Social Influence of Group Norms Developed by Human-Robot Groups
Yotaro Fuse, Masataka Tokumaru
- 发表年份
- 2020
- 引用次数
- 17
- 访问权限
- 开放获取
摘要
Several studies on how social robots respond, gesture, and display emotion in human-robot interactions have been conducted. In particular, sociality of robots implies that robots do not only exhibit human-like behaviors, but also display a tendency to adapt to a group of individuals. For robots to exhibit sociality, they need to adapt to group norms without telling them how to behave by the group members. In this study, we investigated the effect of group norms on human decision-making in human-robot groups, which comprise two robots using our proposed robotic model. Furthermore, we conducted quizzes with the robots and a human participant using unclear and vague answers. We assessed this influence by making the participant and the two robots repeat a set of actions: to answer the same quiz and recognize each answer of the group members. Additionally, we evaluated the extent to which the group norms changed the opinions of humans using a questionnaire. We analyzed the results of the questionnaire and chronological change in their answers for the quiz with the same question. The quiz experimental results showed that the human participants changed their answers after they discovered the answers of the robots for the first time due to social influence from the robots assumed that the human participants were confused about the diversity of the answers in the group and were aware of the consideration of the robots of the group norm. This is to ensure that they can adjust their answers to the group norm. Moreover, the questionnaire results revealed that the group norms gave the human participants right answers to the quiz that has no correct answers. Therefore, we concluded that robots attempt to comply with a group norm affects human's decision-making.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002