Advertisement

Soft Computing

, Volume 18, Issue 9, pp 1729–1743 | Cite as

The simulation of an emotional robot implemented with fuzzy logic

  • Fang-Yie Leu
  • Jung-chun Liu
  • Ya-Ting Hsu
  • Yi-Li Huang
Focus

Abstract

Recently, researchers have tried to better understand human behaviors so as to let robots act in more human ways, which means a robot may have its own emotions defined by its designers. To achieve this goal, in this study, we designed and simulated a robot, named Shiau_Lu, which is empowered with six universal human emotions, including happiness, anger, fear, sadness, disgust and surprise. When we input a sentence to Shiau_Lu through voice, it recognizes the sentence by invoking the Google speech recognition method running on an Android system, and outputs a sentence to reveal its current emotional states. Each input sentence affects the strength of the six emotional variables used to represent the six emotions, one corresponding to one. After that, the emotional variables will change into new states. The consequent fuzzy inference process infers and determines the most significant emotion as the primary emotion, with which an appropriate output sentence as a response of the input is chosen from its Output-sentence database. With the new states of the six emotional variables, when the robot encounters another sentence, the above process repeats and another output sentence is then selected and replied. Artificial intelligence and psychological theories of human behaviors have been applied to the robot to simulate how emotions are influenced by the outside world through languages. In fact, the robot may help autistic children to interact more with the world around them and relate themselves well to the outside world.

Keywords

Affective computing Fuzzy theory  Artificial intelligence  Emotion recognition  Context awareness 

Notes

Acknowledgments

The work was partially supported by TungHai University under the project GREENs and the National Science Council, Taiwan under Grants NSC 102-2221-E-029-003-MY3, and NSC 100-2221-E-029-018.

References

  1. Andreu J, Angelov P (2013) Towards generic human activity recognition for ubiquitous applications. J Ambient Intell Humaniz Comput 4(2):155–156CrossRefGoogle Scholar
  2. Bellman RE, Kalaba RE, Zadeh LA (1964) Abstraction and pattern classification. RAND CorporationGoogle Scholar
  3. Buckley JJ (1992) Theory of the fuzzy controller: an introduction. Fuzzy Sets Syst 51(3):249–258CrossRefzbMATHMathSciNetGoogle Scholar
  4. Carrino F, Sokhn M, Le Calvé A, Mugellini E, Khaled OA (2013) Personal information management based on semantic technologies. J Ambient Intell Humaniz Comput 4(3):401–407CrossRefGoogle Scholar
  5. Chen Y, Chen Y (2006) Affective computing model based on rough fuzzy sets. IEEE Int Conf Cogn Inf 2:835–838Google Scholar
  6. Chen CW, Kouh JS, Tsai JF (2013) Maneuvering modeling and simulation of AUV dynamic systems with Euler–Rodriguez quaternion method. China Ocean Eng 27(3):403–416 Google Scholar
  7. Coeckelbergh M (2012) Are emotional robots deceptive? IEEE Trans Affect Comput 3(4):388–393Google Scholar
  8. Ekman P (2003) Emotions revealed: recognizing faces and feelings to improve communication and emotional life, Holt Paperbacks, 2nd edn (March 20, 2007). Henry Holt and Company, New YorkGoogle Scholar
  9. Fong B, Westerink J (2012) Affective computing in consumer electronics. IEEE Trans Affect Comput 3(2):129–131CrossRefGoogle Scholar
  10. Fullér R, Zimmermann H-J (1993) Fuzzy reasoning for solving fuzzy mathematical programming problems. Fuzzy Sets Syst 60(2):121–133CrossRefzbMATHGoogle Scholar
  11. He T, Chen H (2010) The mining and analysis of affective law. In: International conference on computational and information sciences, pp 1130–1133Google Scholar
  12. Lanatà A, Valenza G, Scilingo EP (2013) Eye gaze patterns in emotional pictures. J Ambient Intell Humaniz Comput 4(6):705–715CrossRefGoogle Scholar
  13. Lee CM, Narayanan S, Pieraccini R (2001) Recognition of negative emotions from the speech signal. In: IEEE workshop on automatic speech recognition and understanding, pp 240–243Google Scholar
  14. Lee CM, Narayanan S, Pieraccini R (2001) Recognition of negative emotions from the speech signal. In: IEEE workshop on automatic speech recognition and understanding, pp 240–243Google Scholar
  15. Lin S, Zhigang L (2012) Generation of basic emotions for virtual human in the virtual environment. In: IEEE symposium on electrical and electronics, engineering, pp 585–588Google Scholar
  16. Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7(1):1–13CrossRefzbMATHGoogle Scholar
  17. Negoita C (1981) The current interest in fuzzy optimization. Fuzzy Sets Syst 6(3):261–269CrossRefzbMATHMathSciNetGoogle Scholar
  18. Pedrycz W (2010) Human centricity in computing with fuzzy sets: an interpretability quest for higher order granular constructs. J Ambient Intell Humaniz Comput 1(1):65–74CrossRefMathSciNetGoogle Scholar
  19. Scherer KR (2005) What are emotions? And how can they be measured? Social Sci Inf 44(4):695–729CrossRefGoogle Scholar
  20. Sugeno M (1985) An introductory survey of fuzzy control. Inf Sci 36(1–2):59–83CrossRefzbMATHMathSciNetGoogle Scholar
  21. Wu D (2012) Fuzzy sets and systems in building closed-loop affective computing systems for human–computer interaction: advances and new research directions. In: IEEE international conference on fuzzy systems, pp 1–8Google Scholar
  22. Yang M-S (1993) On a class of fuzzy classification maximum likelihood procedures. Fuzzy Sets Syst 57(3):365–375CrossRefzbMATHGoogle Scholar
  23. Yang M-S (1993) A survey of fuzzy clustering. Math Comput Model 18(11):1–16CrossRefzbMATHGoogle Scholar
  24. Young L, Camprodon JA, Hauser M, Pascual-Leone A, Saxe R (2010) Disruption of the right temporoparietal junction with transcranial magnetic stimulation reduces the role of beliefs in moral judgments. Proc Natl Acad Sci 107:6753–6758CrossRefGoogle Scholar
  25. Zadeh LA (1965) Information and control. Fuzzy sets 8(3):338–353zbMATHMathSciNetGoogle Scholar
  26. Zhao Y, Wang X, Goubran M, Whalen T, Petriu EM (2013) Human emotion and cognition recognition from body language of the head using soft computing techniques. J Ambient Intell Humaniz Comput 4(1):121–140CrossRefGoogle Scholar
  27. Zimmermann H-J (1976) Description and optimization of fuzzy system. Int J Gen Syst 2(4):209–215CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Fang-Yie Leu
    • 1
  • Jung-chun Liu
    • 1
  • Ya-Ting Hsu
    • 1
  • Yi-Li Huang
    • 1
  1. 1.Department of Computer ScienceTungHai UniversityTaichungTaiwan

Personalised recommendations