Abstract
Cognitive development concerns the evolution of human mental capabilities through experience earned during life. Important features needed to accomplish this target are the self-generation of motivations and goals as well as the development of complex behaviors consistent with these goals. Our target is to build such a bio-inspired cognitive architecture for situated agents, capable of integrating new sensing data from any source. Based on neuroscience assessed concepts, as neural plasticity and neural coding, we show how a categorization module built on cascading classifiers is able to interpret different sensing data. Moreover, we see how to give a biological interpretation to our classification model using the winner-take-all paradigm.
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Notes
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In questa parte ci sono due/tre frasi da rivedere; non ho capito bene le correzioni!
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Gini, G., Franchi, A.M., Ferrini, F., Gallo, F., Mutti, F., Manzotti, R. (2016). Bio-inspired Classification in the Architecture of Situated Agents. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_43
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DOI: https://doi.org/10.1007/978-3-319-08338-4_43
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