Multilayered Evolutionary Architecture for Behaviour Arbitration in Cognitive Agents

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  • Título: Multilayered Evolutionary Architecture for Behaviour Arbitration in Cognitive Agents
  • Autor: Romero López, Oscar Javier
  • Publicación original: 2007
  • Descripción física: PDF
  • Nota general:
    • 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 Machine Learning System (MLS) based on bio-inspired techniques such as Extended Classifier Systems (XCS), Artificial Immune Systems (AIS), Neuro Connectionist Q-Learning (NQL) and Learning Classifier Systems (LCS) among others.
      In this research an evolutionary mechanism based on Gene Expression Programming (GEP) 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. 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.
  • Notas de reproducción original: Digitalización realizada por la Biblioteca Virtual del Banco de la República (Colombia)
  • Notas:
    • Resumen: Bio-inspired machine learning; Gene expression programming; Hybrid behaviour; Co-evolution; Subsumption architecture
    • © Derechos reservados del autor
    • Colfuturo
  • Forma/género: texto
  • Idioma: inglés
  • Institución origen: Biblioteca Virtual del Banco de la República
  • Encabezamiento de materia:

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