@conference {1499547,
	title = {Decision-Focused Learning of Adversary Behavior in Security Games},
	booktitle = {GAIW: Games, Agents and Incentives Workshop at International Conference on Autonomous Agents and Multiagent Systems (AAMAS-19)},
	year = {2019},
	abstract = {Stackelberg security games are a critical tool for maximizing theutility of limited defense resources to protect important targetsfrom an intelligent adversary. Motivated by green security, wherethe defender may only observe an adversary{\textquoteright}s response to defenseon a limited set of targets, we study the problem of defendingagainst the same adversary on a larger set of targets from the samedistribution. We give a theoretical justification for why standardtwo-stage learning approaches, where a model of the adversary istrained for predictive accuracy and then optimized against, mayfail to maximize the defender{\textquoteright}s expected utility in this setting. Wedevelop a decision-focused learning approach, where the adversary behavior model is optimized for decision quality, and showempirically that it achieves higher defender expected utility thanthe two-stage approach when there is limited training data and alarge number of target features.},
	author = {Perrault, Andrew and Bryan Wilder and Ewing, Eric and Mate, Aditya and Dilkina, Bistra and Tambe, Milind}
}
