Abstract
A Hierarchical Extended Kohonen Map (HEKM) learns to associate actions to perceptions under the supervision of a planner: they cooperate to solve path finding problems. We argue for the utility of using the hierarchical version of the KM instead of the “flat” KM. We measure the benefits of cooperative learning due to the interaction of neighboring neurons in the HEKM. We highlight a beneficial side-effect obtained by transferring motion skill from the planner to the HEKM, namely, smoothness of motion.
Supported by the No. 2129-042413.94/1 project of the Fonds National de la Recherche Scientifique, Berne, Suisse.
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© 1996 Springer-Verlag Berlin Heidelberg
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Versino, C., Gambardella, L.M. (1996). Learning fine motion by using the Hierarchical Extended Kohonen Map. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_40
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DOI: https://doi.org/10.1007/3-540-61510-5_40
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