“I Choose... YOU!” Membership preferences in human–robot teams

Abstract

Although groups of robots are expected to interact with groups of humans in the near future, research related to teams of humans and robots is still scarce. This paper contributes to the study of human–robot teams by describing the development of two autonomous robotic partners and by investigating how humans choose robots to partner with in a multi-party game context. Our work concerns the successful development of two autonomous robots that are able to interact with a group of two humans in the execution of a task for social and entertainment purposes. The creation of these two characters was motivated by psychological research on learning goal theory, according to which we interpret and approach a given task differently depending on our learning goal. Thus, we developed two robotic characters implemented in two robots: Emys (a competitive robot, based on characteristics related to performance-orientation goals) and Glin (a relationship-driven robot, based on characteristics related to learning-orientation goals). In our study, a group of four (two humans and two autonomous robots) engaged in a card game for social and entertainment purposes. Our study yields several important conclusions regarding groups of humans and robots. (1) When a partner is chosen without previous partnering experience, people tend to prefer robots with relationship-driven characteristics as their partners compared with competitive robots. (2) After some partnering experience has been gained, the choice becomes less clear, and additional driving factors emerge as follows: (2a) participants with higher levels of competitiveness (personal characteristics) tend to prefer Emys, whereas those with lower levels prefer Glin, and (2b) the choice of which robot to partner with also depends on team performance, with the winning team being the preferred choice.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3

Notes

  1. 1.

    Video explaining the methodology design of the two studies reported in the present paper: https://youtu.be/rwvBIDsN6Cc.

  2. 2.

    Value of the 2nd lowest card.

  3. 3.

    Value of the 2nd highest card.

References

  1. Bartneck, C., Kulić, D., Croft, E., & Zoghbi, S. (2009). Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. International Journal of Social Robotics, 1(1), 71–81.

    Article  Google Scholar 

  2. Bornstein, G., & Yaniv, I. (1998). Individual and group behavior in the ultimatum game: Are groups more “rational” players? Experimental Economics, 1(1), 101–108.

    MATH  Article  Google Scholar 

  3. Breazeal, C., Kidd, C. D., Thomaz, A. L., Hoffman, G., & Berlin, M. (2005). Effects of nonverbal communication on efficiency and robustness in human–robot teamwork. In: 2005 IEEE/RSJ international conference on intelligent robots and systems (pp. 708–713). IEEE.

  4. Buro, M., Long, J. R., Furtak, T., & Sturtevant, N. R. (2009). Improving state evaluation, inference, and search in trick-based card games. In: IJCAI (pp. 1407–1413).

  5. Chang, W. L., White, J. P., Park, J., Holm, A., & Šabanović, S. (2012). The effect of group size on people’s attitudes and cooperative behaviors toward robots in interactive gameplay. In: RO-MAN, 2012 IEEE (pp. 845–850). IEEE.

  6. Coradeschi, S., & Saffiotti, A. (2006). Symbiotic robotic systems: Humans, robots, and smart environments. IEEE Intelligent Systems, 21(3), 82–84.

    Article  Google Scholar 

  7. Correia, F., Alves-Oliveira, P., Maia, N., Ribeiro, T., Petisca, S., Melo, F. S., et al. (2016). Just follow the suit! trust in human–robot interactions during card game playing. In: 2016 25th IEEE international symposium on robot and human interactive communication (RO-MAN) (pp. 507–512). IEEE.

  8. Correia, F., Alves-Oliveira, P., Ribeiro, T., Melo, F. S., & Paiva, A. (2017). A social robot as a card game player. In: 13th AAAI conference on artificial intelligence and interactive digital entertainment.

  9. Correia, F., Mascarenhas, S., Prada, R., Melo, F. S., & Paiva, A. (2018). Group-based emotions in teams of humans and robots. In: Proceedings of the 2018 ACM/IEEE international conference on human–robot interaction (pp. 261–269). ACM.

  10. Dweck, C. S. (1986). Motivational processes affecting learning. American Psychologist, 41(10), 1040.

    Article  Google Scholar 

  11. Eison, J. A. (1979). The development and validation of a scale to assess differing student orientations towards grades and learning. PhD thesis, University of Tennessee, Knoxville.

  12. Eyssel, F., & Kuchenbrandt, D. (2012). Social categorization of social robots: Anthropomorphism as a function of robot group membership. British Journal of Social Psychology, 51(4), 724–731.

    Article  Google Scholar 

  13. Fraune, M. R., Kawakami, S., Šabanović, S., De Silva, P. R. S., & Okada, M. (2015). Three’s company, or a crowd?: The effects of robot number and behavior on HRI in Japan and the USA. In: Proceedings of robotics: Science and systems. Rome, Italy. https://doi.org/10.15607/RSS.2015.XI.033.

  14. Fraune, M., Nishiwaki, Y., Šabanović, S., Smith, E., & Okada, M. (2017). threatening flocks and mindful snowflakes: How group entitativity affects perceptions of robots. In: International conference on human–robot interaction, HRI. ACM Press (to appear).

  15. Gates, B. (2007). A robot in every home. Scientific American, 296(1), 58–65.

    Article  Google Scholar 

  16. Ginsberg, M. L. (2001). Gib: Imperfect information in a computationally challenging game. Journal of Artificial Intelligence Research, 14, 303–358.

    MATH  Article  Google Scholar 

  17. Groom, V., & Nass, C. (2007). Can robots be teammates? Benchmarks in human–robot teams. Interaction Studies, 8(3), 483–500.

    Article  Google Scholar 

  18. Hendrick, S. S. (1988). A generic measure of relationship satisfaction. Journal of Marriage and the Family, 50(1), 93–98.

    Article  Google Scholar 

  19. Hinds, P. J., Carley, K. M., Krackhardt, D., & Wholey, D. (2000). Choosing work group members: Balancing similarity, competence, and familiarity. Organizational Behavior and Human Decision Processes, 81(2), 226–251.

    Article  Google Scholar 

  20. Hoffman, G., & Breazeal, C. (2007). Effects of anticipatory action on human-robot teamwork efficiency, fluency, and perception of team. In: Proceedings of the ACM/IEEE international conference on Human–robot interaction (pp. 1–8). ACM.

  21. Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2), 65–70.

    MathSciNet  MATH  Google Scholar 

  22. Jung, M. F., Lee, J. J., DePalma, N., Adalgeirsson, S. O., Hinds, P. J., & Breazeal, C. (2013). Engaging robots: easing complex human–robot teamwork using backchanneling. In: Proceedings of the 2013 conference on computer supported cooperative work (pp. 1555–1566). ACM.

  23. Kedzierski, J., Muszyński, R., Zoll, C., Oleksy, A., & Frontkiewicz, M. (2013). Emys—Emotive head of a social robot. International Journal of Social Robotics, 5(2), 237–249.

    Article  Google Scholar 

  24. Kuchenbrandt, D., Eyssel, F., Bobinger, S., & Neufeld, M. (2013). When a robot’s group membership matters. International Journal of Social Robotics, 5(3), 409–417.

    Article  Google Scholar 

  25. Lee, K. M., Peng, W., Jin, S. A., & Yan, C. (2006). Can robots manifest personality? An empirical test of personality recognition, social responses, and social presence in human–robot interaction. Journal of Communication, 56(4), 754–772.

    Article  Google Scholar 

  26. Mendelson, M. J., & Aboud, F. E. (1999). Measuring friendship quality in late adolescents and young adults: Mcgill friendship questionnaires. Canadian Journal of Behavioural Science, 31(2), 130.

    Article  Google Scholar 

  27. Nass, C., & Moon, Y. (2000). Machines and mindlessness: Social responses to computers. Journal of Social Issues, 56(1), 81–103.

    Article  Google Scholar 

  28. Oliveira, R., Arriaga, P., Alves-Oliveira, P., Correia, F., Petisca, S., & Paiva, A. (2018). Friends or foes? Socioemotional support and gaze behaviors in mixed groups of humans and robots. In: Proceedings of the 2018 ACM/IEEE international conference on human–robot interaction (pp. 279–288). ACM.

  29. Ortony, A., Clore, G. L., & Collins, A. (1990). The cognitive structure of emotions. Cambridge: Cambridge University Press.

    Google Scholar 

  30. Porter, C. O. (2005). Goal orientation: Effects on backing up behavior, performance, efficacy, and commitment in teams. Journal of Applied Psychology, 90(4), 811.

    Article  Google Scholar 

  31. Reeves, B., & Nass, C. (1996). How people treat computers, television, and new media like real people and places. Cambridge: CSLI Publications and Cambridge.

    Google Scholar 

  32. Rus, D., Donald, B., & Jennings, J. (1995). Moving furniture with teams of autonomous robots. In: Proceedings of 1995 IEEE/RSJ international conference on intelligent robots and systems 95. ‘Human robot interaction and cooperative robots’ (Vol. 1, pp. 235–242). IEEE.

  33. Russell, S., Norvig, P., & Intelligence, A. (1995). A modern approach. Artificial intelligence (Vol. 25, p. 27). Egnlewood Cliffs: Prentice-Hall.

    MATH  Google Scholar 

  34. Shah, J., Wiken, J., Williams, B., & Breazeal, C. (2011). Improved human–robot team performance using Chaski, a human-inspired plan execution system. In: Proceedings of the 6th international conference on human–robot interaction (pp. 29–36). ACM

  35. Smither, R. D., & Houston, J. M. (1992). The nature of competitiveness: The development and validation of the competitiveness index. Educational and Psychological Measurement, 52(2), 407–418.

    Article  Google Scholar 

  36. Sturtevant, N. R. (2008). An analysis of UCT in multi-player games. In: International conference on computers and games (pp. 37–49). Springer.

  37. Tapus, A., Ţăpuş, C., & Matarić, M. J. (2008). User–robot personality matching and assistive robot behavior adaptation for post-stroke rehabilitation therapy. Intelligent Service Robotics, 1(2), 169–183.

    Article  Google Scholar 

  38. Walters, M. L., Dautenhahn, K., Te Boekhorst, R., Koay, K. L., Kaouri, C., Woods, S., et al. (2005). The influence of subjects’ personality traits on personal spatial zones in a human–robot interaction experiment. In: IEEE international workshop on robot and human interactive communication, 2005. ROMAN 2005 (pp. 347–352). IEEE.

Download references

Acknowledgements

This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT-UID/CEC/500 21/2013), through the Project AMIGOS (PTDC/EEISII/7174/2014), and through the Project LAW TRAIN (Ref. H2020-FCT-2014/653587). Filipa Correia, Sofia Petisca, and Patrícia Alves-Oliveira acknowledge their FCT Grants (Refs. SFRH/BD/118031/2016, SFRH/BD/118013/2016, and SFRH/BD/110223/2015, respectively).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Filipa Correia.

Additional information

The present paper is an extended version of the work in the article “Groups of humans and robots: understanding membership preferences and team formation”, published in the Proceedings of Robotics: Science and Systems (2017), with the https://doi.org/10.15607/RSS.2017.XIII.024. The present version includes a detailed description of the autonomous robots’ development, not included in the aforementioned article. It also includes a significantly improved discussion of our results in terms of human–robot collaboration.

This is one of several papers published in Autonomous Robots comprising the “Special Issue on Robotics Science and Systems”.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Correia, F., Petisca, S., Alves-Oliveira, P. et al. “I Choose... YOU!” Membership preferences in human–robot teams. Auton Robot 43, 359–373 (2019). https://doi.org/10.1007/s10514-018-9767-9

Download citation

Keywords

  • Social robots
  • Human–robot teams
  • Collaboration