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:
  • Tecnología; Tecnología / Ingeniería y operaciones afines