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AoL: Action Learning: A methodology to capture expertise in adjustment tasks

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Ruiz Vegas, Francisco JavierMés informacióMés informacióMés informació
Samà Monsonís, AlbertMés informacióMés informació
Raya Giner, CristóbalMés informacióMés informacióMés informació
Agell Jané, Núria
Document typeConference report
Defense date2012
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
It is well known that some people can perform a task with greater precision and accuracy than others: they are experts. In the past, experts were interviewed to find out why they have this expertise, but this was not always completely effective because often experts "don't know what they know". In this paper we propose a model of the process of making decisions performed by experts in the final adjustment of products task. Based on this model, we also propose a system based on a machine learning module that facilitates the capture of these expert skills. We give an example to illustrate the process proposed.
CitationRuiz, F. [et al.]. AoL: Action Learning: A methodology to capture expertise in adjustment tasks. A: Jornadas de ARCA. "Actas de XIV Jornadas de ARCA : Sistemas Cualitativos y sus Aplicaciones en Diagnosis, Robótica e Inteligencia Ambiental". Salou: 2012, p. 95-99. 
URIhttp://hdl.handle.net/2117/17652
ISBN978-84-616-2007-4
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  • Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial - Ponències/Comunicacions de congressos [1.447]
  • GREC - Grup de Recerca en Enginyeria del Coneixement - Ponències/Comunicacions de congressos [114]
  • Departament d'Enginyeria Electrònica - Ponències/Comunicacions de congressos [1.656]
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