@conference {1506243,
	title = {Risk-Averse Strategies for Security Games with Execution and Observational Uncertainty },
	booktitle = {Conference on Artificial Intelligence (AAAI)},
	year = {2011},
	abstract = {Attacker-defender Stackelberg games have become a popular
game-theoretic approach for security with deployments for
LAX Police, the FAMS and the TSA. Unfortunately, most of
the existing solution approaches do not model two key uncertainties of the real-world: there may be noise in the defender{\textquoteright}s
execution of the suggested mixed strategy and/or the observations made by an attacker can be noisy. In this paper, we provide a framework to model these uncertainties, and demonstrate that previous strategies perform poorly in such uncertain settings. We also provide RECON, a novel algorithm that
computes strategies for the defender that are robust to such
uncertainties, and provide heuristics that further improve RECON{\textquoteright}s efficiency},
	author = {Yin, Zhengyu and Jain, Manish and Tambe, Milind and Ordonez, Fernando}
}
