Security games with interval uncertainty

Security games provide a framework for allocating limited security resources in adversarial domains, and are currently used in deployed systems for LAX, the Federal Air Marshals, and the U.S. Coast Guard. One of the major challenges in security games is finding solutions that are robust to uncertainty about the game model. Bayesian game models have been used to model uncertainty, but algorithms for these games do not scale well enough for many applications. We take an alternative approach based on using intervals to model uncertainty in security games. We present a fast polynomial time algorithm for security games with interval uncertainty, which represents the first viable approach for computing robust solutions to very large security games. We also introduce a methodology for using intervals to approximate solutions to infinite Bayesian games with distributional uncertainty. Our experiments show that intervals can be an effective approach for these more general Bayesian games; our algorithm is faster and results in higher quality solutions than previous methods.

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