A qualitative learning system to acquire human sensory abilities in adjustment tasks

Adjustment in creative processes is not purely a functional or a physical task, but arise from highly subjective preceptive and cognitive aspects which cannot be fully modeled by standard quantitative structures. In such tasks, the involvement of human experts becomes necessary, preventing the complete process automation. This paper introduces an innovative artificial cognitive system to support decision-making in adjustment processes based on human sensory abilities. The proposed system, based on expert knowledge management, draws on a machine learning tool jointly with an actions' generator module. The methodology proposed is applied to a real case study: color-adjustment in the automotive painting industry.