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JACIII Vol.24 No.1 pp. 169-178
doi: 10.20965/jaciii.2020.p0169
(2020)

Paper:

A Robot in a Human–Robot Group Learns Group Norms and Makes Decisions Through Indirect Mutual Interaction with Humans

Yotaro Fuse*, Hiroshi Takenouchi**, and Masataka Tokumaru***

*Kansai University Graduate School
3-3-35 Yamate-cho, Suita-shi, Osaka 564-8680, Japan

**Fukuoka Institute of Technology
3-30-1 Wajiro-higashi, Higashi-ku, Fukuoka 811-0295, Japan

***Kansai University
3-3-35 Yamate-cho, Suita-shi, Osaka 564-8680, Japan

Received:
April 18, 2019
Accepted:
November 18, 2019
Published:
January 20, 2020
Keywords:
social robotics, human–robot group, group norm, human–robot interaction
Abstract

In this study, we investigate whether group norms occur in human–robot groups. At present, there are a number of studies that examine social robots’ ways of responding, gesturing, and displaying emotion. However, sociality implies that robots not only exhibit human-like behaviors, but also display the tendency to adapt to a group of individuals. For robots to exhibit sociality, they must adapt to group norms without being told by the group members how to behave. Group norms refer to the unwritten, unspoken, and informal rules that are present in a group of individuals. In a previous study, we demonstrated that a robot model learned group norms in human groups [1]. In the present study, we investigate whether group norms occur in human–robot groups. To this end, we prepared quizzes with unclear and vague answers, and instructed participants to take the quizzes with the robot. The results of the quiz experiments demonstrated that the robot considered group norms in human–robot groups when making decisions; thus, group norms occurred in human–robot groups.

Robot and laptop used in experiment

Robot and laptop used in experiment

Cite this article as:
Y. Fuse, H. Takenouchi, and M. Tokumaru, “A Robot in a Human–Robot Group Learns Group Norms and Makes Decisions Through Indirect Mutual Interaction with Humans,” J. Adv. Comput. Intell. Intell. Inform., Vol.24 No.1, pp. 169-178, 2020.
Data files:
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