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

N. Agell; Cecilio Angulo Bahón; Francisco Javier Ruiz Vegas; Mònica Sánchez Soler

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

N. Agell, Cecilio Angulo Bahón, Francisco Javier Ruiz Vegas, Mònica Sánchez Soler

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Research output: Conference paper › Contribution

Abstract

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.

Original language	English
Publication status	Published - 16 Jul 2012
Event	26th International Workshop on Qualitative Reasoning (QR 2012) - Duration: 16 Jul 2012 → 18 Jul 2012

Conference

Conference	26th International Workshop on Qualitative Reasoning (QR 2012)
Period	16/07/12 → 18/07/12

Cite this

@conference{024f82ab7e32421f8a4e912db3794ce6,

title = "A qualitative learning system to acquire human sensory abilities in adjustment tasks",

abstract = "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.",

author = "N. Agell and {Angulo Bah{\'o}n}, Cecilio and {Ruiz Vegas}, {Francisco Javier} and {S{\'a}nchez Soler}, M{\`o}nica",

year = "2012",

month = jul,

day = "16",

language = "English",

note = "26th International Workshop on Qualitative Reasoning (QR 2012) ; Conference date: 16-07-2012 Through 18-07-2012",

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TY - CONF

T1 - A qualitative learning system to acquire human sensory abilities in adjustment tasks

AU - Agell, N.

AU - Angulo Bahón, Cecilio

AU - Ruiz Vegas, Francisco Javier

AU - Sánchez Soler, Mònica

PY - 2012/7/16

Y1 - 2012/7/16

N2 - 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.

AB - 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.

M3 - Contribution

T2 - 26th International Workshop on Qualitative Reasoning (QR 2012)

Y2 - 16 July 2012 through 18 July 2012

ER -

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

Abstract

Conference

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Cite this