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Issue No. 04 - July/August (2008 vol. 23)
ISSN: 1541-1672
pp: 51-57
Vanina Martinez , University of Maryland, College Park
Gerardo I. Simari , University of Maryland, College Park
V.S. Subrahmanian , University of Maryland, College Park
Amy Sliva , 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. CONVEX<sup><em>k</em> _NN</sup> algorithms use <em>k</em>-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.
behavioral modeling, case-based reasoning, predictive reasoning
Vanina Martinez, Gerardo I. Simari, V.S. Subrahmanian, Amy Sliva, "CONVEX: Similarity-Based Algorithms for Forecasting Group Behavior", IEEE Intelligent Systems, vol. 23, no. , pp. 51-57, July/August 2008, doi:10.1109/MIS.2008.62
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