Risk-Averse Strategies for Security Games with Execution and Observational Uncertainty


  • Zhengyu Yin University of Southern California
  • Manish Jain University of Southern California
  • Milind Tambe University of Southern California
  • Fernando Ordóñez University of Southern California


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.




How to Cite

Yin, Z., Jain, M., Tambe, M., & Ordóñez, F. (2011). Risk-Averse Strategies for Security Games with Execution and Observational Uncertainty. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 758-763. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/7862



AAAI Technical Track: Multiagent Systems