Risk-Averse Strategies for Security Games with Execution and Observational Uncertainty
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.
Cite
Text
Yin et al. "Risk-Averse Strategies for Security Games with Execution and Observational Uncertainty." AAAI Conference on Artificial Intelligence, 2011. doi:10.1609/AAAI.V25I1.7862Markdown
[Yin et al. "Risk-Averse Strategies for Security Games with Execution and Observational Uncertainty." AAAI Conference on Artificial Intelligence, 2011.](https://mlanthology.org/aaai/2011/yin2011aaai-risk/) doi:10.1609/AAAI.V25I1.7862BibTeX
@inproceedings{yin2011aaai-risk,
title = {{Risk-Averse Strategies for Security Games with Execution and Observational Uncertainty}},
author = {Yin, Zhengyu and Jain, Manish and Tambe, Milind and Ordóñez, Fernando},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2011},
pages = {758-763},
doi = {10.1609/AAAI.V25I1.7862},
url = {https://mlanthology.org/aaai/2011/yin2011aaai-risk/}
}