Adversary models account for imperfect crime data: Forecasting and planning against real-world poachers
Loading...
Date
2018
Journal Title
Journal ISSN
Volume Title
Publisher
International Conference on Autonomous Agents and Multiagent Systems
Abstract
Poachers are engaged in extinction level wholesale slaughter, so
it is critical to harness historical data for predicting poachers’ behavior.
However, in these domains, data collected about adversarial
actions are remarkably imperfect, where reported negative
instances of crime may be mislabeled or uncertain. Unfortunately,
past attempts to develop predictive and prescriptive models to
address this problem suffer from shortcomings from a modeling
perspective as well as in the implementability of their techniques.
Most notably these models i) neglect the uncertainty in crime data,
leading to inaccurate and biased predictions of adversary behavior,
ii) use coarse-grained crime analysis and iii) do not provide
a convincing evaluation as they only look at a single protected
area. Additionally, they iv) proposed time-consuming techniques
which cannot be directly integrated into low resource outposts.
In this innovative application paper, we (I) introduce iWare-E a
novel imperfect-observation aWare Ensemble (iWare-E) technique,
which is designed to handle the uncertainty in crime information
efficiently. This approach leads to superior accuracy and efficiency
for adversary behavior prediction compared to the previous stateof-
the-art. We also demonstrate the country-wide efficiency of the
models and are the first to (II) evaluate our adversary behavioral
model across different protected areas in Uganda, i.e., Murchison
Fall and Queen Elizabeth National Park, (totaling about 7500 km2)
as well as (III) on fine-grained temporal resolutions. Lastly, (IV) we
provide a scalable planning algorithm to design fine-grained patrol
routes for the rangers, which achieves up to 150% improvement in
number of predicted attacks detected.
Description
Keywords
Predictive models, Wildlife poaching, Ensemble techniques, Field test evaluation, Wildlife protection
Citation
Gholami, S., Mc Carthy, S., Dilkina, B., Plumptre, A., Tambe, M., Driciru, M., ... & Enyel, T. O. E. (2018). Adversary models account for imperfect crime data: Forecasting and planning against real-world poachers (Corrected Version). In 17th International Conference on Autonomous Agents and Multiagent Systems.