@conference {1508855,
	title = {An Efficient Heuristic Approach for Security Against Multiple Adversaries },
	booktitle = {International Conference on Autonomous Agents and Multiagent Systems, AAMAS-2007	},
	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 Ordonez, Fernando and Kraus, Sarit}
}
