Protect - A deployed game-theoretic system for strategic security allocation for the United States coast guard

Abstract

While three deployed applications of game theory for security have recently been reported, we as a community of agents and AI researchers remain in the early stages of these deployments; there is a continuing need to understand the core principles for innovative security applications of game theory. Toward that end, this article presents PROTECT, a game-theoretic system deployed by the United States Coast Guard (USCG) in the Port of Boston for scheduling its patrols. USCG has termed the deployment of PROTECT in Boston a success; PROTECT is currently being tested in the Port of New York, with the potential for nationwide deployment. PROTECT is premised on an attackerdefender Stackelberg game model and offers five key innovations. First, this system is a departure from the assumption of perfect adversary rationality noted in previous work, relying instead on a quantal response (QR) model of the adversary's behavior - to the best of our knowledge, this is the first real-world deployment of the QR model. Second, to improve PROTECT's efficiency, we generate a compact representation of the defender's strategy space, exploiting equivalence and dominance. Third, we show how to practically model a real maritime patrolling problem as a Stackelberg game. Fourth, our experimental results illustrate that PROTECT's QR model more robustly handles real-world uncertainties than a perfect rationality model. Finally, in evaluating PROTECT, this article for the first time provides real-world data: comparison of human-generated versus PROTECT security schedules, and results from an Adversarial Perspective Team's (human mock attackers) analysis. Copyright © 2012, Association for the Advancement of Artificial Intelligence.