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Applying Probabilistic Programming to Affective Computing.

Desmond C Ong1, Harold Soh2, Jamil Zaki3

  • 1ASTAR Artificial Intelligence Initiative and with the Institute of High Performance Computing, Agency of Science, Technology and Research (A*STAR), Singapore 138632.

IEEE Transactions on Affective Computing
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Summary
This summary is machine-generated.

This study introduces a probabilistic programming approach for Affective Computing, enabling the translation of psychological emotion theories into executable computational models. This method enhances flexibility, modularity, and integration with deep learning for advanced emotion research.

Keywords:
Affective ComputingArtificial IntelligenceEmotion TheoryModeling Human Emotion

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Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Computational Psychology

Background:

  • Affective Computing (AC) is advancing rapidly due to AI.
  • Translating psychological emotion theories into computational models remains a challenge.
  • Existing methods lack flexibility and integration capabilities.

Purpose of the Study:

  • Propose a probabilistic programming approach for Affective Computing.
  • Model psychological theories of emotion as generative, executable programs.
  • Facilitate theory-building and experimentation in emotion research.

Main Methods:

  • Utilize probabilistic programming languages for emotion modeling.
  • Integrate reasoning about emotions with other latent mental states.
  • Leverage deep learning libraries for efficient inference and learning.
  • Implement psychological theories as stochastic, executable computer programs.

Main Results:

  • Probabilistic programming offers flexibility, modularity, and compositionality.
  • Enables integration with deep learning for large-scale data analysis.
  • Provides a standardized platform for comparing competing emotion theories.
  • Executable code examples facilitate researcher adoption.

Conclusions:

  • Probabilistic programming offers a powerful framework for Affective Computing.
  • This approach bridges the gap between psychological theory and computational modeling.
  • Facilitates standardized, data-driven research and development in emotion AI.