Analysis of emergent properties in a hybrid bio-inspired architecture for cognitive agents

In this work, a hybrid, self-configurable, multilayered and evolutio-nary architecture for cognitive agents is developed. Each layer of the subsump-tion architecture is modeled by one different Machine Learning System MLS based on bio-inspired techniques. In this research an evolutionary mechanism s...

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Detalles Bibliográficos
Autor Principal: Romero López, Oscar Javier; Antonio Jiménez, Angélica de
Formato: Artículo (Article)
Lenguaje:Desconocido (Unknown)
Publicado: 2007
Materias:
Acceso en línea:http://babel.banrepcultural.org/cdm/ref/collection/p17054coll23/id/676
Descripción
Sumario:In this work, a hybrid, self-configurable, multilayered and evolutio-nary architecture for cognitive agents is developed. Each layer of the subsump-tion architecture is modeled by one different Machine Learning System MLS based on bio-inspired techniques. In this research an evolutionary mechanism supported on Gene Expression Programming to self-configure the behaviour arbitration between layers is suggested. In addition, a co-evolutionary mechan-ism to evolve behaviours in an independent and parallel fashion is used. The proposed approach was tested in an animat environment using a multi-agent platform and it exhibited several learning capabilities and emergent properties for self-configuring internal agent’s architecture.