2026-05-29T23:30:59Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/99292026-02-03T12:28:26Zcom_10630_2254col_10630_37959

00925njm 22002777a 4500 dc Herrero-Reder, Ignacio author Urdiales-García, Amalia Cristina author Peula-Palacios, José Manuel author Sandoval-Hernández, Francisco author 2015-06-17 In reactive layers of robotic architectures, behaviors should learn their operation from experience, following the trends of modern intelligence theories. A Case Based Reasoning (CBR) reactive layer could allow to achieve this goal but, as complexity of behaviors increases, thecurse of dimensionality arises: a too high amount of cases in the behaviors casebases deteriorate response times so robot's reactiveness is finally too slow for a good performance. In this work we analyze this problem and propose some improvements in the traditional CBR structure and retrieval phase, at reactive level, to reduce the impact of scalability problems when facing complex behaviors design. http://hdl.handle.net/10630/9929 orcid.org/0000-0001-9567-200X Robótica A bottom-up robot architecture based on learnt behaviors driven design