Three Strategies to Success: Learning Adversary Models in Security Games / 308
Nika Haghtalab, Fei Fang, Thanh H. Nguyen, Arunesh Sinha, Ariel D. Procaccia, Milind Tambe

State-of-the-art applications of Stackelberg security games — including wildlife protection — offer a wealth of data, which can be used to learn the behavior of the adversary. But existing approaches either make strong assumptions about the structure of the data, or gather new data through online algorithms that are likely to play severely suboptimal strategies. We develop a new approach to learning the parameters of the behavioral model of a bounded rational attacker (thereby pinpointing a near optimal strategy), by observing how the attacker responds to only three defender strategies. We also validate our approach using experiments on real and synthetic data.

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