Evolutionary Neural Network Model of Universal Grammar

被引:0
|
作者
Saiki, Motohiro [1 ]
Matsuda, Satoshi [2 ]
机构
[1] Nihon Univ, Grad Sch Ind Technol, Grad Course Math Informat Engn, Tokyo 102, Japan
[2] Nihon Univ, Coll Ind Technol, Dept Math Informat Engn, Narashino, Chiba 2758475, Japan
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中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
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页数:5
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