A sensory control system for adjusting group emotion using Bayesian networks and reinforcement learning

Jun Ho Kim; Ki Hoon Kim; Sung Bae Cho

doi:10.1007/978-3-319-32034-2_32

A sensory control system for adjusting group emotion using Bayesian networks and reinforcement learning

Jun Ho Kim, Ki Hoon Kim, Sung Bae Cho

College of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

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.

Original language	English
Title of host publication	Hybrid Artificial Intelligent Systems - 11th International Conference, HAIS 2016, Proceedings
Editors	Francisco Martinez-Alvarez, Alicia Troncoso, Hector Quintian, Emilio Corchado
Publisher	Springer Verlag
Pages	377-388
Number of pages	12
ISBN (Print)	9783319320335
DOIs	https://doi.org/10.1007/978-3-319-32034-2_32
Publication status	Published - 2016
Event	11th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2016 - Seville, Spain Duration: 2016 Apr 8 → 2016 Apr 20

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9648
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	11th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2016
Country/Territory	Spain
City	Seville
Period	16/4/8 → 16/4/20

Bibliographical note

Funding Information:
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).

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-32034-2_32

Cite this

Kim, J. H., Kim, K. H., & Cho, S. B. (2016). A sensory control system for adjusting group emotion using Bayesian networks and reinforcement learning. In F. Martinez-Alvarez, A. Troncoso, H. Quintian, & E. Corchado (Eds.), Hybrid Artificial Intelligent Systems - 11th International Conference, HAIS 2016, Proceedings (pp. 377-388). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9648). Springer Verlag. https://doi.org/10.1007/978-3-319-32034-2_32

Kim, Jun Ho ; Kim, Ki Hoon ; Cho, Sung Bae. / A sensory control system for adjusting group emotion using Bayesian networks and reinforcement learning. Hybrid Artificial Intelligent Systems - 11th International Conference, HAIS 2016, Proceedings. editor / Francisco Martinez-Alvarez ; Alicia Troncoso ; Hector Quintian ; Emilio Corchado. Springer Verlag, 2016. pp. 377-388 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{782d2b82e60945ac9ad71b2c67897c8a,

title = "A sensory control system for adjusting group emotion using Bayesian networks and reinforcement learning",

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.",

author = "Kim, {Jun Ho} and Kim, {Ki Hoon} and Cho, {Sung Bae}",

note = "Funding Information: 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). Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 11th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2016 ; Conference date: 08-04-2016 Through 20-04-2016",

year = "2016",

doi = "10.1007/978-3-319-32034-2_32",

language = "English",

isbn = "9783319320335",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "377--388",

editor = "Francisco Martinez-Alvarez and Alicia Troncoso and Hector Quintian and Emilio Corchado",

booktitle = "Hybrid Artificial Intelligent Systems - 11th International Conference, HAIS 2016, Proceedings",

address = "Germany",

}

Kim, JH, Kim, KH & Cho, SB 2016, A sensory control system for adjusting group emotion using Bayesian networks and reinforcement learning. in F Martinez-Alvarez, A Troncoso, H Quintian & E Corchado (eds), Hybrid Artificial Intelligent Systems - 11th International Conference, HAIS 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9648, Springer Verlag, pp. 377-388, 11th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2016, Seville, Spain, 16/4/8. https://doi.org/10.1007/978-3-319-32034-2_32

A sensory control system for adjusting group emotion using Bayesian networks and reinforcement learning. / Kim, Jun Ho; Kim, Ki Hoon; Cho, Sung Bae.

Hybrid Artificial Intelligent Systems - 11th International Conference, HAIS 2016, Proceedings. ed. / Francisco Martinez-Alvarez; Alicia Troncoso; Hector Quintian; Emilio Corchado. Springer Verlag, 2016. p. 377-388 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9648).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A sensory control system for adjusting group emotion using Bayesian networks and reinforcement learning

AU - Kim, Jun Ho

AU - Kim, Ki Hoon

AU - Cho, Sung Bae

N1 - Funding Information: 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). Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84964058535&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84964058535&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-32034-2_32

DO - 10.1007/978-3-319-32034-2_32

M3 - Conference contribution

AN - SCOPUS:84964058535

SN - 9783319320335

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 377

EP - 388

BT - Hybrid Artificial Intelligent Systems - 11th International Conference, HAIS 2016, Proceedings

A2 - Martinez-Alvarez, Francisco

A2 - Troncoso, Alicia

A2 - Quintian, Hector

A2 - Corchado, Emilio

PB - Springer Verlag

T2 - 11th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2016

Y2 - 8 April 2016 through 20 April 2016

ER -

Kim JH, Kim KH, Cho SB. A sensory control system for adjusting group emotion using Bayesian networks and reinforcement learning. In Martinez-Alvarez F, Troncoso A, Quintian H, Corchado E, editors, Hybrid Artificial Intelligent Systems - 11th International Conference, HAIS 2016, Proceedings. Springer Verlag. 2016. p. 377-388. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-32034-2_32

A sensory control system for adjusting group emotion using Bayesian networks and reinforcement learning

Abstract

Publication series

Other

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this