@conference {1500964,
	title = {Optimal Patrol Planning for Green Security Games with Black-Box Attackers },
	booktitle = {Conference on Decision and Game Theory for Security (GameSec) 2017},
	year = {2017},
	abstract = {Motivated by the problem of protecting endangered animals,
there has been a surge of interests in optimizing patrol planning for conservation area protection. Previous efforts in these domains have mostly
focused on optimizing patrol routes against a specific boundedly rational
poacher behavior model that describes poachers{\textquoteright} choices of areas to attack. However, these planning algorithms do not apply to other poaching
prediction models, particularly, those complex machine learning models
which are recently shown to provide better prediction than traditional
bounded-rationality-based models. Moreover, previous patrol planning
algorithms do not handle the important concern whereby poachers infer the patrol routes by partially monitoring the rangers{\textquoteright} movements. In
this paper, we propose OPERA, a general patrol planning framework
that: (1) generates optimal implementable patrolling routes against a
black-box attacker which can represent a wide range of poaching prediction models; (2) incorporates entropy maximization to ensure that the
generated routes are more unpredictable and robust to poachers{\textquoteright} partial monitoring. Our experiments on a real-world dataset from Uganda{\textquoteright}s
Queen Elizabeth Protected Area (QEPA) show that OPERA results in
better defender utility, more efficient coverage of the area and more unpredictability than benchmark algorithms and the past routes used by
rangers at QEPA.},
	author = {Haifeng Xu and Ford, Benjamin and Fang, Fei and Dilkina, Bistra and Plumptre, Andrew and Tambe, Milind and Driciru, Margaret and Wanyama, Fred and Rwetsiba, Aggrey and Nsubaga, Mustapha and Mabonga, Joshua}
}
