Communications of the ACM
A Combination of Machine Learning and Game Theory Helps Fight Elephant Poaching in ganda
Technology has located 10 antelope traps and elephant snares in Uganda's Queen Elizabeth National Park in the past month.
Credit: iStockPhoto.com
Technology that integrates machine learning and game theory, called Protection Assistant for Wildlife Security (PAWS), is being tested in Uganda as a way to fight elephant poachers. PAWS is designed to enable researchers to predict poacher attacks so they can advise rangers on what areas to patrol. To generate the predictions, researchers analyzed 12 years worth of data collected by rangers supplied by the Wildlife Conservation Society.
University of Southern California professor Milind Tambe says the data is sufficient to enable a machine-learning algorithm to make intelligent guesses about future poaching strikes. "We want to randomize our patrols because we ourselves don't want to become predictable to the poachers," he says. Game theory is thus tapped to suggest routes that will not be easily predictable, Tambe notes.
Ugandan rangers have used PAWS to locate 10 antelope traps and elephant snares in the past month, which Reuters says is "a far better score card than they could usually expect." Tambe notes his artificial intelligence-game theory solution has been used by the U.S. Coastguard, the Transportation Security Administration, the Federal Air Marshals Service, the Los Angeles Sheriff's Department, and other organizations to randomize their patrols since the early 2000s.
From Quartz
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