TY - JOUR AU - Yin, Zhengyu AU - Jain, Manish AU - Tambe, Milind AU - Ordóñez, Fernando PY - 2011/08/04 Y2 - 2024/03/28 TI - Risk-Averse Strategies for Security Games with Execution and Observational Uncertainty JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 25 IS - 1 SE - AAAI Technical Track: Multiagent Systems DO - 10.1609/aaai.v25i1.7862 UR - https://ojs.aaai.org/index.php/AAAI/article/view/7862 SP - 758-763 AB - <p> 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'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's efficiency. </p> ER -