Adaptive resource allocation for wildlife protection against illegal poachers

Abstract

Illegal poaching is an international problem that leads to the extinc-tion of species and the destruction of ecosystems. As evidenced by dangerously dwindling populations of endangered species, exist-ing anti-poaching mechanisms are insufficient. This paper intro-duces the Protection Assistant for Wildlife Security (PAWS) appli-cation- a joint deployment effort done with researchers at Uganda’s Queen Elizabeth National Park (QENP) with the goal of improving wildlife ranger patrols. While previous works have deployed appli-cations with a game-theoretic approach (specifically Stackelberg Games) for counter-terrorism, wildlife crime is an important do-main that promotes a wide range of new deployments. Additional-ly, this domain presents new research challenges and opportunities related to learning behavioral models from collected poaching data. In addressing these challenges, our first contribution is a behavioral model extension that captures the heterogeneity of poachers ’ deci-sion making processes. Second, we provide a novel framework, PAWS-Learn, that incrementally improves the behavioral model of the poacher population with more data. Third, we develop a new algorithm, PAWS-Adapt, that adaptively improves the resource al-location strategy against the learned model of poachers. Fourth, we demonstrate PAWS’s potential effectiveness when applied to pa-trols in QENP, where PAWS will be deployed.