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

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.