Robust Bayesian Methods for Stackelberg Security Games

Recent work has applied game-theoretic models to real-world security problems at the Los Angeles International Airport (LAX)and Federal Air Marshals Service (FAMS). The analysis of thesedomains is based on input from domain experts intended to capture the best available intelligence information about potential terrorist activities and possible security countermeasures. Nevertheless, these models are subject to significant uncertainty - especiallyin security domains where intelligence about adversary capabilities and preferences is very difficult to gather. This uncertaintypresents significant challenges for applying game-theoretic analysis in these domains. Our experimental results show that standard solution methods based on perfect information assumptionsare very sensitive to payoff uncertainty, resulting in low payoffs forthe defender. We describe a model of Bayesian Stackelberg gamesthat allows for general distributional uncertainty over the attacker'spayoffs. We conduct an experimental analysis of two algorithms forapproximating equilibria of these games, and show that the resulting solutions give much better results than the standard approachwhen there is payoff uncertainty.

Authors

• Christopher Kiekintveld
• Janusz Marecki Milind Tambe
• Milind Tambe Harvard University Google Research

Keywords

• Game theory
• security
• robustness
• Bayesian
• Stackelberg
• optimization
• replicator dynamics