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CONVEX: Similarity-Based Algorithms for Forecasting Group Behavior
July/August 2008 (vol. 23 no. 4)
pp. 51-57
Vanina Martinez, University of Maryland, College Park
Gerardo I. Simari, University of Maryland, College Park
Amy Sliva, University of Maryland, College Park
V.S. Subrahmanian, University of Maryland, College Park
A proposed framework for predicting a group's behavior associates two vectors with that group. The context vector tracks aspects of the environment in which the group functions; the action vector tracks the group's previous actions. Given a set of past behaviors consisting of a pair of these vectors and given a query context vector, the goal is to predict the associated action vector. To achieve this goal, two families of algorithms employ vector similarity. CONVEXk _NN algorithms use k-nearest neighbors in high-dimensional metric spaces; CONVEXMerge algorithms look at linear combinations of distances of the query vector from context vectors. Compared to past prediction algorithms, these algorithms are extremely fast. Moreover, experiments on real-world data sets show that the algorithms are highly accurate, predicting actions with well over 95-percent accuracy.

1. S. Khuller et al., "Finding Most Probable Worlds of Probabilistic Logic Programs," Proc. 2007 Int'l Conf. Scalable Uncertainty Management, LNCS 4772, Springer, 2007, pp. 45–59.
2. S. Khuller et al., "Computing Most Probable Worlds of Action Probabilistic Logic Programs: Scalable Estimation for 1030,000Worlds," Annals Mathematics and Artificial Intelligence, vol. 51, nos. 2–4, 2007, pp. 295–331.
3. J. Wilkenfeld et al., The Use of Violence by Ethnopolitical Organizations in the Middle East, tech. report, Nat'l Consortium for the Study of Terrorism and Responses to Terrorism, 2007.
4. A. Sliva et al., "The SOMA Terror Organization Portal (STOP): Social Network and Analytic Tools for the Real-Time Analysis of Terror Groups," Social Computing, Behavioral Modeling, and Prediction, Springer, 2008, pp. 9–18.
5. A.W. Mannes et al., "Stochastic Opponent Modeling Agents: A Case Study with Hezbollah," Social Computing, Behavioral Modeling, and Prediction, Springer, 2008, pp. 37–45.
1. P. Schrodt, "Forecasting Conflict in the Balkans Using Hidden Markov Models," Programming for Peace: Computer-Aided Methods for International Conflict Resolution and Prevention, Springer, 2000, pp. 161–184, http://web.ku.edu/keds/papers.dirKEDS.APSA00.pdf .
2. J. Bond et al., "Forecasting Turmoil in Indonesia: An Application of Hidden Markov Models," Proc. Int'l Studies Assoc. Convention, Int'l Studies Assoc., 2004, pp. 17–21.
3. S. Khuller et al., "Finding Most Probable Worlds of Probabilistic Logic Programs," Proc. 2007 Int'l Conf. Scalable Uncertainty Management, LNCS 4772, Springer, 2007, pp. 45–59.
4. S. Khuller et al., "Computing Most Probable Worlds of Action Probabilistic Logic Programs: Scalable Estimation for 1,030,000 Worlds," Annals Mathematics and Artificial Intelligence, vol. 51, nos. 2–4, 2007, pp. 295–331.
5. V.S. Subrahmanian et al., "CARA: A Cultural-Reasoning Architecture," IEEE Intelligent Systems, vol. 22, no. 2, 2007, pp. 12–16.
6. V.S. Subrahmanian, "Cultural Modeling in Real Time," Science, vol. 317, no. 5844, 2007, pp. 1509–1510.
7. A. Sliva et al., "The SOMA Terror Organization Portal (STOP): Social Network and Analytic Tools for the Real-Time Analysis of Terror Groups," Social Computing, Behavioral Modeling, and Prediction, Springer, 2008, pp. 9–18.

Index Terms:
behavioral modeling, case-based reasoning, predictive reasoning
Citation:
Vanina Martinez, Gerardo I. Simari, Amy Sliva, V.S. Subrahmanian, "CONVEX: Similarity-Based Algorithms for Forecasting Group Behavior," IEEE Intelligent Systems, vol. 23, no. 4, pp. 51-57, July-Aug. 2008, doi:10.1109/MIS.2008.62
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