@article{An_Shieh_Tambe_Yang_Baldwin_DiRenzo_Maule_Meyer_2012, title={PROTECT -- A Deployed Game Theoretic System for Strategic Security Allocation for the United States Coast Guard}, volume={33}, url={https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/view/2401}, DOI={10.1609/aimag.v33i4.2401}, abstractNote={<p>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. Towards that end, this paper presents PROTECT, a game-theoretic system deployed by the United States Coast Guard (USCG) in the port of Boston for scheduling their patrols. USCG has termed the deployment of PROTECT in Boston a success, and efforts are underway to test it in the port of New York, with the potential for nationwide deployment.</p><p><br />PROTECT is premised on an attacker-defender 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 paper for the first time provides real-world data: (i) comparison of human-generated vs PROTECT security schedules, and (ii) results from an Adversarial Perspective Team’s (human mock attackers) analysis.</p>}, number={4}, journal={AI Magazine}, author={An, Bo and Shieh, Eric and Tambe, Milind and Yang, Rong and Baldwin, Craig and DiRenzo, Joseph and Maule, Ben and Meyer, Garrett}, year={2012}, month={Dec.}, pages={96} }