Bio-inspired cognitive architecture for adaptive agents based on an evolutionary approach
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- Títol: Bio-inspired cognitive architecture for adaptive agents based on an evolutionary approach
- Autor: Romero López, Oscar Javier; Antonio Jiménez, Angélica de
- Publicació original: 2008
- 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 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.
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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 Reinforcement Machine Learning System (RMLS) based on bio-inspired techniques.
- Notes de reproducció original: Digitalización realizada por la Biblioteca Virtual del Banco de la República (Colombia)
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Notes:
- Resum: Artificial immune systems; Cognitive science.; Connectionist Q-Learning; Extended classifier systems; 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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