Open-Ended Learning of Reactive Knowledge in Cognitive Robotics Based on Neuroevolution

  1. A. Romero 1
  2. F. Bellas 1
  3. R.J. Duro 1
  1. 1 Universidade da Coruña
    info

    Universidade da Coruña

    La Coruña, España

    ROR https://ror.org/01qckj285

Libro:
Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021. Bilbao, Spain. September 22–24, 2021. Proceedings
  1. Hugo Sanjurjo González (coord.)
  2. Iker Pastor López (coord.)
  3. Pablo García Bringas (coord.)
  4. Héctor Quintián (coord.)
  5. Emilio Corchado (coord.)

Editorial: Springer International Publishing AG

ISBN: 978-3-030-86271-8,  978-3-030-86270-1

Año de publicación: 2021

Páginas: 65-76

Congreso: Hybrid Artificial Intelligent Systems (HAIS) (16. 2021. Bilbao)

Tipo: Aportación congreso

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Resumen

Reactive knowledge corresponds to implicit knowledge in the human brain, that is, unconscious knowledge such as reflexes that are executed without “thinking”. It is a key aspect in human development, and it is also a key aspect in cognitive architectures for robots, mainly because it avoids inefficient action selection procedures and allows addressing higher-level cognitive processes that make use of it. This paper deals with the acquisition of this type of knowledge in a cognitive architecture for open-ended learning. We propose a method for the learning of policies (reactive knowledge) trough evolution from deliberative models by means of neuroevolution. It is interesting to see in the results presented that this approach of learning reactive knowledge instead of exhaustively selecting the appropriate action every instant of time provides equivalent/better results and more efficient action sequences.

Fuente de los datos: Dialnet