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Seven hard problems in symbolic background knowledge acquisition

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Aspects and Prospects of Theoretical Computer Science (IMYCS 1990)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 464))

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Abstract

By using a special characterization of machine learning algorithms, we first define what is background knowledge, as opposed to case-based, strategic, and explanatory types of knowledge. We oppose also the symbolic to the numeric view of background knowledge. We discuss then what we see as the seven most difficult topics in background knowledge acquisition, namely the detection of implicit implications, first order logic knowledge representation and acquiring "Skolem" functions, uncertain knowledge, weak knowledge, time management and fusion of several sources of knowledge, knowledge for vision, certification of knowledge.

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Author information

Authors and Affiliations

  1. CNRS & Université Paris Sud, LRI, Bldg 490, 91405, Orsay, France

    Yves Kodratoff

  2. George Mason University, AI Center, Fairfax, 22030, Virginia, USA

    Yves Kodratoff

Authors
  1. Yves Kodratoff
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Jürgen Dassow Jozef Kelemen

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© 1990 Springer-Verlag Berlin Heidelberg

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Kodratoff, Y. (1990). Seven hard problems in symbolic background knowledge acquisition. In: Dassow, J., Kelemen, J. (eds) Aspects and Prospects of Theoretical Computer Science. IMYCS 1990. Lecture Notes in Computer Science, vol 464. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-53414-8_29

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  • DOI: https://doi.org/10.1007/3-540-53414-8_29

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-53414-3

  • Online ISBN: 978-3-540-46869-1

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