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.

In this paper, we describe the model, theory developed, and deployment of PROTECT, a game-theoretic system that the United States Coast Guard (USCG) uses to schedule patrols in the Port of Boston. The USCG evaluated PROTECT's deployment in the Port of Boston as a success and is currently evaluating the system in the Port of New York, with the potential for nationwide deployment. PROTECT is premised on an attacker-defender Stackelberg game model; however, its development and implementation required both theoretical contributions and detailed evaluations. We describe the work required in the deployment, which we group into five key innovations. First, we propose a compact representation of the defender's strategy space by exploiting equivalence and dominance, to make PROTECT efficient enough to solve real-world sized problems. Second, this system does not assume that adversaries are perfectly rational, a typical assumption in previous game-theoretic models for security. Instead, PROTECT relies on a quantal response (QR) model of the adversary's behavior. We believe this is the first real-world deployment of a QR model. Third, we develop specialized solution algorithms that can solve this problem for real-world instances and give theoretical guarantees. Fourth, our experimental results illustrate that PROTECT's QR model handles real-world uncertainties more robustly than a perfect-rationality model. Finally, we present (1) a comparison of human-generated and PROTECT security schedules, and (2) results of an evaluation of PROTECT from an analysis by human mock attackers.

We provide an overview of two recent applications of security games. We describe new features and challenges introduced in the new applications.

Given the real-world deployments of attacker-defender Stackelberg security games, robustness to deviations from expected attacker behaviors has now emerged as a critically important issue. This paper provides four key contributions in this context. First, it identifies a fundamentally problematic aspect of current algorithms for security games. It shows that there are many situations where these algorithms face multiple equilibria, and they arbitrarily select one that may hand the defender a significant disadvantage, particularly if the attacker deviates from its equilibrium strategies due to unknown constraints. Second, for important subclasses of security games, it identifies situations where we will face such multiple equilibria. Third, to address these problematic situations, it presents two equilibrium refinement algorithms that can optimize the defender's utility if the attacker deviates from equilibrium strategies. Finally, it experimentally illustrates that the refinement approach achieved significant robustness in consideration of attackers' deviation due to unknown constraints.