Abstract

Security is a critical concern around the world, whether it’s the challenge of protecting ports, airports and other critical national infrastructure, or protecting wildlife and forests, or suppressing crime in urban areas. In many of these cases, 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 allocate limited security resources, while avoiding schedule predictability. 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: at the Los Angeles International Airport (LAX), for the Federal Air Marshals (FAMS), for the US coast guard in Boston, New York and Los Angeles (and potentially other ports); and applications are under evaluation for the TSA and for the Los Angeles Sheriff’s department. These applications are leading to real-world use-inspired research in computational game theory in scaling up to large-scale problems, handling significant adversarial uncertainty, dealing with bounded rationality of human adversaries, and other interdisciplinary challenges. This talk will outline key principles of our algorithms, major research results and open problems.

• This is joint work with a large number of collaborators, including former and current students, postdocs, and several other colleagues, listed at: http://teamcore.usc.edu/security