PROTECT -- A Deployed Game Theoretic System for Strategic Security Allocation for the United States Coast Guard
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| Title | PROTECT -- A Deployed Game Theoretic System for Strategic Security Allocation for the United States Coast Guard |
| Authors | |
| 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. 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 adversarys behavior --- to the best of our knowledge, this is the first real-world deployment of the QR model. Second, to improve PROTECTs efficiency, we generate a compact representation of the defenders 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 PROTECTs 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 Teams (human mock attackers) analysis. |
| Publisher | Association for the Advancement of Artificial Intelligence |
| Date | 2012-12-31 |
| Source | AI Magazine Vol 33, No 4: Winter Issue |
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