RT Conference Proceedings
T1 A bottom-up robot architecture based on learnt behaviors driven design
A1 Herrero-Reder, Ignacio
A1 Urdiales-García, Amalia Cristina
A1 Peula-Palacios, José Manuel
A1 Sandoval-Hernández, Francisco
K1 Robótica
AB 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 problemand 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.
YR 2015
FD 2015-06-17
LK http://hdl.handle.net/10630/9929
UL http://hdl.handle.net/10630/9929
LA eng
NO Draft previo a la revisión. El artículo definitivo tiene derechos de autor.
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026