Regret-Based Optimization and Preference Elicitation for Stackelberg Security Games with Uncertainty

@inproceedings{Nguyen2014RegretBasedOA,
  title={Regret-Based Optimization and Preference Elicitation for Stackelberg Security Games with Uncertainty},
  author={Thanh Hong Nguyen and Amulya Yadav and Bo An and Milind Tambe and Craig Boutilier},
  booktitle={AAAI},
  year={2014}
}
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… CONTINUE READING
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