Game Theoretic Behavior Features Change Prediction in Hostile Environments

Prediction of adversarial course of actions (COA) is critical to many applications including: crime prediction, Unmanned Aerial Vehicle (UAV) threat prediction, and terrorism attack prevention. Researchers have shown that integrating behavior features (or preferences/patterns/modes) into prediction systems, which utilize random process theory and likelihood estimation calculations, can improve prediction accuracy. However, these calculations currently assume behavior features that are static and will not change during a long time horizon, which make such models difficult to adapt to adversary behavior feature changes. This paper provides an approach for dynamically predicting changes of behavior features utilizing the tenets of game theory. An example scenario and extensive simulations illustrate the feature prediction capability of this model.