2026-04-28T07:02:42Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/93942024-07-17T00:05:20Zcom_10259_3847com_10259_5086com_10259_2604col_10259_7109

Collaborative agents for drilling optimisation tasks using an unsupervised connectionist model Curiel Herrera, Leticia Elena Herrero Cosío, Álvaro Corchado, Emilio Bravo Díez, Pedro Miguel The purpose of this study is the optimization of drilling tasks in the construction of big auto-carrier storage warehouses. This is carried out by applying different Artificial Intelligence (AI) techniques: a cooperative unsupervised connectionist model (focused on the detection of some optimal drilling conditions) and software agents. These agents can collaborate to save drilling time and waste by interchanging information about the conditions of drill bits and the kind of material to be drilled. 2024-07-16T10:57:06Z 2024-07-16T10:57:06Z 2024-07-16T10:57:06Z 2005 info:eu-repo/semantics/conferenceObject http://hdl.handle.net/10259/9394 eng 4th International workshop on practical applications of agents and multiagent systems, p. 199-205 https://dialnet.unirioja.es/servlet/libro?codigo=959658 info:eu-repo/semantics/openAccess Universidad de León. Servicio de Publicaciones