https://scholars.lib.ntu.edu.tw/handle/123456789/597595
標題: | Using Machine Theory of Mind to Learn Agent Social Network Structures from Observed Interactive Behaviors with Targets | 作者: | Chuang, Y.-S. Hung, H.-Y. Gamborino, E. JOSHUA GOH TSUNG-REN HUANG YU-LING CHANG SU-LING YEH LI-CHEN FU |
公開日期: | 2020 | 出版社: | Institute of Electrical and Electronics Engineers Inc. | 起(迄)頁: | 1013-1019 | 來源出版物: | 29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020 | 摘要: | Human social interactions are laden with behavioral preferences that stem from hidden social network representations. In this study, we applied an artificial neural network with machine theory of mind (ToMnet+) to learn and predict social preferences based on implicit information from the way agents and social targets interact behaviorally. Our findings have implications for machine applications that seek to infer hidden information structures solely from third-person observation of behaviors. We consider that social machines with such an ability would have an enhanced potential for more naturalistic human-machine interactions. © 2020 IEEE. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095750526&doi=10.1109%2fRO-MAN47096.2020.9223453&partnerID=40&md5=62f7ceda98cb3e7b689560691fbe0bac https://scholars.lib.ntu.edu.tw/handle/123456789/597595 |
ISBN: | 978-1728160757 | DOI: | 10.1109/RO-MAN47096.2020.9223453 |
顯示於: | 腦與心智科學研究所 |
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