single-jc.php

JACIII Vol.18 No.1 pp. 62-70
doi: 10.20965/jaciii.2014.p0062
(2014)

Paper:

Atmosphere Understanding for Humans Robots Interaction Based on SVR and Fuzzy Set

Kazuhiro Ohnishi, Fangyan Dong, and Kaoru Hirota

Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

Received:
May 22, 2013
Accepted:
November 25, 2013
Published:
January 20, 2014
Keywords:
humans-robots interaction, emotion, atmosphere, fuzzy set, support vector regression (SVR)
Abstract

A method for understanding the atmosphere is proposed for humans-robots interactions in a multi-agent society, where the individual assessment of the atmosphere is estimated using a Support Vector Regression (SVR) method that represents the emotions of all agents and the atmosphere of the entire society is represented as a fuzzy set in a Fuzzy Atmosfield. This method provides the necessary information that allows each agent (human/robot) in the society to understand the differences between the objective characteristics of the atmosphere and the agent’s individual assessment of the subjective atmosphere and to make appropriate behavioral decisions thereafter. In the experiments, 13 scenarios are tested by four humans. The characteristics of the atmosphere are calculated by applying the proposed method to the emotion data from the four humans. The results are compared with the subjective atmosphere information from the four humans and it is found that the average accuracy reaches 90%. This proposal is planned in order to realize customized services for the humans-robots interactions in a “Multi-Agent Fuzzy Atmosfield,” which is the subject of the authors’ group’s ongoing research project.

Cite this article as:
K. Ohnishi, F. Dong, and K. Hirota, “Atmosphere Understanding for Humans Robots Interaction Based on SVR and Fuzzy Set,” J. Adv. Comput. Intell. Intell. Inform., Vol.18, No.1, pp. 62-70, 2014.
Data files:
References
  1. [1] R.W. Picard, “Affective Computing: Challenges,” Int. J. of Human-Computer Studies, Vol.59, No.1-2, pp. 55-64, 2003.
  2. [2] J. L. Burke, R. R. Murphy et al., “Final Report for the DARPA/NSF Interdisciplinary Study on Human-Robot Interaction,” IEEE Trans. on Systems, Man, and Cybernetics-Part C: Applications and Reviews, Vol.34, No.2, pp. 103-112, 2004.
  3. [3] T. Kishi, T. Kojima, N. Endo, M. Destephe, T. Otani, L. Jamone, P. Kryczka, G. Trovato, K. Hashimoto, S. Cosentino, and A. Takahashi, “Impression Survey of the Emotion Expression Humanoid Robot with Mental Model based Dynamic Emotions,” IEEE Int. Conf. on Robotics and Automation (ICRA), 2013.
  4. [4] R. Read and T. Belpaeme, “How to Use Non-Linguistic Utterances to Convey Emotion in Child-Robot Interaction,” HRI’12 Proc. of the 7th annual ACM/IEEE Int. Conf. on Human-Robot Interaction, pp. 219-220, 2012.
  5. [5] K. Hirota and F. Y. Dong, “Development of Mascot Robot System in NEDO project,” IEEE Int. Conf. on Intelligent Systems, Varna, Bulgaria, 2008.
  6. [6] Y. Yamazaki, H. A. Vu et al., “Gesture Recognition Using Combination of Acceleration Sensor and Images for Casual Communication Between Robots and Humans,” IEEE World Congress on Evolutionary Computation, Barcelona, Spain, 2010.
  7. [7] Y. Yamazaki, Y. Hatakeyama, F.-Y. Dong, K. Nomoto, and K. Hirota, “Fuzzy Inference based Mentality Expression for Eye Robot in Affinity Pleasure-Arousal Space,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.12, No.3, pp. 304-313, 2008.
  8. [8] Z. -T. Liu, F.-Y Dong, M.Wu, D.-Y. Li, Y. Yamazaki, and K. Hirota, “Emotional States Based 3-D Fuzzy Atmosfield for Casual Communication between Humans and Robots,” 2011 IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE2011), (Taipei, Taiwan), pp. 777-782, 2011/6.
  9. [9] Y. Yamazaki, F. Dong, Y. Uehara, Y. Hatakeyama, H. Nobuhara, Y. Takama, and K. Hirota, “Mentality Expression in Affinity Pleasure-Arousal Space using Ocular and Eyelid Motion of Eye Robot,” SCIS&ISIS, pp. 422-425, 2006.
  10. [10] Y. Yamazaki, F. Dong, Y. Masuda, Y. Uehara, P. Kormushev, H. A. Vu, P. Q. Le, and K. Hirota, “Intent Expression Using Eye Robot for Mascot Robot System,” Int. Symp. on Advanced Intelligent System, pp. 576-580, 2007.
  11. [11] Y. Uehara, Y. Yamazaki, Y. Masuda, H. A. Vu, K. Fukuda, Y. Mastuura, P. Q. Le, M. S. Hannachi, F. Dong, Y. Takama, and K. Hirota, “Speaker Emotion Inference Module and its Application to Mascot Robot System,” ISME, pp. 299-306, 2008.
  12. [12] Y. Yamazaki, H. A. Vu, P. Q. Le, K. Fukuda, Y. Matsuura, M. S. Hannachi, F. Dong, Y. Takama, and K. Hirota, “Mascot Robot System by integrating Eye Robot and Speech Recognition using its Casual Information Recommendation,” ISCIIA, 2008.
  13. [13] V. Akre, E. Falkum et al., “The Communication Atmosphere Between Physician closures Competitive Perfectionist or Supportive Dialogue A Norwegian Study,” Social Science & Medicine, Vol.44. No.4, pp. 519-526, 1997.
  14. [14] T.M. Rutkowski and D. P.Mandic, “Modelling the Communication Atmosphere: A Human Centered Multimedia Approach to Evaluate Communicative Situations,” Artificial Intelligence for Human Computing, Springer-Verlag, Berlin, Vol.4451, pp. 155-169, 2007.
  15. [15] T. M. Rutkowski, K. Kakusho et al., “Evaluation of the Communication Atmosphere,” Knowledge-Based Intelligent Information and Engineering Systems, Springer-Verlag, Berlin, Vol.3213, pp. 364-370, 2004.
  16. [16] B. Anderson, “Affective Atmospheres,” Emotion, Space and Society, Vol.2, No.2, pp. 77-81, 2009.
  17. [17] Z. T. Liu,M.Wu, D.-Y. Li, L.-F. Chen, F.-Y. Dong Y. Yamazaki, and K. Hirota, “Concept of Fuzzy Atmosfield for Representing Communication Atmosphere and Its Application to Humans-Robots Interaction,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.17, pp. 7-13, 2013.
  18. [18] K. Ohnishi, F. Dong, and K. Hirota, “Transform Function from Affinity Arousal-Pleasure Space into Atmosfield for Atmosphere Measurement,” Int. Conf. on Information Technology and Computer Applications (ITCA), pp. 120-124, 2011.
  19. [19] D. Basak, S. Pal, D. C. Patranabis, “Support Vector Regression,” Neural Information Processing, Vol.11, No.10, pp. 203-224, 2007.
  20. [20] L. A. Zadeh, “Fuzzy sets,” Information and Control, Vol.8, pp. 338-353, 1965.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Nov. 16, 2018