Abstract
This paper describes an evolutionary robotics experiment, which aims at showing the possibility of learning by guidance in a dynamic cognition perspective. Our model relies on Continuous Time Recurrent Neural Networks and Hebbian plasticity. The agents have the ability to be guided by stimuli and we study the influence of a guidance on their external behavior and internal dynamic when faced with other stimuli. The article develops the experiment and presents some results on the dynamic of the systems. © 2011 Springer-Verlag.
Cite
CITATION STYLE
Manac’h, K., & De Loor, P. (2011). Guiding for associative learning: How to shape artificial dynamic cognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5777 LNAI, pp. 189–196). https://doi.org/10.1007/978-3-642-21283-3_24
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