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.
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Manac’h, K., De Loor, P. (2011). Guiding for Associative Learning: How to Shape Artificial Dynamic Cognition. In: Kampis, G., Karsai, I., Szathmáry, E. (eds) Advances in Artificial Life. Darwin Meets von Neumann. ECAL 2009. Lecture Notes in Computer Science(), vol 5777. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21283-3_24
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DOI: https://doi.org/10.1007/978-3-642-21283-3_24
Publisher Name: Springer, Berlin, Heidelberg
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