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Search for an Appropriate Behavior within the Emotional Regulation in Virtual Creatures Using a Learning Classifier

Jonathan-Hernando Rosales1, Félix Ramos1, Marco Ramos2

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This study introduces a novel method for virtual creatures to regulate emotions and adapt behaviors using a learning classifier system (LCS). The approach enables appropriate responses even without prior experience, enhancing artificial emotional intelligence.

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Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Neuroscience

Background:

  • Emotion regulation involves conscious or unconscious control of emotional behaviors.
  • Conscious emotion regulation relies on prior experiences and situational adaptation.
  • Current literature lacks methods for endowing virtual agents with emotion regulation.

Purpose of the Study:

  • To develop a computational model for emotion regulation in virtual creatures.
  • To enable virtual agents to compute appropriate behaviors in specific emotional situations.
  • To address the gap in artificial emotional intelligence research.

Main Methods:

  • Utilized a learning classifier system (LCS) to model emotion regulation.
  • Implemented a system capable of adapting behaviors based on learned experiences.
  • Tested the model in both known and unknown situational contexts.

Main Results:

  • The proposed LCS model successfully generated appropriate behaviors in emotional situations.
  • The system demonstrated effective adaptation even in scenarios lacking prior experience.
  • The approach proved powerful in situations with available prior information.

Conclusions:

  • It is feasible to computationally model and implement emotion regulation in virtual agents.
  • Learning classifier systems offer a viable mechanism for artificial emotional intelligence.
  • The developed method advances the capability of virtual creatures to exhibit adaptive emotional behaviors.