2026-09-13T19:39:01Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/93942024-07-17T00:05:20Zcom_10259_3847com_10259_5086com_10259_2604col_10259_7109
Repositorio Institucional de la Universidad de Burgos
author
Curiel Herrera, Leticia Elena
author
Herrero Cosío, Álvaro
author
Corchado, Emilio
author
Bravo Díez, Pedro Miguel
2024-07-16T10:57:06Z
2024-07-16T10:57:06Z
2005
http://hdl.handle.net/10259/9394
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.
eng
Collaborative agents for drilling optimisation tasks using an unsupervised connectionist model
info:eu-repo/semantics/conferenceObject
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URL
https://riubu.ubu.es/bitstream/10259/9394/1/Curiel-Collaborative_Agents_Drilling_Optimisation_2005.pdf
File
MD5
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application/pdf
Curiel-Collaborative_Agents_Drilling_Optimisation_2005.pdf