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.
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CITATION STYLE
Versino, C., & Gambardella, L. M. (1996). Learning fine motion by using the Hierarchical Extended Kohonen Map. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1112 LNCS, pp. 221–226). Springer Verlag. https://doi.org/10.1007/3-540-61510-5_40
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