Building real stackelberg security games for border patrols

Abstract

We present a decision support system to help plan preventive border patrols. The system represents the interaction between defenders and intruders as a Stackelberg security game (SSG) where the defender pools local resources to conduct joint preventive border patrols. We introduce a new SSG that constructs defender strategies that pair adjacent precincts to pool resources that are used to patrol a location within one of the two precincts. We introduce an efficient formulation of this problem and an efficient sampling method to construct an implementable defender strategy.

The system automatically constructs the Stackelberg game from geographically located past crime data, topology and cross border information. We use clustering of past crime data and logit probability distribution to assign risk to patrol areas. Our results on a simplified real-world inspired border patrol instance show the computational efficiency of the model proposed, its robustness with respect to parameters used in automatically constructing the instance, and the quality of the sampled solution obtained.