Advertisement

Modelling Empathy in Social Robotic Companions

  • Iolanda Leite
  • André Pereira
  • Ginevra Castellano
  • Samuel Mascarenhas
  • Carlos Martinho
  • Ana Paiva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7138)

Abstract

Empathy can be broadly defined as the ability to understand and respond appropriately to the affective states of others. In this paper, we present a scenario where a social robot acts as a chess companion for children, and describe our current efforts towards endowing such robot with empathic capabilities. A multimodal framework for modeling some of the user’s affective states that combines visual and task-related features is presented. Using this model of the user, we personalise the learning environment by adapting the robot’s empathic responses to the particular preferences of the child who is interacting with the robot. We also describe a preliminary study conducted in this scenario.

Keywords

social robots empathy affective user modeling adaptive interaction 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aghaei Pour, P., Hussain, M.S., AlZoubi, O., D’Mello, S., Calvo, R.A.: The Impact of System Feedback on Learners’ Affective and Physiological States. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010. LNCS, vol. 6094, pp. 264–273. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Anderson, C., Keltner, D.: The role of empathy in the formation and maintenance of social bonds. Behavioral and Brain Sciences 25(01), 21–22 (2002)Google Scholar
  3. 3.
    Arroyo, I., Ferguson, K., Johns, J., Dragon, T., Meheranian, H., Fisher, D., Barto, A., Mahadevan, S., Woolf, B.P.: Repairing disengagement with non-invasive interventions. In: Proc. of AIED 2007, pp. 195–202. IOS Press, Amsterdam (2007)Google Scholar
  4. 4.
    Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Machine Learning 47(2), 235–256 (2002)CrossRefzbMATHGoogle Scholar
  5. 5.
    Batson, C.D., Early, S., Salvarani, G.: Perspective taking: Imagining how another feels versus imaging how you would feel. Personality and Social Psychology Bulletin 23(7), 751 (1997)CrossRefGoogle Scholar
  6. 6.
    Bickmore, T., Schulman, D., Yin, L.: Maintaining engagement in long-term interventions with relational agents. Applied Artificial Intelligence 24(6), 648–666 (2010)CrossRefGoogle Scholar
  7. 7.
    Castellano, G., Leite, I., Pereira, A., Martinho, C., Paiva, A., McOwan, P.W.: It’s all in the game: Towards an affect sensitive and context aware game companion. In: Proceedings of ACII 2009, pp. 29–36. IEEE (2009)Google Scholar
  8. 8.
    Castellano, G., Leite, I., Pereira, A., Martinho, C., Paiva, A., McOwan, P.W.: Affect recognition for interactive companions: Challenges and design in real-world scenarios. Journal on Multimodal User Interfaces 3(1-2), 89–98 (2010)CrossRefGoogle Scholar
  9. 9.
    Castellano, G., Pereira, A., Leite, I., Paiva, A., McOwan, P.W.: Detecting user engagement with a robot companion using task and social interaction-based features. In: Proceedings of ICMI 2009, pp. 119–126. ACM Press (2009)Google Scholar
  10. 10.
    Conati, C., Maclaren, H.: Empirically building and evaluating a probabilistic model of user affect. User Modeling and User-Adapted Interaction 19(3), 267–303 (2009)CrossRefGoogle Scholar
  11. 11.
    Cooper, B., Brna, P., Martins, A.: Effective Affective in Intelligent Systems: Building on Evidence of Empathy in Teaching and Learning. In: Paiva, A.M. (ed.) IWAI 1999. LNCS, vol. 1814, pp. 21–34. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  12. 12.
    Craig, S., Graesser, A., Sullins, J., Gholson, B.: Affect and learning: an exploratory look into the role of affect in learning with AutoTutor. Learning, Media and Technology 29(3), 241–250 (2004)Google Scholar
  13. 13.
    Csikszentmihalyi, M.: Flow: The psychology of optimal experience. Harper Perennial, New York (1991)Google Scholar
  14. 14.
    Deutsch, F., Madle, R.A.: Empathy: Historic and current conceptualizations, measurement, and a cognitive theoretical perspective. Human Development 18(4), 267–287 (1975)CrossRefGoogle Scholar
  15. 15.
    D’Mello, S., Craig, S., Fike, K., Graesser, A.: Responding to learners’ cognitive-affective states with supportive and shakeup dialogues. In: Human-Computer Interaction. Ambient, Ubiquitous and Intelligent Interaction, pp, 595–604 (2009)Google Scholar
  16. 16.
    D’Mello, S., Picard, R.W., Graesser, A.: Toward an affect-sensitive autotutor. IEEE Intelligent Systems, 53–61 (2007)Google Scholar
  17. 17.
    Domagk, S.: Do pedagogical agents facilitate learner motivation and learning outcomes?: The role of the appeal of agent’s appearance and voice. Journal of Media Psychology 22(2), 84–97 (2010)CrossRefGoogle Scholar
  18. 18.
    Efklides, A., Petkaki, C.: Effects of mood on students’ metacognitive experiences. Learning and Instruction 15(5), 415–431 (2005)CrossRefGoogle Scholar
  19. 19.
    Hoffman, M.L.: Empathy and moral development: Implications for caring and justice. Cambridge Univ. Press (2001)Google Scholar
  20. 20.
    Horgan, D.D., Morgan, D.: Chess expertise in children. Applied Cognitive Psychology 4(2), 109–128 (1990)CrossRefGoogle Scholar
  21. 21.
    Isen, A.M., Reeve, J.: The influence of positive affect on intrinsic and extrinsic motivation: Facilitating enjoyment of play, responsible work behavior, and self-control. Motivation and Emotion 29(4), 295–323 (2005)CrossRefGoogle Scholar
  22. 22.
    Kapoor, A., Burleson, W., Picard, R.W.: Automatic prediction of frustration. International Journal of Human-Computer Studies 65(8), 724–736 (2007)CrossRefGoogle Scholar
  23. 23.
    Kapoor, A., Picard, R.W.: Multimodal affect recognition in learning environments. In: ACM International Conference on Multimedia, pp. 677–682 (2005)Google Scholar
  24. 24.
    Keltner, D., Ellsworth, P.C., Edwards, K.: Beyond simple pessimism: Effects of sadness and anger on social perception. Journal of Personality and Social Psychology 64, 740–740 (1993)CrossRefGoogle Scholar
  25. 25.
    Leite, I., Martinho, C., Pereira, A., Paiva, A.: iCat: an affective game buddy based on anticipatory mechanisms. In: Proceedings of AAMAS 2008, pp. 1229–1232. IFAAMAS (2008)Google Scholar
  26. 26.
    Leite, I., Martinho, C., Pereira, A., Paiva, A.: As Time goes by: Long-term evaluation of social presence in robotic companions. In: Proceedings of RO-MAN 2009, pp. 669–674. IEEE (2009)Google Scholar
  27. 27.
    Leite, I., Mascarenhas, S., Pereira, A., Martinho, C., Prada, R., Paiva, A.: ”Why Can’t We Be Friends?” An Empathic Game Companion for Long-Term Interaction. In: Safonova, A. (ed.) IVA 2010. LNCS, vol. 6356, pp. 315–321. Springer, Heidelberg (2010)Google Scholar
  28. 28.
    Lester, J.C., Converse, S.A., Kahler, S.E., Barlow, S.T., Stone, B.A., Bhogal, R.S.: The persona effect: affective impact of animated pedagogical agents. In: Proceedings of CHI 1997, pp. 359–366. ACM, NY (1997)Google Scholar
  29. 29.
    Nakano, Y.I., Ishii, R.: Estimating user’s engagement from eye-gaze behaviors in human-agent conversations. In: Proceeding of IUI 2010, pp. 139–148. ACM, New York (2010)Google Scholar
  30. 30.
    Robison, J., McQuiggan, S., Lester, J.: Evaluating the consequences of affective feedback in intelligent tutoring systems. In: Proceedings of ACII 2009. IEEE (2009)Google Scholar
  31. 31.
    Russell, J.A.: A circumplex model of affect. Journal of Personality and Social Psychology 39, 1161–1178 (1980)CrossRefGoogle Scholar
  32. 32.
    Saerbeck, M., Schut, T., Bartneck, C., Janse, M.: Expressive robots in education: varying the degree of social supportive behavior of a robotic tutor. In: Proceedings of CHI 2010, pp. 1613–1622. ACM (2010)Google Scholar
  33. 33.
    Sanghvi, J., Castellano, G., Leite, I., Pereira, A., McOwan, P.W., Paiva, A.: Automatic analysis of affective postures and body motion to detect engagement with a game companion. In: ACM/IEEE International Conference on Human-Robot Interaction, Lausanne, Switzerland (2011)Google Scholar
  34. 34.
    Sidner, C.L., Kidd, C.D., Lee, C., Lesh, N.: Where to look: a study of human-robot engagement. In: Proc. of IUI 2004, pp. 78–84. ACM (2004)Google Scholar
  35. 35.
    Tiberius, R.G.: The why of teacher/student relationships. Essays on Teaching Excellence 31 (2003)Google Scholar
  36. 36.
    van Breemen, A., Yan, X., Meerbeek, B.: iCat: an animated user-interface robot with personality. In: Proc. of AAMAS 2005, pp. 143–144. ACM (2005)Google Scholar
  37. 37.
    Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., Picard, R.: Affect-aware tutors: recognising and responding to student affect. International Journal of Learning Technology 4(3), 129–164 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Iolanda Leite
    • 1
  • André Pereira
    • 1
  • Ginevra Castellano
    • 2
  • Samuel Mascarenhas
    • 1
  • Carlos Martinho
    • 1
  • Ana Paiva
    • 1
  1. 1.INESC-ID and Instituto Superior TécnicoTechnical University of LisbonPortugal
  2. 2.HCI Centre, School of Electronic, Electrical and Computer EngineeringUniversity of BirminghamUnited Kingdom

Personalised recommendations