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Forecasting complex group behavior via multiple plan recognition

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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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References

  1. Wang F Y, Carley K M, Zeng D, Mao W. Social computing: from social informatics to social intelligence. IEEE Intelligent Systems, 2007, 22(2): 79–83

    Article  Google Scholar 

  2. Zeng D, Wang F Y, Carley K M. Social computing. IEEE Intelligent Systems, 2007, 22(5): 20–22

    Article  MATH  Google Scholar 

  3. Martinez V, Simari G I, Sliva A, Subrahmanian V S. CONVEX: similarity-based algorithms for forecasting group behavior. IEEE Intelligent Systems, 2008, 23(4): 51–57

    Article  Google Scholar 

  4. Khuller S, Martinez V, Nau D, Simari G, Sliva A, Subrahmanian V S. Finding most probable worlds of probabilistic logic programs. In: Proceedings of 1st International Conference on Scalable Uncertainty Management. 2007, 45–59

  5. Li X, Mao W, Zeng D, Wang F Y. Automatic construction of domain theory for attack planning. In: Proceedings of the 2010 IEEE International Conference on Intelligence and Security Informatics. 2010, 65–70

  6. Geib C W, Goldman R P. A probabilistic plan recognition algorithm based on plan tree grammars. Artificial Intelligence, 2009, 173(11): 1101–1132

    Article  MathSciNet  Google Scholar 

  7. Hu D H, Yang Q. CIGAR: concurrent and interleaving goal and activity recognition. In: Proceedings of 23rd National Conference on Artificial Intelligence. 2008, 1363–1368

  8. Kutluhan E, James H, Dana S N. HTN planning: complexity and expressivity. In: Proceedings of 12th National Conference on Artificial Intelligence. 1994, 1123–1128

  9. Subrahmanian V S. Cultural modeling in real time. Science, 2007, 317(5844): 1509–1510

    Article  Google Scholar 

  10. Subrahmanian V S, Albanese M, Martinez M V, Nau D, Reforgiato D, Simari G I, Sliva A, Wilkenfeld J, Udrea O. CARA: a culturalreasoning architecture. IEEE Intelligent Systems, 2007, 22(2): 12–16

    Article  Google Scholar 

  11. Charniak E, Goldman R P. A Bayesian model of plan recognition. Artificial Intelligence, 1993, 64(1): 53–79

    Article  Google Scholar 

  12. Huber M J, Durfee E H, Wellman M P. The automated mapping of plans for plan recognition. In: Proceedings of 12th National Conference on Artificial Intelligence. 1994, 344–351

  13. Albrecht D W, Zukerman I, Nicholson A E. Bayesian models for keyhole plan recognition in an adventure game. User Modeling and User-Adapted Interaction, 1998, 8(1): 5–47

    Article  Google Scholar 

  14. Bui H H, Venkatesh S, West G. Policy recognition in the abstract hid den Markov model. Journal of Artificial Intelligence Research, 2002, 17(1): 451–499

    MathSciNet  MATH  Google Scholar 

  15. Pynadath D V, Wellman M P. Probabilistic state-dependent grammars for plan recognition. In: Proceedings of 16th Conference on Uncertainty in Artificial Intelligence. 2000, 507–514

  16. Goldman R, Geib C, Miller C. A new model of plan recognition. In: Proceedings of the 15th Annual Conference on Uncertainty in Artificial Intelligence. 1999, 245–254

  17. Charikar M, Chekuri C, Cheung T, Dai Z, Goel A, Guha S, Li M. Approximation algorithms for directed Steiner problems. In: Proceedings of 9th Annual ACM-SIAM Symposium on Discrete Algorithms. 1998, 192–200

  18. Zosin L, Khuller S. On directed Steiner trees. In: Proceedings of 13th Annual ACM-SIAM Symposium on Discrete Algorithms. 2002, 59–63

  19. Garey M R, Johnson D S. Computers and intractability: a guide to the theory of NP-completeness. New York: W. H. Freeman & Co., 1990

    Google Scholar 

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Authors and Affiliations

  1. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Xiaochen Li, Wenji Mao & Daniel Zeng

Authors
  1. Xiaochen Li
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  2. Wenji Mao
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  3. Daniel Zeng
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Corresponding author

Correspondence to Wenji Mao.

Additional information

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

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