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ORIGINAL RESEARCH article

Front. Robot. AI
Sec. Human-Robot Interaction
Volume 11 - 2024 | doi: 10.3389/frobt.2024.1407095
This article is part of the Research Topic Creative Approaches to Appropriation and Design: Novel Robotic Systems for Heterogeneous Contexts View all 5 articles

Predicting Humor Effectiveness of Robots for Human Line Cutting

Provisionally accepted
Yuto Ushijima Yuto Ushijima 1*Satoru Satake Satoru Satake 2Takayuki Kanda Takayuki Kanda 1
  • 1 Kyoto University, Kyoto, Japan
  • 2 Deep Interaction Laboratories, ATR, Kyoto, Japan

The final, formatted version of the article will be published soon.

    It is extremely challenging for security guard robots to independently stop human line-cutting behavior. We propose addressing this issue by using humorous phrases. First, we created a dataset and built a humor effectiveness predictor. Using a simulator, we replicated 13,000 situations of line-cutting behavior and collected 500 humorous phrases through crowdsourcing.Combining these simulators and phrases, we evaluated each phrase's effectiveness in different situations through crowdsourcing. Using machine learning with this dataset, we constructed a humor effectiveness predictor. In the process of preparing this machine learning, we discovered that considering the situation and the discomfort caused by the phrase is crucial for predicting the effectiveness of humor. Next, we constructed a system to select the best humorous phrase for the line-cutting behavior using this predictor. We then conducted a video experiment in which we compared the humorous phrases selected using this proposed system with typical non-humorous phrases. The results revealed that humorous phrases selected by the proposed system were more effective in discouraging line-cutting behavior than typical non-humorous phrases.

    Keywords: human-robot interaction, Humor, Low moral behaviors, crowdsourcing, machine learning

    Received: 26 Mar 2024; Accepted: 17 Oct 2024.

    Copyright: © 2024 Ushijima, Satake and Kanda. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yuto Ushijima, Kyoto University, Kyoto, Japan

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.