PAWS (Protection Assistant for Wildlife Security)

PAWS advances research in machine learning, AI planning, and behavior modeling for assisting in protection of wildlife. PAWS takes basic information about the protected area and information about previous patrolling and poaching activities as input, and generates predictions of potential poaching locations and possible patrol routes as output. The core algorithm of PAWS integrates machine learning for predicting poachers’ behavior, 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.

Motivation

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).

What PAWS is all About?

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.

Preliminary Field Test

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. The photos below were taking during the preliminary field tests.

Predictive Analytics

Predicting where poachers will strike next is vital to protecting endangered species. By leveraging knowledge about where and when poacher attacks have occurred, Machine Learning techniques can predict where the next attack will happen. Ensembles of decision trees have demonstrated their superiority in predictive performance in both laboratory experiments and real-world field tests. Moreover, decision trees are a “white-box” approach, meaning that domain experts (e.g., conservationists, park rangers) can easily look at the learned model (in the form of logical rules) and determine whether the decision tree is making reasonable inferences about how poachers behave. Future work will focus on augmenting the patrol planning capabilities of PAWS with this new predictive analytic approach, resulting in efficient patrol schedules that are more effectively targeted to where poachers will be attacking.

Animals at Murchison Fall National Park

One-years worth of snares

USC at Second Global Tiger Stocktaking Conference

USC and team patrol a tropical forest in Southeast Asia

We thank IBM for PhD fellowship for Rong Yang.

 

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