PAWS (Protection Assistant for Wildlife Security)

Sustainable development goals
  • Life on land
  • Reduced inequality
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About the project

PAWS advances research in machine learning, AI planning, and behavior modeling for assisting in the protection of wildlife. PAWS takes basic information about the protected area and information about previous patrolling and poaching activities and generates predictions of potential poaching locations and possible patrol routes.

More project information

The University of Southern California developed an algorithm that was first field tested in Uganda’s Queen Elizabeth National Park (QENP) in April 2014. 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.

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. 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. The project aims to contribute to the UN’s SDGs by protecting wildlife and life on land.