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dc.contributor.authorHerrero-Reder, Ignacio 
dc.contributor.authorUrdiales García, Cristina
dc.contributor.authorPeula Palacios, Jose Manuel
dc.contributor.authorSandoval Hernández, Francisco
dc.date.accessioned2015-06-17T09:10:45Z
dc.date.available2015-06-17T09:10:45Z
dc.date.created2015
dc.date.issued2015-06-17
dc.identifier.urihttp://hdl.handle.net/10630/9929
dc.descriptionDraft previo a la revisión. El artículo definitivo tiene derechos de autor.es_ES
dc.description.abstractIn 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.es_ES
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.es_ES
dc.language.isoenges_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectRobóticaes_ES
dc.subject.otherCase based reasoninges_ES
dc.subject.otherReactive layeres_ES
dc.subject.otherLearning architecturees_ES
dc.subject.otherRoboticses_ES
dc.titleA bottom-up robot architecture based on learnt behaviors driven designes_ES
dc.typeinfo:eu-repo/semantics/preprintes_ES
dc.centroE.T.S.I. de Telecomunicaciónes_ES
dc.relation.eventtitleInternational Work Conference on Artificial Neural Networks (IWANN-2015)es_ES
dc.relation.eventplaceMallorca, Españaes_ES
dc.relation.eventdate10/06/2015es_ES
dc.identifier.orcidorcid.org/0000-0001-9567-200Xes_ES
dc.cclicenseby-nc-ndes_ES


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