Abstract
In this paper we present a strategy for inducing a behavior in a real agent through a learning process with a human teacher. The agent creates internal models extracting information from the consequences of the actions it must carry out, and not just learning the task itself. The mechanism that permits this background learning process is the Multilevel Darwinist Brain, a cognitive mechanism that allows an autonomous agent to decide the actions it must apply in its environment in order to fulfill its motivations. It is a reinforcement based mechanism that uses evolutionary techniques to perform the on line learning of the models.
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Bellas, F., Becerra, J.A., Duro, R.J. (2005). Induced Behavior in a Real Agent Using the Multilevel Darwinist Brain. In: Mira, J., Álvarez, J.R. (eds) Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. IWINAC 2005. Lecture Notes in Computer Science, vol 3562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11499305_44
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DOI: https://doi.org/10.1007/11499305_44
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26319-7
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