PROTECT -- A Deployed Game Theoretic System for Strategic Security Allocation for the United States Coast Guard
An, Bo (University of Southern California) | Shieh, Eric (University of Southern California) | Tambe, Milind (University of Southern California) | Yang, Rong (University of Southern California) | Baldwin, Craig (United States Coast Guard) | DiRenzo, Joseph (United States Coast Guard) | Maule, Ben (United States Coast Guard) | Meyer, Garrett (United States Coast Guard)
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.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.
- Country:
- North America > United States
- California (0.29)
- New York (0.25)
- Virginia (0.28)
- North America > United States
- Genre:
- Personal (0.68)
- Research Report > New Finding (0.46)
- Technology: