Skip to main content
SpringerLink
Log in

On the Possibility of Regulation of Human Emotions via Multimodal Social Interaction with an Embodied Agent Controlled by eBICA-Based Emotional Interaction Model

  • Conference paper
  • First Online:
Artificial General Intelligence (AGI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13539))

Included in the following conference series:

  • 880 Accesses

Abstract

Embodied social agents are expected to become useful for emotion regulation with applications in the field of service-oriented artificial intelligence. Emotion models can be used to generate an adequate response of the agent in order to achieve a desired emotion regulation effect. Here an eBICA-based model of emotional interaction of embodied social agents is proposed, combining concepts borrowed from biology, cognitive psychology, sociology, and ethics. Based on it, a concept of an embodied emotionally-intelligent agent is developed that will enable natural user emotion regulation during human-computer social interaction. The expected impact includes new smart emotion regulation technologies and new feasible means for emotional communication with and via artifacts.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Gross, J.J.: Handbook of Emotion Regulation, 2nd edn. Guilford, New York (2014)

    Google Scholar 

  2. Jiang, Y., Li, W., Hossain, M.S., Chen, M., Alelaiwi, A., Al-Hammadi, M.: A snapshot research and implementation of multimodal information fusion for data-driven emotion recognition. Inf. Fusion 53, 209–221 (2020)

    Article  Google Scholar 

  3. Wisessing, P., Zibrek, K., Cunningham, D.W., Dingliana, J., McDonnell, R.: Enlighten me: importance of brightness and shadow for character emotion and appeal. ACM Trans. Graph. 39(3), 1–19 (2020)

    Article  Google Scholar 

  4. Gratch, J., Marsella, S.: A domain-independent framework for modeling emotion. Cogn. Syst. Res. 5(4), 269–306 (2004)

    Article  Google Scholar 

  5. Sonlu, S., Güdükbay, U., Durupinar, F.: A conversational agent framework with multi-modal personality expression. ACM Trans. Graph. 40(1), 1–16 (2021)

    Article  Google Scholar 

  6. Ghafurian, M., Budnarain, N., Hoey, J.: Improving humanness of virtual agents and users’ cooperation through emotions. IEEE Trans. Affect. Comput. (2021). https://doi.org/10.1109/TAFCC.2021.3096831

    Article  Google Scholar 

  7. Jain, S., Asawa, K.: Modeling of emotion elicitation conditions for a cognitive-emotive architecture. Cogn. Syst. Res. 55, 60–76 (2019)

    Article  Google Scholar 

  8. Pacheco-Lorenzo, M.R., Valladares-Rodríguez, S.M., Anido-Rifón, L.E., Fernández-Iglesias, M.J.: Smart conversational agents for the detection of neuropsychiatric disorders: a systematic review. J. Biomed. Inform. 113, 103632 (2021). https://doi.org/10.1016/j.jbi.2020.103632

    Article  Google Scholar 

  9. Kaywan, P., Ahmed, K., Miao, Y., Ibaida, A., Gu, B.: DEPRA: an early depression detection analysis chatbot. In: Siuly, S., Wang, H., Chen, L., Guo, Y., Xing, C. (eds.) HIS 2021. LNCS, vol. 13079, pp. 193–204. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-90885-0_18

    Chapter  Google Scholar 

  10. Stratou, G., Morency, L.P.: MultiSense-context-aware nonverbal behavior analysis framework: a psychological distress use case. IEEE Trans. Affect. Comput. 8(2), 190–203 (2017)

    Article  Google Scholar 

  11. Abdollahi, H., Mahoor, M., Zandie, R., Sewierski, J., Qualls, S.: Artificial emotional intelligence in socially assistive robots for older adults: a pilot study. IEEE Trans. Affect. Comput. (2022).https://doi.org/10.1109/TAFFC.2022.3143803

  12. Lara-Alvarez, C., Miter-Hernandez, H., Flores. J.J., Perez-Espinosa, H.: Induction of emotional states in educational video games through a fuzzy control system. IEEE Trans. Affect. Comput. 12(1), 66–77 (2021)

    Google Scholar 

  13. Hong, A., et al.: A multimodal emotional human-robot interaction architecture for social robots engaged in bidirectional communication. IEEE Trans. Cybern. 51(12), 5954–5968 (2021)

    Article  Google Scholar 

  14. Marsella, S.C., Gratch, J.: EMA: a process model of appraisal dynamics. Cogn. Syst. Res. 10(1), 70–90 (2009)

    Article  Google Scholar 

  15. Breazeal, C. Designing Social Robots. MIT Press (2002)

    Google Scholar 

  16. Breazeal, C.: Emotion and sociable humanoid robots. Int. J. Hum. Comput. Stud. 59(1–2), 119–155 (2003)

    Article  Google Scholar 

  17. Cafaro, A., Ravenet, B., Pelachaud, C.: Exploiting evolutionary algorithms to model nonverbal reactions to conversational interruptions in user-agent interactions. IEEE Trans. Affect. Comput. 13(1), 485–495 (2022)

    Article  Google Scholar 

  18. McRorie, M., Sneddon, I., McKeown, G., Bevacqua, E., De Sevin, E., Pelachaud, C.: Evaluation of four designed virtual agent personalities. IEEE Trans. Affect. Comput. 3(3), 311–322 (2012)

    Article  Google Scholar 

  19. Nery, B., Ventura, R.: A dynamical systems approach to online event segmentation in cognitive robotics. J. Behav. Robot. 2(1), 18–24 (2011)

    Article  Google Scholar 

  20. Ventura, R.: Emotions and empathy: a bridge between nature and society? Int. J. Mach. Conscious. 2(2), 343–361 (2010)

    Article  MathSciNet  Google Scholar 

  21. Sellers, M.: Toward a comprehensive theory of emotion for biological and artificial agents. Biologically Inspired Cogn. Architectures 4, 3–26 (2013)

    Article  Google Scholar 

  22. Chella, A.: Some Challenges for Emotional Agents. Int. J. Synth. Emotions 2(2), 70–72 (2011)

    Google Scholar 

  23. Chella, A., Lebiere, C., Noelle, D.C., Samsonovich, A.V.: On a roadmap to biologically inspired cognitive agents. Front. Artif. Intell. Appl. 233, 453–460 (2011)

    Google Scholar 

  24. Tulsulkar, G., Mishra, N., Thalmann, N.M., Lim, H.E., Lee, M.P., Cheng, S.K.: Can a humanoid social robot stimulate the interactivity of cognitively impaired elderly? a thorough study based on computer vision methods. Vis. Comput. 37(12), 3019–3038 (2021)

    Article  Google Scholar 

  25. Marsella, S., Gratch, J., Petta, P.: Computational models of emotion. In: Scherer, K.R., Bänziger, T., Roesch, E. (Eds.) A Blueprint for Affective Computing: A sourcebook and manual. Oxford: Oxford University Press (2010)

    Google Scholar 

  26. Shum, H.-Y., Xiao-dong, H.E., Di, L.I.: From Eliza to xiaoice: challenges and opportunities with social chatbots. Front. Inf. Technol. Electr. Eng. 19(1), 10–26 (2018)

    Article  Google Scholar 

  27. Zhang, K.L., Liu, T.T., Liu, Z., Zhuang, Y., Chai, Y.J.: Multimodal human-computer interactive technology for emotion regulation. J. Image Graph. 25(11), 2451–2464 (2020)

    Google Scholar 

  28. Samsonovich, A.V.: Socially emotional brain-inspired cognitive architecture framework for artificial intelligence. Cogn. Syst. Res. 60, 57–76 (2020)

    Article  Google Scholar 

  29. Bach, J. Principles of Synthetic Intelligence PSI: An Architecture of Motivated Cognition. Oxford University Press (2009)

    Google Scholar 

  30. Bach, J., Coutinho, M., Lichtinger, L.: Extending MicroPsi’s model of motivation and emotion for conversational agents. In: Hammer, P., Agrawal, P., Goertzel, B., Iklé, M. (eds.) AGI 2019. LNCS (LNAI), vol. 11654, pp. 32–43. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27005-6_4

    Chapter  Google Scholar 

  31. Franklin, S., Madl, T., D’Mello, S., Snaider, J.: LIDA: a systems-level architecture for cognition, emotion, and learning. IEEE Trans. Auton. Ment. Dev. 6(1), 19–41 (2014)

    Article  Google Scholar 

  32. Kugele, S., Franklin, S.: Learning in LIDA. Cogn. Syst. Res. 66, 176–200 (2021)

    Article  Google Scholar 

  33. Ortony, A., Clore, G., Collins, A.: The Cognitive Structure of Emotions. Cambridge University Press, Cambridge (1988)

    Book  Google Scholar 

  34. Smith, C.A., Lazarus, R.S.: Emotion and adaptation. In: Pervin, L.A. (ed.) Handbook of personality: Theory and research, pp. 609–637. Guilford, New York (1990)

    Google Scholar 

  35. Scherer, K.R.: Appraisal theory. In: Dalgleish, T., Power, M. (eds.) Handbook of cognition and emotion, pp. 637–663. Wiley, London (1999)

    Google Scholar 

  36. Scherer, K.R.: Appraisal considered as a process of multilevel sequential checking 2001. In K.R. Scherer, A. Shorr, T. Johnstone (Eds.), Appraisal processes in emotion: Theory, methods, research. Canary, NC: Oxford University Press (2001)

    Google Scholar 

  37. Sloman, A., Chrisley, L.: More things than are dreamt of in your biology: Information-processing in biologically inspired robots. Cogn. Syst. Res. 6(2), 145–174 (2005)

    Article  Google Scholar 

  38. Hudlicka, E.: Guidelines for designing computational models of emotions. Int. J. Synth. Emotions 2(1), 26–79 (2011)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the Russian Science Foundation Grant #22-11-00213, https://rscf.ru/en/project/22-11-00213/.

Author information

Authors and Affiliations

  1. National Research Nuclear University MEPhI, 115409, Moscow, Russia

    Alexei V. Samsonovich

  2. Ningbo University, Ningbo, China

    Zhen Liu & Ting Ting Liu

Authors
  1. Alexei V. Samsonovich
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhen Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ting Ting Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alexei V. Samsonovich .

Editor information

Editors and Affiliations

  1. SingularityNET Foundation, Amsterdam, The Netherlands

    Ben Goertzel

  2. SingularityNET Foundation, Amsterdam, The Netherlands

    Matt Iklé

  3. SingularityNET Foundation, Amsterdam, The Netherlands

    Alexey Potapov

  4. Russian Academy of Sciences, Novosibirsk, Russia

    Denis Ponomaryov

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Samsonovich, A.V., Liu, Z., Liu, T.T. (2023). On the Possibility of Regulation of Human Emotions via Multimodal Social Interaction with an Embodied Agent Controlled by eBICA-Based Emotional Interaction Model. In: Goertzel, B., Iklé, M., Potapov, A., Ponomaryov, D. (eds) Artificial General Intelligence. AGI 2022. Lecture Notes in Computer Science(), vol 13539. Springer, Cham. https://doi.org/10.1007/978-3-031-19907-3_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-19907-3_36

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19906-6

  • Online ISBN: 978-3-031-19907-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation