Forecasting complex group behavior via multiple plan recognition

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.

Keywords

group behavior forecasting / multiple plan recognition / graph search

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

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

[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

[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

[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

[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

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

[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

[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

[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

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