@article {1500628,
	title = {Predicting Poaching for Wildlife Protection},
	journal = {IBM Journal of Research and Development (To appear)},
	year = {2017},
	abstract = {Wildlife species such as tigers and elephants are under the threat of poaching. To combat
poaching, conservation agencies ({\textquotedblleft}defenders{\textquotedblright}) need to (1) anticipate where the poachers are
likely to poach and (2) plan effective patrols. We propose an anti-poaching tool CAPTURE
(Comprehensive Anti-Poaching tool with Temporal and observation Uncertainty REasoning),
which helps the defenders achieve both goals. CAPTURE builds a novel hierarchical model for
poacher-patroller interaction. It considers the patroller{\textquoteright}s imperfect detection of signs of
poaching, the complex temporal dependencies in the poacher{\textquoteright}s behaviors and the defender{\textquoteright}s lack
of knowledge of the number of poachers. Further, CAPTURE uses a new game-theoretic
algorithm to compute the optimal patrolling strategies and plan effective patrols. This paper
investigates the computational challenges that CAPTURE faces. First, we present a detailed
analysis of parameter separation and target abstraction, two novel approaches used by
CAPTURE to efficiently learn the parameters in the hierarchical model. Second, we propose two
heuristics {\textendash} piece-wise linear approximation and greedy planning {\textendash} to speed up the computation
of the optimal patrolling strategies. We discuss in this paper the lessons learned from using
CAPTURE to analyze real-world poaching data collected over 12 years in Queen Elizabeth
National Park in Uganda.},
	author = {F. Fang and T. H. Nguyen and A. Sinha and S. Gholami and A. Plumptre and L. Joppa and M. Tambe and M. Driciru and F. Wanyama and A. Rwetsiba and R. Critchlow and C. M. Beale}
}
