Abstract
In this paper, we present a new system, ENIGME. Its purpose is to learn the operative domain knowledge in the form of a rule system that follows as closely as possible the explicit reasoning method chosen for the future system. To this end we have mapped the inputs of a Machine Learning system to different parts of the model of expertise as it is used in the KADS methodology. This knowledge constrains the learning process thereby assuring coherence between the learnt and acquired knowledge, and allowing the expert to guide the learning tool effectively.
We thank the whole machine learning and knowledge acquisition team at the LAFORIA. We have benefited from many discussions with Karine Causse and Bernard Le Roux who, furthermore, provided us with the bridge learning set. We are also particularly grateful to Nigel Shadbolt for his constructive comments on earlier drafts of this paper.
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Thomas, J., Laublet, P., Ganascia, JG. (1993). A machine learning tool designed for a model-based knowledge acquisition approach. In: Aussenac, N., Boy, G., Gaines, B., Linster, M., Ganascia, J.G., Kodratoff, Y. (eds) Knowledge Acquisition for Knowledge-Based Systems. EKAW 1993. Lecture Notes in Computer Science, vol 723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57253-8_51
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DOI: https://doi.org/10.1007/3-540-57253-8_51
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