Title
CAPTURE: A New Predictive Anti-Poaching Tool for Wildlife Protection
Author(s)
Nguyen, Thanh H. ;Sinha, Arunesh ;Gholami, Shahrzad ;Plumptre, Andrew ;Joppa, Lucas ;Tambe, Milind;Driciru, Margaret ;Wanyama, Fred ;Rwetsiba, Aggrey ;Critchlow, Rob ;Beale, Colin
Published
2016
Abstract
Wildlife poaching presents a serious extinction threat to many animal
species. Agencies (“defenders”) focused on protecting such
animals need tools that help analyze, model and predict poacher
activities, so they can more effectively combat such poaching; such
tools could also assist in planning effective defender patrols, building
on the previous security games research.
To that end, we have built a new predictive anti-poaching tool,
CAPTURE (Comprehensive Anti-Poaching tool with Temporal
and observation Uncertainty REasoning). CAPTURE provides
four main contributions. First, CAPTURE’s modeling of poachers
provides significant advances over previous models from behavioral
game theory and conservation biology. This accounts for:
(i) the defender’s imperfect detection of poaching signs; (ii) complex
temporal dependencies in the poacher’s behaviors; (iii) lack
of knowledge of numbers of poachers. Second, we provide two
new heuristics: parameter separation and target abstraction to reduce
the computational complexity in learning the poacher models.
Third, we present a new game-theoretic algorithm for computing
the defender’s optimal patrolling given the complex poacher
model. Finally, we present detailed models and analysis of realworld
poaching data collected over 12 years in Queen Elizabeth
National Park in Uganda to evaluate our new model’s prediction
accuracy. This paper thus presents the largest dataset of real-world
defender-adversary interactions analyzed in the security games literature.
CAPTURE will be tested in Uganda in early 2016
Keywords
Security Game;Wildlife Protection;Temporal Behavorial Model
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PUB15846