@conference {1501624,
	title = {Playing Security Games with No Prior Knowledge },
	booktitle = {International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
	year = {2016},
	abstract = {This paper investigates repeated security games with unknown (to
the defender) game payoffs and attacker behaviors. As existing
work assumes prior knowledge about either the game payoffs or the
attacker{\textquoteright}s behaviors, they are not suitable for tackling our problem.
Given this, we propose the first efficient defender strategy, based on
an adversarial online learning framework, that can provably achieve
good performance guarantees without any prior knowledge. In particular, we prove that our algorithm can achieve low performance
loss against the best fixed strategy on hindsight (i.e., having full
knowledge of the attacker{\textquoteright}s moves). In addition, we prove that our
algorithm can achieve an efficient competitive ratio against the optimal adaptive defender strategy. We also show that for zero-sum
security games, our algorithm achieves efficient results in approximating a number of solution concepts, such as algorithmic equilibria and the minimax value. Finally, our extensive numerical results
demonstrate that, without having any prior information, our algorithm still achieves good performance, compared to state-of-the-art
algorithms from the literature on security games, such as SUQR
[19], which require significant amount of prior knowledge.},
	author = {Haifeng Xu and Long Tran Thanh and Nick Jennings}
}
