@conference {1508536,
	title = {Efficient Algorithms to solve Bayesian Stackelberg Games for Security Applications },
	booktitle = {National Conference on Artificial Intelligence (AAAI)},
	year = {2008},
	abstract = {In a class of games known as Stackelberg games, one agent
(the leader) must commit to a strategy that can be observed by
the other agent (the adversary/follower) before the adversary
chooses its own strategy. We consider Bayesian Stackelberg
games, in which the leader is uncertain about the type of the
adversary it may face. Such games are important in security domains, where, for example, a security agent (leader)
must commit to a strategy of patrolling certain areas, and an
adversary (follower) can observe this strategy over time before choosing where to attack. We present here two different MIP-formulations, ASAP (providing approximate policies with controlled randomization) and DOBSS (providing
optimal policies) for Bayesian Stackelberg games. DOBSS is
currently the fastest optimal procedure for Bayesian Stackelberg games and is in use by police at the Los Angeles International Airport(LAX) to schedule their activities.},
	author = {Praveen Paruchuri and Jonathan P. Pearce and Janusz Marecki and Tambe, Milind and Ordonez, Fernando and Kraus, Sarit}
}
