A competence-performance based model to develop a syntactic language for artificial agents Articles uri icon

authors

  • MINGO POSTIGLIONI, JACK MARIO
  • ALERB, RICARDO

publication date

  • December 2016

start page

  • 79

end page

  • 94

volume

  • 373

International Standard Serial Number (ISSN)

  • 0020-0255

Electronic International Standard Serial Number (EISSN)

  • 1872-6291

abstract

  • The hypothesis of language use is an attractive theory in order to explain how natural languages evolve and develop in social populations. In this paper we present a model partially based on the idea of language games, so that a group of artificial agents are able to produce and share a symbolic language with syntactic structure. Grammatical structure is induced by grammatical evolution of stochastic regular grammars with learning capabilities, while language development is refined by means of language games where the agents apply on-line probabilistic reinforcement learning. Within this framework, the model adapts the concepts of competence and performance in language, as they have been proposed in some linguistic theories. The first experiments in this article have been organized around the linguistic description of visual scenes with the possibility of changing the referential situations. A second and more complicated experimental setting is also analyzed, where linguistic descriptions are enforced to keep word order constraints. (C) 2016 Elsevier Inc. All rights reserved.

keywords

  • stochastic grammars; grammatical evolution; reinforcement learning; language games; multi-agents systems; evolution