Online learning methods for border patrol resource allocation

Abstract

We introduce a model for border security resource allocation with repeated interactions between attackers and defenders. The defender must learn the optimal resource allocation strategy based on historical apprehension data, balancing exploration and exploitation in the policy. We experiment with several solution methods for this online learning problem including UCB, sliding-window UCB, and EXP3. We test the learning methods against several different classes of attackers including attacker with randomly varying strategies and attackers who react adversarially to the defender’s strategy. We present experimental data to identify the optimal parameter settings for these algorithms and compare the algorithms against the different types of attackers.