Protection Assistant for Wildlife Security

Poaching is a threat to the population of key species and the whole ecosystem. How can we build computational tools based on game theory and machine learning to help patrols protect wildlife from poaching?

SC Faculty and Researchers

Fei Fang

Poaching of endangered species is reaching critical levels as the populations of these species plummet to unsustainable numbers. The global tiger population, for example, has dropped over 95% from the start of the 1900s and has resulted in three out of nine species extinctions. Depending on the area and animals poached, motivations for poaching range from profit to sustenance, with the former being more common when profitable species such as tigers, elephants, and rhinos are the targets.

To counter poaching efforts and to rebuild the species' populations, countries have set up protected wildlife reserves and conservation agencies tasked with defending these large reserves. Because of the size of the reserves and the common lack of law enforcement resources, conservation agencies are at a significant disadvantage when it comes to deterring and capturing poachers. Agencies use patrolling as a primary method of securing the park. Due to their limited resources, however, patrol managers must carefully create patrols that account for many different variables (e.g., limited patrol units to send out, multiple locations that poachers can attack at varying distances to the outpost).

PAWS takes basic information about the protected area and information about previous patrolling and poaching activities as input, and generates patrol routes as output. As the patrollers execute the patrol routes, more poaching data will be collected, and feed back to PAWS. The core algorithm of PAWS integrates learning poachers' behavior model, game-theoretic reasoning and route planning. More specifically, PAWS learns the behavior models of the poachers from the crime data collected. Based on the poachers' behavior model, PAWS calculates a randomized patrolling strategy, in the form of a set of patrol routes and the probabilities of taking each route. PAWS then suggests patrol routes sampled from this strategy to the patrollers.

A preliminary field test of PAWS was conducted in Uganda's Queen Elizabeth National Park (QENP) in April 2014. PAWS patrols were outputted onto a GPS unit as a series of waypoints. Using the set of waypoints on the GPS as a directional guide, wildlife rangers executed their patrol and searched for signs of illegal activity.

Project Publications

Fei Fang, Thanh H. Nguyen, Rob Pickles, Wai Y. Lam, Gopalasamy R. Clements, Bo An, Amandeep Singh, Milind Tambe, Andrew Lemieux. Deploying PAWS: Field Optimization of the Protection Assistant for Wildlife Security. In Proceedings of the Innovative Applications of Artificial Intelligence, January 2016 (Winner of Deployed Application Award).

Fei Fang, Peter Stone, Milind Tambe. When Security Games Go Green: Designing Defender Strategies to Prevent Poaching and Illegal Fishing. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), July 2015 (Computational Sustainability Track, Outstanding Paper Award).

Debarun Kar, Fei Fang, Francesco Maria Delle Fave, Nicole Sintov, Milind Tambe. "A Game of Thrones": When Human Behavior Models Compete in Repeated Stackelberg Security Games. In Proceedings of the Fourteenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2015.

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