PAWS, which stands for Protection Assistant for Wildlife Security, is an innovative solution for protecting endangered species targeted by illegal poaching. PAWS was created in 2013 by Milind Tambe, then a professor of computer science at the University of Southern California, and a team of Ph.D. students who were alarmed by the rate at which species like elephants and tigers were being poached. 

PAWS uses machine learning to predict where poachers might strike next, based on analyses of terrain navigation preferences of both animals and poachers. The software then applies this predictive modeling, combined with game-theoretic reasoning, to design patrolling routes for park rangers, relying on input data on the local area and past poaching and patrol routes. By “randomizing” patrol routes, it puts patrollers in a position where they are more likely to outsmart poachers and prevent killings, while also protecting the rangers themselves since poachers will struggle to learn ranger’s routes. 

PAWS offers an innovative new way to fight poaching. With poachers using ever-more advanced tools to find and kill animals without detection from rangers, wildlife conservation organizations tasked with protecting species under threat from poaching are using PAWS to match and outsmart poachers.  

PAWS was originally tested  in Cambodia, Malaysia and Uganda. In 2020, with funding from Microsoft AI for Good, PAWS launched in a dozen countries around the world.


Zewe, Adam. (2019, Oct. 10). “Outsmarting Poachers.” Harvard University John A. Paulson School of Engineering and Applied Sciences.;

Khalid, Samia. (2018, July 23).  “AI, Drones and Game Theory for Smart Wildlife Conservation.” Medium.

Snow, Jackie. (2016, June 12). “Rangers Use Artificial Intelligence to Fight Poachers.” National Geographic.

Bondi, Elizabeth, Hoon Oh, Haifeng Xu, Fei Fang, Bistra Dilkina, and Milind Tambe. “Using Game Theory in Real Time in the Real World: A Conservation Case Study.” In Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, pp. 2336-2338. International Foundation for Autonomous Agents and Multiagent Systems, 2019.


Artificial Intelligence, Biodiversity