A new system has emerged that will allow animal advocates to fight poachers. She uses historical data about where illegal hunters appear most.
According to the UN, the illegal trade in animals is a global and lucrative business that generates between US $8 billion and US $10 billion annually. Due to the scale of this activity, dozens of species of rare animals are under threat. For example, at the beginning of the 20th century, according to researchers, more than 100 thousand tigers lived throughout Southeast Asia. Today, less than 4,000 tigers remain in the wild due to habitat loss and aggressive poaching.
AI and machine learning have the potential to dramatically improve the efficiency of law enforcement by helping them track where poachers have been and predict where they are most likely to appear.
One of these systems is PAWS (Protection Assistant for Wildlife Security). Professor Milind Tambe, co-founder of the Center for Artificial Intelligence at Harvard University and director of the Center for Computing and Society Research, led its development after attending the Global Tiger Initiative in 2013.
PAWS uses poaching data from the World Wildlife Foundation’s open SMART (Spatial Monitoring and Reporting Tool) and uses game theory in which the player must optimize limited resources for the greatest defense against threats and attacks. In this way, the model can build the most efficient routes for the rangers, taking into account the historical data.
They will be used in the territory where only 100 rangers are working on patrols on 2 thousand square kilometers of the park. PAWS first divides the park into separate 1 km squares and then assigns a risk factor to each square based on where the hunters were previously found. This is data collected by the SMART system over the past ten years.
The system then suggests patrol routes in areas of greatest risk. These suggestions change over time as poachers adjust to the actions of the rangers. Routes are also influenced by the season, the location of paths, rivers and roads; weather and topographic conditions.