@conference {1504821,
	title = {A Robust Approach to Addressing Human Adversaries in Security Games },
	booktitle = {European Conference on Artificial Intelligence (ECAI)},
	year = {2012},
	abstract = {Game-theoretic approaches have been proposed for addressing the complex problem of assigning limited security resources
to protect a critical set of targets. However, many of the standard
assumptions fail to address human adversaries who security forces
will likely face. To address this challenge, previous research has
attempted to integrate models of human decision-making into the
game-theoretic algorithms for security settings. The current leading
approach, based on experimental evaluation, is derived from a wellfounded solution concept known as quantal response and is known
as BRQR. One critical difficulty with opponent modeling in general
is that, in security domains, information about potential adversaries
is often sparse or noisy and furthermore, the games themselves are
highly complex and large in scale. Thus, we chose to examine a completely new approach to addressing human adversaries that avoids
the complex task of modeling human decision-making. We leverage
and modify robust optimization techniques to create a new type of
optimization where the defender{\textquoteright}s loss for a potential deviation by
the attacker is bounded by the distance of that deviation from the
expected-value-maximizing strategy. To demonstrate the advantages
of our approach, we introduce a systematic way to generate meaningful reward structures and compare our approach with BRQR in the
most comprehensive investigation to date involving 104 security settings where previous work has tested only up to 10 security settings.
Our experimental analysis reveals our approach performing as well
as or outperforming BRQR in over 90\% of the security settings tested
and we demonstrate significant runtime benefits. These results are in
favor of utilizing an approach based on robust optimization in these
complex domains to avoid the difficulties of opponent modeling.},
	author = {Pita, James and Richard John and Maheswaran, Rajiv and Tambe, Milind and Kraus, Sarit}
}
