@conference {1508857,
	title = {An Efficient Heuristic for Security Against Multiple Adversaries in Stackelberg Games },
	booktitle = {AAAI Spring Symposium on Game and Decision-Theoretic Agents},
	year = {2007},
	abstract = {In adversarial multiagent domains, security, commonly defined as the ability to deal with intentional threats from other
agents, is a critical issue. This paper focuses on domains
where these threats come from unknown adversaries. These
domains can be modeled as Bayesian games; much work has
been done on finding equilibria for such games. However,
it is often the case in multiagent security domains that one
agent can commit to a mixed strategy which its adversaries
observe before choosing their own strategies. In this case, the
agent can maximize reward by finding an optimal strategy,
without requiring equilibrium. Previous work has shown this
problem of optimal strategy selection to be NP-hard. Therefore, we present a heuristic called ASAP, with three key advantages to address the problem. First, ASAP searches for
the highest-reward strategy, rather than a Bayes-Nash equilibrium, allowing it to find feasible strategies that exploit the
natural first-mover advantage of the game. Second, it provides strategies which are simple to understand, represent,
and implement. Third, it operates directly on the compact,
Bayesian game representation, without requiring conversion
to normal form. We provide an efficient Mixed Integer Linear
Program (MILP) implementation for ASAP, along with experimental results illustrating significant speedups and higher
rewards over other approaches.},
	author = {Praveen Paruchuri and Jonathan P. Pearce and Tambe, Milind and Kraus, Sarit and Ordonez, Fernando}
}
