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Titolo:
Random Boolean nets and features of language
Autore:
Hurford, JR;
Indirizzi:
Univ Edinburgh, Dept Linguist, Language Evolut & Computat Res Unit, Edinburgh, Midlothian, Scotland Univ Edinburgh Edinburgh Midlothian Scotland burgh, Midlothian, Scotland
Titolo Testata:
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
fascicolo: 2, volume: 5, anno: 2001,
pagine: 111 - 116
SICI:
1089-778X(200104)5:2<111:RBNAFO>2.0.ZU;2-6
Fonte:
ISI
Lingua:
ENG
Keywords:
chaos; Chomsky; complexity; Kauffman; language acquisition; language diversity; language learnability; linguistic parameters; poverty of stimulus; random Boolean nets;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
13
Recensione:
Indirizzi per estratti:
Indirizzo: Hurford, JR Univ Edinburgh, Dept Linguist, Language Evolut & Computat Res Unit, Edinburgh, Midlothian, Scotland Univ Edinburgh Edinburgh Midlothian Scotland thian, Scotland
Citazione:
J.R. Hurford, "Random Boolean nets and features of language", IEEE T EV C, 5(2), 2001, pp. 111-116

Abstract

This paper describes an attempt to cast several essential quite abstract properties of natural languages within the Framework of Kauffman's random Boolean nets (RBNs). These properties are complexity, interconnectedness, stability, diversity, and underdeterminedness, Specifically, in the research reported here, a language is modeled as an attractor of a Boolean net. (Groups of) nodes in the net might be thought of as linguistic principles or parameters as posited by Chomskyan theory of the 1980s. According to this theory, the task of the language learner is to set parameters to appropriate values on the basis of very limited experience of the language in use. The setting of one parameter can have a complex effect on the settings of other parameters. A RBN is generated and run to find an attractor. A state from this attractor is degraded, which represents the degenerate input of languageto the language learner, and this degraded state is then input to a net with the same connectivity and activation functions as the original net to see whether it converges on the same attractor as the original. In practice, many nets fail to converge on the original attractor and degenerate into attractors representing complete uncertainty. Other nets settle at intermediate levels of uncertainty and some nets manage to overcome the incompleteness of input and converge on attractors identical to that from which the original inputs were (de)generated, Finally, an attempt was made to select a population of such successful nets, using a genetic algorithm where fitness was correlated with an ability to acquire several different languages faithfully. It has so far proved impossible to breed such successful nets, lending some plausibility to the Chomskyan suggestion that the human language acquisition capacity is not the outcome of natural selection.

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Documento generato il 26/01/20 alle ore 16:49:26