Learning, Optimization, and Planning Under Uncertainty for Wildlife Conservation

paws_informs.pdf7.33 MB

Abstract:

In collaboration with conservation NGOs, our project helps plan effective ranger patrols to protect endangered animals from poaching. Algorithmically, the problem is to optimize limited resources to maximize the number of snares confiscated. Given limited and incomplete data, we leverage linear programming, multi-armed bandits, and game theory to handle uncertainty about poacher behavior. Our approaches are supported with theorems, experiments, and real-world field tests. Our system is being integrated into existing conservation software to become available to 1,000 protected areas worldwide.