APE: A Data-Driven, Behavioral Model-Based Anti-Poaching Engine

Abstract

We consider the problem of protecting a set of animals such as rhinos and elephants in a game park using D drones and R ranger patrols (on the ground) with R >= D. Using two years of data about animal movements in a game park, we propose the probabilistic spatio-temporal graph (pSTG) model of animal movement behaviors and show how we can learn it from the movement data. Using 17 months of data about poacher behavior, we also learn the probability that a region in the game park will be targeted by poachers. We formalize the anti-poaching problem as that of finding a coordinated route for the drones and ranger patrols that maximize the expected number of animals that are protected, given these two models as input and show that it is NP-complete. Because of this, we fine tune classical local search and genetic algorithms to the case of anti-poaching by taking specific advantage of the nature of the anti-poaching problem and its objective function. We develop a measure of the quality of an algorithm to route the drones and ranger patrols called "improvement ratio." We develop a dynamic programming based APE_Coord_Route algorithm and show that it performs very well in practice, achieving an improvement ratio over 90%.