@conference {1502904,
	title = {Regret-based Optimization and Preference Elicitation for Stackelberg Security Games with Uncertainty },
	booktitle = {National Conference on Artificial Intelligence (AAAI)},
	year = {2014},
	abstract = {Stackelberg security games (SSGs) have been deployed in a
number of real-world domains. One key challenge in these
applications is the assessment of attacker payoffs which may
not be perfectly known. Previous work has studied SSGs
with uncertain payoffs modeled by interval uncertainty and
provided maximin-based robust solutions. In contrast, in this
work we propose the use of the less conservative minimax
regret decision criterion for such payoff-uncertain SSGs and
present the first algorithms for computing minimax regret for
SSGs. We also address the challenge of preference elicitation,
using minimax regret to develop the first elicitation strategies
for SSGs. Experimental results validate the effectiveness of
our approaches.},
	author = {Thanh H. Nguyen and Amulya Yadav and An, Bo and Tambe, Milind and Craig Boutilier}
}
