Toward a Context-Based Approach to Assess Engagement in Human-Robot Social Interaction

  • Laurence Devillers
  • Guillaume Dubuisson Duplessis
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 427)


This article addresses the issue of evaluating Human-Robot spoken interactions in a social context by considering the engagement of the human participant. Our work regards the Communication Accommodation Theory (CAT) as a promising paradigm to consider for engagement, by its study of macro- and micro-contexts influencing the behaviour of dialogue participants (DPs), and by its effort in depicting the accommodation process underlying the behaviour of DPs. We draw links between the accommodation process described in this theory and human engagement that could be fruitfully used to evaluate Human-Robot social interactions. We present our goal which is to take into account a model of dialogue activities providing a convenient local interpretation context to assess human contributions (involving verbal and nonverbal channels) along with CAT to assess Human-Robot social interaction.


Human-robot interaction Social dialogue Communication accommodation theory Engagement 



This work has been funded by the JOKER project and supported by ERA-Net CHIST-ERA, and the “Agence Nationale pour la Recherche” (ANR, France).


  1. 1.
    Walker, M.A., Litman, D.J., Kamm, C.A., Abella, A.: PARADISE: a framework for evaluating spoken dialogue agents. In: Proceedings of the Eighth Conference on European Chapter of the Association for Computational Linguistics, pp. 271–280. Association for Computational Linguistics (1997)Google Scholar
  2. 2.
    Möller, S., Ward, N.G.: A framework for model-based evaluation of spoken dialog systems. In: Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue, pp. 182–189. Association for Computational Linguistics (2008)Google Scholar
  3. 3.
    Glas, N., Pelachaud, C.: Definitions of engagement in human-agent interaction. In: Proceedings of the International Workshop on Engagement in Human Computer Interaction (ENHANCE), pp. 944–949 (2015)Google Scholar
  4. 4.
    Corrigan, L.J., Peters, C., Castellano, G., Papadopoulos, F., Jones, A., Bhargava, S., Janarthanam, S., Hastie, H., Deshmukh, A., Aylett, R.: Social-task engagement: striking a balance between the robot and the task. In: Proceedings of the Embodied Communication of Goals Intentions Workshop ICSR, vol. 13, pp. 1–7 (2013)Google Scholar
  5. 5.
    Sidner, C.L., Lee, C., Kidd, C.D., Lesh, N., Rich, C.: Explorations in engagement for humans and robots. Artif. Intell. 166(1), 140–164 (2005)CrossRefGoogle Scholar
  6. 6.
    Rich, C., Sidner, C.L.: Collaborative discourse, engagement and always-on relational agents. In: Proceedings of the AAAI Fall Symposium: Dialog with Robots (2010)Google Scholar
  7. 7.
    Bohus, D., Horvitz, E.: Models for multiparty engagement in open-world dialog. In: Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 225–234. Association for Computational Linguistics (2009)Google Scholar
  8. 8.
    Bohus, D., Horvitz, E.: Learning to predict engagement with a spoken dialog system in open-world settings. In: Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 244–252. Association for Computational Linguistics (2009)Google Scholar
  9. 9.
    Salam, H., Chetouani, M.: A multi-level context-based modeling of engagement in human-robot interaction. In: Proceedings of the International Workshop on Context Based Affect Recognition (2015)Google Scholar
  10. 10.
    Gallois, C., Ogay, T., Giles, Howard, H.: Communication accommodation theory: a look back and a look ahead. In: Gudykunst, W. (ed.) Theorizing About Intercultural Communication, pp. 121–148. Sage, Thousand Oaks (2005)Google Scholar
  11. 11.
    Bunt, H.: The DIT++ taxonomy for functional dialogue markup. In: Proceedings of the AAMAS Workshop, Towards a Standard Markup Language for Embodied Dialogue Acts, pp. 13–24 (2009)Google Scholar
  12. 12.
    Schuller, B., Steidl, S., Batliner, A., Burkhardt, F., Devillers, L., Müller, C., Narayanan, S.: Paralinguistics in speech and language–state-of-the-art and the challenge. Comput. Speech Lang. 27(1), 4–39 (2013)Google Scholar
  13. 13.
    McCrae, R.R., John, O.: An introduction to the five-factor model and its applications. J. Pers. 60, 175–215 (1992)CrossRefGoogle Scholar
  14. 14.
    Dubuisson Duplessis, G., Devillers, L.: Towards the consideration of dialogue activities in engagement measures for human-robot social interaction. In: Proceedings of the workshop Designing & Evaluating Social Robots for Public Settings, International Conference on Intelligent Robots and Systems, pp. 19–24, Hambourg, Germany (Sep 2015)Google Scholar
  15. 15.
    Clark, H.: Using language, vol. 4. Cambridge University Press (1996)Google Scholar
  16. 16.
    Schegloff, E.A., Sacks, H.: Opening up closings. Semiotica 8(4), 289–327 (1973)CrossRefGoogle Scholar
  17. 17.
    Devillers, L., Rosset, S., Dubuisson Duplessis, G., Sehili, M., Béchade, L., Delaborde, A., Gossart, C., Letard, V., Yang, F., Yemez, Y., Türker, B., Sezgin, M., El Haddad, K., Dupont, S., Luzzati, D., Estève, Y., Gilmartin, E., Campbell, N.: Multimodal data collection of human-robot humorous interactions in the Joker project. In: Proceedings of the 6th International Conference on Affective Computing and Intelligent Interaction (ACII) (2015)Google Scholar
  18. 18.
    Devillers, L., Tahon, M., Sehili, M.A., Delaborde, A.: Inference of human beings’ emotional states from speech in human–robot interactions. Int. J. Soc. Robot. 1–13 (2015)Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  • Laurence Devillers
    • 1
    • 2
  • Guillaume Dubuisson Duplessis
    • 1
  1. 1.LIMSI, CNRS, Université Paris-SaclayOrsayFrance
  2. 2.Sorbonne Universités, Université Paris-SorbonneParisFrance

Personalised recommendations