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A multilayer affective computing model with evolutionary strategies reflecting decision-makers' preferences in

Chong Su1, Xuri Ma1, Jing Lv1

  • 1College of Information Science and Technology, Beijing University of Chemical Technology, China.

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|December 26, 2021
PubMed
Summary

This study introduces a multilayer affective computing model (MACM) to improve interactive decision-making in industrial control. The model considers human personality, emotional space, and affective states for better optimization and automated decision support.

Keywords:
Affective computingAffective parametersDecision-makers’ preferencesGenetic algorithms (GA)Iterative learning control (ILC)Process control

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Area of Science:

  • Industrial Control Systems
  • Affective Computing
  • Optimization

Background:

  • Industrial control optimization often involves multi-step interactions.
  • Existing methods neglect decision-makers' affective states and preferences during interaction.
  • This gap hinders effective controller parameter tuning and decision-making.

Purpose of the Study:

  • To develop a novel multilayer affective computing model (MACM) for interactive decision-making.
  • To integrate affective computing into industrial control optimization processes.
  • To enable computers to learn from human experts and automate decision-making.

Main Methods:

  • Developed a multilayer affective computing model (MACM) incorporating personality, emotional space, and affective states.
  • Implemented an affective state recognition method using facial expressions.
  • Created an affective parameter identification method for personalized state-changing rules.
  • Defined decision-makers' preferences and developed a mining method using MACM and iterative learning control (ILC).

Main Results:

  • The proposed affective computing-driven interactive decision-making method simplifies conversion of decision problems.
  • Demonstrated effectiveness in PI controller tuning and batch process optimization.
  • Achieved superior performance compared to traditional methods lacking affective state tracking.

Conclusions:

  • The MACM provides a robust framework for affective computing in interactive decision-making.
  • The method enhances automated decision-making by learning from human experts.
  • The study highlights the efficiency and limitations of the proposed technique in specific applications.