Multilayered Evolutionary Architecture for Behaviour Arbitration in Cognitive Agents
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- Títol: Multilayered Evolutionary Architecture for Behaviour Arbitration in Cognitive Agents
- Autor: Romero López, Oscar Javier
- Publicació original: 2007
- Descripció física: PDF
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Nota general:
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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.
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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.
- Notes de reproducció original: Digitalización realizada por la Biblioteca Virtual del Banco de la República (Colombia)
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Notes:
- Resum: Bio-inspired machine learning; Gene expression programming; Hybrid behaviour; Co-evolution; Subsumption architecture
- © Derechos reservados del autor
- Colfuturo
- Forma/gènere: text
- Idioma: inglés
- Institució origen: Biblioteca Virtual del Banco de la República
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