@conference {1502034,
	title = {Robust Resource Allocation in Security Games and Ensemble Modeling of Adversary Behavior },
	booktitle = {ACM Symposium on Applied Computing (ACM SAC 2015) Track on Intelligent Robotics and Multi-Agent Systems (IRMAS)},
	year = {2015},
	abstract = {Game theoretic algorithms have been used to optimize the
allocation of security resources to improve the protection of critical
infrastructure against threats when limits on security resources
prevent full protection of all targets. Past approaches have assumed
adversaries will always behave to maximize their expected utility,
failing to address real-world adversaries who are not perfectly
rational. Instead, adversaries may be boundedly rational, i.e., they
generally act to increase their expected value but do not
consistently maximize it. A successful approach to addressing
bounded adversary rationality has been a robust approach that does
not explicitly model adversary behavior. However, these robust
algorithms implicitly rely on an efficiently computable weak model
of adversary behavior, which does not necessarily match adversary
behavior trends. We therefore propose a new robust algorithm that
provides a more refined model of adversary behavior that retains
the advantage of efficient computation. We also develop an
ensemble method used to tune the algorithm{\textquoteright}s parameters, and
compare this method{\textquoteright}s accuracy in predicting adversary behavior
to previous work. We test these contributions in security games
against human subjects to show the advantages of our approach. },
	author = {Tambe, Arjun and Nguyen, Thanh}
}
