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Seven hard problems in symbolic background knowledge acquisition

  • Yves Kodratoff
Part II Invited Lectures
Part of the Lecture Notes in Computer Science book series (LNCS, volume 464)

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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Copyright information

© Springer-Verlag Berlin Heidelberg 1990

Authors and Affiliations

  • Yves Kodratoff
    • 1
    • 2
  1. 1.CNRS & Université Paris Sud, LRIOrsayFrance
  2. 2.George Mason University, AI CenterVirginiaUSA

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