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

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@Article{Mingo:2016:IS,

• author = "Jack Mario Mingo and Ricardo Aler",
• title = "A competence-performance based model to develop a syntactic language for artificial agents",
• journal = "Information Sciences",
• year = "2016",
• volume = "373",
• pages = "79--94",
• month = "10 " # dec,
• keywords = "genetic algorithms, genetic programming, Grammatical evolution, Stochastic grammars, Reinforcement learning, Language games, Multi-agents systems",
• ISSN = "0020-0255",
• URL = "http://www.sciencedirect.com/science/article/pii/S0020025516306727",
• DOI = "doi:10.1016/j.ins.2016.08.088",
• size = "16 pages",
• 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 analysed, where linguistic descriptions are enforced to keep word order constraints.",

}

Genetic Programming entries for Jack Mario Mingo Ricardo Aler Mur

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