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.
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