Computing the optimal strategy to commit to
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Leader-follower strategies for robotic patrolling in environments with arbitrary topologies
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Adversarial uncertainty in multi-robot patrol
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Solving Stackelberg games with uncertain observability
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Security and Game Theory: Algorithms, Deployed Systems, Lessons Learned
Security and Game Theory: Algorithms, Deployed Systems, Lessons Learned
Using multi-agent simulation to improve the security of maritime transit
MABS'11 Proceedings of the 12th international conference on Multi-Agent-Based Simulation
Improving resource allocation strategy against human adversaries in security games
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Computing optimal strategy against quantal response in security games
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Computing optimal strategy against quantal response in security games
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Game theory for security: an important challenge for multiagent systems
EUMAS'11 Proceedings of the 9th European conference on Multi-Agent Systems
Security scheduling for real-world networks
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Security games with surveillance cost and optimal timing of attack execution
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Security games with interval uncertainty
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Modeling human adversary decision making in security games: an initial report
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Computing Stackelberg strategies in stochastic games
ACM SIGecom Exchanges
Defender (mis)coordination in security games
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Game-theoretic question selection for tests
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Efficiently solving joint activity based security games
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Scaling-up security games with boundedly rational adversaries: a cutting-plane approach
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Protecting moving targets with multiple mobile resources
Journal of Artificial Intelligence Research
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While three deployed applications of game theory for security have recently been reported at AAMAS [12], we as a community 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.