud, Orsay, 1988, pp. 1–30.
Diday E. “Towards a statistical theory of intensions for knowledge analysis”, Rapport de Recherche INRIA N∘ 1494, 1991.
Diday E. “Des objets de l'analyse des données à ceux de l'analyse des connaissances”, in Induction Symbolique et Numérique à partir de données, CEPADUES-EDITION, pp. 9–75, 1991.
Dubois D., Prade H. Théorie des possibilités, ed. Masson, 1985.
Fisher D. “Conceptual clustering, Learning from examples and Inference”, in Proc. of the 4th International Workshop on Machine Learning, Irvine, California, 1987.
Ganascia J.B. “Improvement and refinement of the learning bias semantic”, in Proc. of the 8th ECAI, pp. 238–270, 1988.
Gettler-Summa M. “Factorial axis representation by symbolic objects”, in Traitement des connaissances Symboliques-Numériques, Paris 14–15 Mai 1992, pp. 53–64, ed. LISE-CEREMADE, Université Paris IX Dauphine.
Kodratoff Y. Leçons d'apprentissage symbolique automatique, CEPADUES-EDITIONS, 1986.
Langley P., Fisher D. “Approaches to conceptual clustering”, in Proc. of the 9th IJCAI, Morgan Kaufman Publishers, pp. 691–697, 1991.
Lebbe J., Vignes R. “Un système générateur de graphes d'identification d'objets symboliques”, in Induction Symbolique et Numérique à partir de Données, CEPADUES-EDITIONS, 1991.
Manago M. Intégration de techniques numériques et symboliques en apprentissage, Thèse de l'Université Paris-Sud, Orsay, 1988.
Michalski R. S. “Pattern recognition as rule-guided inductive inference”, in IEEE Transactions on pattern analysis and machine intelligence, Vol. PAMI-2, n∘ 4, 1980.
Michalski R. S., Stepp R. “An application of AI techniques to structuring objects into an optimal conceptual hierarchy”, in Proc. of the 7th IJCAI, Vancouver, Canada, 1981.
Michalski R.S., Carbonell J., Mitchell T. “Understanding the nature of learning”, in Machine Learning 2 — An artificial Intelligence Approach, Tioga, pp. 3–25, 1986.
Mitchell T. “Generalization as search”, in Artificial Intelligence, Vol. 18, pp. 243–250, 1982.
Quinlan J.R. “Learning logical definitions from relations”, in Machine Learning Journal 5, pp. 239–266, 1990.
Rouveirol C. ITOU: induction de théorie en ordre un, Thèse de l'Université Paris-Sud, Orsay, 1991.
Vignard P. Représentation des connaissances. Mécanismes d'exploitation et d'apprentissage, INRIA, 1991.
Vignes R. Caractérisation automatique de groupes biologiques, Thèse de l'Université Paris VI, 1991.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1993 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Auriol, E. (1993). How to learn in an incomplete knowledge environment: Structured objects for a modal approach. In: Filgueiras, M., Damas, L. (eds) Progress in Artificial Intelligence. EPIA 1993. Lecture Notes in Computer Science, vol 727. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57287-2_59
Download citation
DOI: https://doi.org/10.1007/3-540-57287-2_59
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-57287-9
Online ISBN: 978-3-540-48036-5
eBook Packages: Springer Book Archive