Title
Cloudy with a Chance of Poaching: Adversary Behavior Modeling and Forecasting with Real-World Poaching Data
Author(s)
Kar, Debarun ;Ford, Benjamin ;Gholami, Shahrzad;Fang, Fei ;Plumptre, Andrew;Tambe, Milind ;Driciru, Margaret ;Wanyama, Fred ;Rwetsiba, Aggrey ;Nsubaga, Mustapha ;Mabonga, Joshua
Published
2017
Abstract
Wildlife conservation organizations task rangers to deter and capture
wildlife poachers. Since rangers are responsible for patrolling
vast areas, adversary behavior modeling can help more effectively
direct future patrols. In this innovative application track paper,
we present an adversary behavior modeling system, INTERCEPT
(INTERpretable Classification Ensemble to Protect Threatened
species), and provide the most extensive evaluation in the AI
literature of one of the largest poaching datasets from Queen Elizabeth
National Park (QENP) in Uganda, comparing INTERCEPT
with its competitors; we also present results from a month-long
test of INTERCEPT in the field. We present three major contributions.
First, we present a paradigm shift in modeling and forecasting
wildlife poacher behavior. Some of the latest work in the
AI literature (and in Conservation) has relied on models similar
to the Quantal Response model from Behavioral Game Theory for
poacher behavior prediction. In contrast, INTERCEPT presents a
behavior model based on an ensemble of decision trees (i) that more
effectively predicts poacher attacks and (ii) that is more effectively
interpretable and verifiable. We augment this model to account for
spatial correlations and construct an ensemble of the best models,
significantly improving performance. Second, we conduct an extensive
evaluation on the QENP dataset, comparing 41 models in
prediction performance over two years. Third, we present the results
of deploying INTERCEPT for a one-month field test in QENP
- a first for adversary behavior modeling applications in this domain.
This field test has led to finding a poached elephant and
more than a dozen snares (including a roll of elephant snares) before
they were deployed, potentially saving the lives of multiple
animals - including elephants.
Keywords
Innovative Applications;Human Behavior Modeling;Wildlife Conservation;Deployed Applications
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