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A Sensory Control System for Adjusting Group Emotion Using Bayesian Networks and Reinforcement Learning

  • Jun-Ho Kim
  • Ki-Hoon Kim
  • Sung-Bae ChoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9648)

Abstract

The relationship between sensory stimuli and emotion has been actively investigated, but it is relatively undisclosed to determine appropriate stimuli for inducing the target emotion for a group of people in the same space like school, hospital, store, etc. In this paper, we propose a stimuli control system to adjust group emotion in a closed space, especially kindergarten. The proposed system predicts the next emotion of a group of people using modular tree-structured Bayesian networks, and controls the stimuli appropriate to the target emotion using utility table initialized by domain knowledge and adapted by reinforcement learning as the cases of stimuli and emotions are cumulated. To evaluate the proposed system, the real data were collected for five days from a kindergarten where the sensor and stimulus devices were installed. We obtained 84 % of prediction accuracy, and 56.2 % of stimuli control accuracy. Moreover, in the scenario tests on math and music classes, we could control the stimuli to fit the target emotion with 63.2 % and 76.3 % accuracies, respectively.

Notes

Acknowledgements

This work was supported by the Industrial Strategic Technology Development Program, 10044828, Development of Augmenting Multisensory Technology for Enhancing Significant Effects on the Service Industry, funded by the Ministry of Trade, Industry and Energy (MI, Korea).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceYonsei UniversitySeoulKorea

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