Game Theory for Security: Key Algorithmic Principles, Deployed Systems, Lessons Learned


Milind Tambe, Manish Jain, James Adam Pita, and Albert Xin Jiang. 2012. “Game Theory for Security: Key Algorithmic Principles, Deployed Systems, Lessons Learned.” In 50th Annual Allerton Conference on Communication, Control, and Computing .


Security is a critical concern around the world. In many security domains, limited security resources prevent full security coverage at all times; instead, these limited resources must be scheduled, avoiding schedule predictability, while simultaneously taking into account different target priorities, the responses of the adversaries to the security posture and potential uncertainty over adversary types. Computational game theory can help design such unpredictable security schedules. Indeed, casting the problem as a Bayesian Stackelberg game, we have developed new algorithms that are now deployed over multiple years in multiple applications for security scheduling. These applications are leading to real-world use-inspired research in the emerging research area of “security games”; specifically, the research challenges posed by these applications include scaling up security games to largescale problems, handling significant adversarial uncertainty, dealing with bounded rationality of human adversaries, and other interdisciplinary challenges.
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