• DocumentCode
    280339
  • Title

    Artificial intelligence for genomic interpretation

  • Author

    Sallantin, J. ; Pingand, P.

  • fYear
    1990
  • fDate
    33147
  • Firstpage
    42675
  • Lastpage
    42678
  • Abstract
    Biological entities present a complexity level that should be correctly managed in Artificial Intelligence environments. One has to describe and access them in a proper way. What is the role of symbolic learning in this context? The authors define semi-empirical knowledge and theories, which constitute their goals. Knowledge is represented by the means of conceptual graphs, which allow one to manage easily the control of any process of iterative learning to refine the expert´s knowledge. They present an illustration of these principles in a specific domain, protein folding
  • fLanguage
    English
  • Publisher
    iet
  • Conference_Titel
    Symbols Versus Neurons, IEE Colloquium on
  • Conference_Location
    London
  • Type

    conf

  • Filename
    190573