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

Authors

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

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'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.

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Published

2011-08-04

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

Issue

Section

AAAI Technical Track: Multiagent Systems