• DocumentCode
    1802592
  • Title

    Trained neural networks play chess endgames

  • Author

    Si, Jie ; Tang, FZilun

  • Author_Institution
    Comsearch, Reston, VA, USA
  • Volume
    6
  • fYear
    1999
  • fDate
    36342
  • Firstpage
    3730
  • Abstract
    In this paper, three types of chess endgames were studied and three layer feedforward neural networks were applied to learn the hidden rules in chess endgames. The purpose of this paper is to convert the symbolic rules of chess endgames into numerical information that neural networks can learn. The neural networks have been proved efficient in learning and playing some simple cases of chess endgames
  • Keywords
    feedforward neural nets; games of skill; learning (artificial intelligence); multilayer perceptrons; chess endgames; hidden rule learning; symbolic rules; three layer feedforward neural networks; trained neural networks; Biological neural networks; Feedforward neural networks; Humans; IEEE members; Intelligent networks; Intelligent robots; Mathematical model; Neural networks; Pattern recognition; Signal processing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1999. IJCNN '99. International Joint Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-5529-6
  • Type

    conf

  • DOI
    10.1109/IJCNN.1999.830745
  • Filename
    830745
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1802592