Studying How Innate Motivations Can Drive Skill Acquisition in Cognitive Robots

• Alejandro Romero ^[1] ; Francisco Bellas ^[1] ; Jose A. Becerra ^[1] ; Richard J. Duro ^[1]
  1. [1] Universidade da Coruña

     Universidade da Coruña

     A Coruña, España

     
• Localización: XoveTIC 2019: The 2nd XoveTIC Conference (XoveTIC 2019), A Coruña, Spain, 5–6 September / Alberto Alvarellos González (ed. lit.), Joaquim de Moura (ed. lit.), Beatriz Botana Barreiro (ed. lit.), Javier Pereira-Loureiro (ed. lit.), Manuel Francisco González Penedo (ed. lit.), 2019, ISBN 978-3-03921-444-0
• Idioma: inglés
• Enlaces
  • Texto Completo Libro (pdf)
• Resumen

  • In this paper, we address the problem of how to bootstrap a cognitive architecture to opportunistically start learning skills in domains where multiple skills can be learned at the same time. To this end, taking inspiration from a series of computational models of the use of motivations in infants, we propose an approach that leverages two types of cognitive motivations: exploratory and proficiency based, the latter modulated by the concept of interestingness as an implementation of attentional mechanisms. This approach is tested in an illustrative experiment with a real robot.