Decision-Focused Learning of Adversary Behavior in Security Games
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Stackelberg security games are a critical tool for maximizing the utility of limited defense resources to protect important targets from an intelligent adversary. Motivated by green security, where the defender may only observe an adversary's response to defense on a limited set of targets, we study the problem of defending against the same adversary on a larger set of targets from the same distribution. We give a theoretical justification for why standard two-stage learning approaches, where a model of the adversary is trained for predictive accuracy and then optimized against, may fail to maximize the defender's expected utility in this setting. We develop a decision-focused learning approach, where the adversary behavior model is optimized for decision quality, and show empirically that it achieves higher defender expected utility than the two-stage approach when there is limited training data and a large number of target features.
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