Gale M. Lucas
Emmanuel Johnson
Mikio Nakano
ABSTRACT
Social robots establish rapport with human users. This work explores the extent to which rapport-building can benefit (or harm) conversations with robots, and under what circumstances this occurs. For example, previous work has shown that agents that make conversational errors are less capable of influencing people than agents that do not make errors [1]. Some work has shown this effect with robots, but prior research has not considered additional factors such as the level of rapport between the person and the robot. We predicted that building rapport through a social dialogue (such as an ice-breaker) could mitigate the detrimental effect of a robot's errors on influence. Our study used a Nao robot programmed to persuade users to agree with its rankings on two "survival tasks" (e.g., lunar survival task). We manipulated both errors and social dialogue:the robot either exhibited errors in the second survival task or not, and users either engaged in an ice-breaker with the robot between the two survival tasks or completed a control task. Replicating previous research, errors tended to reduce the robot's influence in the second survival task. Contrary to our prediction, results revealed that the ice-breaker did not mitigate the effect of errors, and if anything, errors were more harmful after the ice-breaker (intended to build rapport) than in the control condition. This backfiring of attempted rapport-building may be due to a contrast effect, suggesting that the design of social robots should avoid introducing dialogues of incongruent quality.
- Wang, Y., P. Khooshabeh, et al. (2013). Looking Real and Making Mistakes. Intelligent Virtual Agents:13th International Conference, IVA 2013, Edinburgh, UK, August 29--31, 2013. Proceedings. R. Aylett, B. Krenn, C. Pelachaud and H. Shimodaira. Berlin, Heidelberg:Springer:339--348. Google Scholar
Cross Ref
- Kanda, T., Shiomi, M., Miyashita, Z., Ishiguro, H., & Hagita, N. (2010). A communication robot in a shopping mall. IEEE Transactions on Robotics, 26(5), 897--913. Google ScholarDigital Library
- Cassell, J., & Bickmore, T. (2003). Negotiated collusion:Modeling social language and its relationship effects in intelligent agents. User modeling and user-adapted interaction 13(1):89--132. Google ScholarDigital Library
- Traum, D., Swartout, W., Marsella, S., & Gratch, J. (2005). Fight, flight, or negotiate:Believable strategies for conversing under crisis. In Proceedings of Intelligent Virtual Agents, 52--64. Google ScholarDigital Library
- Manuvinakurike, R., Velicer, W. F., & Bickmore, T. W. (2014). Automated indexing of Internet stories for health behavior change:weight loss attitude pilot study. Journal of medical Internet research, 16(12). Google ScholarCross Ref
- Hiraoka, T., Neubig, G., Sakti, S., Toda, T., & Nakamura, S. (2014, August). Reinforcement Learning of Cooperative Persuasive Dialogue Policies using Framing. In COLING (pp. 1706--1717).Google Scholar
- Blascovich, J., & McCall, C. (2013). Social influence in virtual environments. In K. Dill (Ed.), The Oxford handbook of media psychology (pp. 305--315). New York, NY:Oxford University Press.Google Scholar
- Salem, M., Lakatos, G., Amirabdollahian, F., & Dautenhahn, K. (2015, March). Would you trust a (faulty) robot?:Effects of error, task type and personality on human-robot cooperation and trust. In Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction (pp. 141--148). ACM.Google ScholarDigital Library
- Desai, M., Medvedev, M., Vázquez, M., McSheehy, S., Gadea-Omelchenko, S., Bruggeman, C., ... & Yanco, H. (2012, March). Effects of changing reliability on trust of robot systems. In Human-Robot Interaction (HRI), 2012 7th ACM/IEEE International Conference on (pp. 73--80). IEEE. Google ScholarDigital Library
- Desai, M., Kaniarasu, P., Medvedev, M., Steinfeld, A., & Yanco, H. (2013, March). Impact of robot failures and feedback on real-time trust. In Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction (pp. 251--258). IEEE. Google ScholarCross Ref
- Wiegmann, D. A., Rich, A., & Zhang, H. (2001). Automated diagnostic aids:The effects of aid reliability on users' trust and reliance. Theoretical Issues in Ergonomics Science, 2(4), 352--367. Google ScholarCross Ref
- Moon, Y. (1998, January). The effects of distance in local versus remote human-computer interaction. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 103--108). ACM Press/Addison-Wesley Publishing Co. Google ScholarDigital Library
- Khooshabeh, P., McCall, C., Gandhe, S., Gratch, J., & Blascovich, J. (2011, May). Does it matter if a computer jokes. In CHI'11 Extended Abstracts on Human Factors in Computing Systems (pp. 77--86). ACM. Google ScholarDigital Library
- Ramachandran, D., & J. Canny (2008). The persuasive power of human-machine dialogue. International Conference on Persuasive Technology, Springer. Google ScholarDigital Library
- Kelley, J. F. (1984). An iterative design methodology for user-friendly natural language office information applications. ACM Transactions on Office Information Systems, 2, 26--41. Google ScholarDigital Library
- Artstein, R., Traum, D., Boberg, J., Gainer, A., Gratch, J., Johnson, E., Leuski, A., & Nakano, M. (2017). Listen to my body:Does making friends help influence people? In Proceedings of the Florida Artificial Intelligence Research Society Conference, 430--435.Google Scholar
- Sherman, S. J., Ahlm, K., Berman, L., & Lynn, S. (1978). Contrast effects and their relationship to subsequent behavior. Journal of Experimental Social Psychology, 14(4), 340--350. Google ScholarCross Ref
Index Terms
- The Role of Social Dialogue and Errors in Robots
Recommendations
Getting to Know Each Other: The Role of Social Dialogue in Recovery from Errors in Social Robots
HRI '18: Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot InteractionThis work explores the extent to which social dialogue can mitigate (or exacerbate) the loss of trust caused when robots make conversational errors. Our study uses a NAO robot programmed to persuade users to agree with its rankings on two tasks. We ...
Culture, Errors, and Rapport-building Dialogue in Social Agents
IVA '18: Proceedings of the 18th International Conference on Intelligent Virtual AgentsThis work explores whether culture impacts the extent to which social dialogue can mitigate (or exacerbate) the loss of trust caused when agents make conversational errors. Our study uses an agent designed to persuade users to agree with its rankings on ...
Effects of Voice-Adaptation and Social Dialogue on Perceptions of a Robotic Learning Companion
HRI '16: The Eleventh ACM/IEEE International Conference on Human Robot InteractionWith a growing number of applications involving social human-robot interactions, there is an increasingly important role for socially responsive speech interfaces that can effectively engage the user. For example, learning companions provide both task-...
Comments