• DocumentCode
    3050522
  • Title

    Learning from examples and counterexamples with equational background knowledge

  • Author

    Kounalis, Emmanuel

  • Author_Institution
    Centre de Recherche en Inf. de Nancy, France
  • fYear
    1990
  • fDate
    6-9 Nov 1990
  • Firstpage
    254
  • Lastpage
    260
  • Abstract
    The author presents a method to check whether an implicit representation (i.e., a formula of the form t/{t1,. . .,tn}, where t is viewed as a generalization of a set of examples and t1,. . .,tn are counterexamples) is a generalization with respect to a finite set of equations which describes the background knowledge problem; that is, whether there exists a ground (variable-free) instance of t which is not equivalent to any ground instance of t1,. . .,tn with respect to a set E of equations. Intuitively, the implicit representation t/{t 1,. . .,tn} is a generalization if the set of ground instances of the formula t/{t1,. . .,tn} is non-empty. Whereas this problem is in general undecidable since the equality is so, it is shown here that, in the case where the set E of equations is compiled into a ground convergent term rewriting system, one can easily discover concepts in theories described by a finite set of equations
  • Keywords
    artificial intelligence; learning systems; rewriting systems; artificial intelligence; counterexamples; equational background knowledge; finite set of equations; ground convergent term rewriting system; ground instances; implicit representation; learning from examples; Arithmetic; Chaos; Equations; Machine learning; Scattering;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools for Artificial Intelligence, 1990.,Proceedings of the 2nd International IEEE Conference on
  • Conference_Location
    Herndon, VA
  • Print_ISBN
    0-8186-2084-6
  • Type

    conf

  • DOI
    10.1109/TAI.1990.130343
  • Filename
    130343