• DocumentCode
    2494062
  • Title

    Evolutionary neural network model of universal grammar

  • Author

    Saiki, Motohiro ; Matsuda, Satoshi

  • Author_Institution
    Grad. Sch. of Ind. Technol., Nihon Univ., Narashino, Japan
  • fYear
    2010
  • fDate
    18-23 July 2010
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    Acquisition and performance of languages or grammar are the typical intellectual activities of human beings, and various models of these processes using neural networks have been proposed. These activities, however, are considered not to be learned completely anew in each individual, but also to have been acquired over the long evolutionary history of human beings. The universal grammar is assumed to be a comprehensive knowledge of grammar that was acquired and hardwired in the brain during human evolution. By employing neuroevolution, we illustrate how the universal grammar might have evolved in the neural network using a genetic algorithm.
  • Keywords
    formal languages; genetic algorithms; grammars; learning (artificial intelligence); neural nets; evolutionary neural network model; genetic algorithm; human evolution; language acquisition; language performance; learning; neuroevolution; universal grammar; Biological cells; Grammar; Ions;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), The 2010 International Joint Conference on
  • Conference_Location
    Barcelona
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-6916-1
  • Type

    conf

  • DOI
    10.1109/IJCNN.2010.5596735
  • Filename
    5596735