Bio-inspired cognitive architecture for adaptive agents based on an evolutionary approach

In this work, an hybrid, self-configurable, multilayered and evolutionary subsumption architecture for cognitive agents is developed. Each layer of the multilayered architecture is modeled by one different Reinforcement Machine Learning System (RMLS) based on bio-inspired techniques. In this researc...

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Detalles Bibliográficos
Autor Principal: Romero López, Oscar Javier; Antonio Jiménez, Angélica de
Formato: Artículo (Article)
Lenguaje:Desconocido (Unknown)
Publicado: 2008
Materias:
Acceso en línea:http://babel.banrepcultural.org/cdm/ref/collection/p17054coll23/id/684
Descripción
Sumario:In this work, an hybrid, self-configurable, multilayered and evolutionary subsumption architecture for cognitive agents is developed. Each layer of the multilayered architecture is modeled by one different Reinforcement Machine Learning System (RMLS) based on bio-inspired techniques. In this research an evolutionary mechanism based on Gene Expression Programming to self-configure the behaviour arbitration between layers is suggested. In addition, a co-evolutionary mechanism to evolve behaviours in an independent and parallel fashion is used too. The proposed approach was tested in an animat environment (artificial life) using a multi-agent platform and it exhibited several learning capabilities and emergent properties for self-configuring internal agent’s architecture.