Collaborative Agents for Drilling Optimisation Tasks Using an Unsupervised Connectionist Model

Título

dc.contributor.author	Curiel, Leticia
dc.contributor.author	Herrero Cosío, Álvaro
dc.contributor.author	Corchado Rodríguez, Emilio Santiago
dc.contributor.author	Bravo Díez, Pedro M.
dc.date.accessioned	2017-09-06T09:16:26Z
dc.date.available	2017-09-06T09:16:26Z
dc.date.issued	2005
dc.identifier.citation	IV International Workshop on Practical Applications of Agents and Multiagent Systems. IWPAAMS'2005.
dc.identifier.uri	http://hdl.handle.net/10366/135089
dc.description.abstract	The purpose of this study is the optimization of drilling tasks in the construction of big auto-car storage warehouse. 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.
dc.format.mimetype	application/pdf
dc.language.iso	en
dc.publisher	Universidad de León
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 Unported
dc.rights.uri	https://creativecommons.org/licenses/by-nc-nd/3.0/
dc.subject	Computer Science
dc.title	Collaborative Agents for Drilling Optimisation Tasks Using an Unsupervised Connectionist Model
dc.type	info:eu-repo/semantics/conferenceObject
dc.rights.accessRights	info:eu-repo/semantics/openAccess