A bayesian network approach to investigating user-robot personality matching

Authors:
Jungsik Hwang;Kun Chang Lee;Jaeyeol Jeong
Affiliations:
Department of Interaction Science, Sungkyunkwan University, South Korea;Business School, WCU Department of Interaction Science, Sungkyunkwan University, Seoul, South Korea;Department of Interaction Science, Sungkyunkwan University, South Korea
Venue:
AMT'12 Proceedings of the 8th international conference on Active Media Technology
Year:
2012

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Abstract

Personality analysis has been an important topic in both psychology and human-robot interaction (HRI). The main theme of this paper is to explore the relationship between individuals' personality traits and their tactile interaction patterns with a robot. A sociable robot, Pleo, was used in the experiment. The tactile interaction patterns of the participants with the robot were video-recorded and analyzed. Bayesian network (BN) classifiers such as NBN (naïve BN), TAN (tree-augmented BN), and GBN (general BN) were used to examine the causal relationship between personality traits and touch patterns. The analysis showed that individuals' personality traits could be inferred based on their tactile interaction patterns with a robot. What-if and goal-seeking analysis using GBN confirmed this result. The findings of this paper are promising and its implications are discussed.