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Integrating models of knowledge and Machine Learning

  • Jean-Gabriel Ganascia
  • Jérôme Thomas
  • Philippe Laublet
Position Papers Inductive Learning and Applications
Part of the Lecture Notes in Computer Science book series (LNCS, volume 667)

Abstract

We propose a framework allowing a real integration of Machine Learning and Knowledge acquisition. This paper shows how the input of a Machine Learning system can be mapped to the model of expertise as it is used in KADS methodology. The notion of learning bias will play a central role. We shall see that parts of it can be identified to what KADS's people call the inference and the task models. Doing this conceptual mapping, we give a semantics to most of the inputs of Machine Learning programs in terms of knowledge acquisition models. The ENIGME system which implements this work will be presented

References

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  3. [McDemott 88]
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  4. [Russel 90]
    Stuart J. Russel and Benjamin N. Grosof, Declarative Bias: An Overview., in Change of representation and inductive bias. ed. by P. BenjaminGoogle Scholar
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    Paul E. Utgoff, Shift the bias for inductive concept learning, Machine learning: An Artificial Intelligence approach V.1Google Scholar
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    B.J. Wielinga, A.Th. Schreiber and J.A. Breuker, KADS: A Modelling Approach to Knowledge Engineering., Knowledge Acquisition volume 4, number 1, march 1992.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Jean-Gabriel Ganascia
    • 2
  • Jérôme Thomas
    • 1
    • 2
  • Philippe Laublet
    • 1
  1. 1.ONERA, DMI, GIAChatillon Cedex
  2. 2.LAFORIA-CNRS Université Pierre et Marie CurieParis Cedex

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