Abstract
Group behavior forecasting is an emergent research and application field in social computing. Most of the existing group behavior forecasting methods have heavily relied on structured data which is usually hard to obtain. To ease the heavy reliance on structured data, in this paper, we propose a computational approach based on the recognition of multiple plans/intentions underlying group behavior.We further conduct human experiment to empirically evaluate the effectiveness of our proposed approach.
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Xiaochen Li received his BSc in Automation from the University of Science & Technology Beijing in 2006 and his MSc in Computer Application Technology from the Institute of Automation, Chinese Academy of Sciences in 2009. He is now a PhD candidate at the Institute of Automation. His research interests mainly focus on social computing and group behavior forecasting.
Wenji Mao received her PhD in Computer Science from the University of Southern California in 2006. She is an associate professor at the Institute of Automation, Chinese Academy of Sciences. Prof. Mao is a member of ACM and AAAI, and a senior member of the China Computer Federation. Her research interests include artificial intelligence, multi-agent systems, and social modeling.
Daniel Zeng received his PhD in Industrial Administration from Carnegie Mellon University in 1998. He is a research professor at the Institute of Automation, Chicese Academy of Sciences. He is also affiliated with the University of Arizona. Prof. Zeng is a member of IEEE. His research interests include software agents and multiagent systems, intelligence and security informatics, social computing, and recommendation systems.
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Li, X., Mao, W. & Zeng, D. Forecasting complex group behavior via multiple plan recognition. Front. Comput. Sci. 6, 102–110 (2012). https://doi.org/10.1007/s11704-011-1186-4
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DOI: https://doi.org/10.1007/s11704-011-1186-4