Predicting humor effectiveness of robots for human line cutting

Abstract

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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