Poaching and illegal logging is a horrible trend of modern times showing humans have little to no respect for nature. And stopping those who go about illegally killing animals and cutting down trees can be downright difficult. Now, the National Science Foundation has turned to artificial intelligence to help out.
This isn’t the first use of modern technology to help fight this problem. Earlier this year, realistic robots were used as traps, and of course there are also drones. But now, researchers are using Artificial Intelligence (A.I.) applications that incorporate a game theory model for wildlife protection.
“Poacher hunters follow a path created by PAWS application driven by A.I.” Photo credit: team leader from Rimba
The app is called PAWS (which stands for the Protection Assistant for Wildlife Security). It was developed in 2013 and in 2014, its pilot system was put to the test in Malaysia and Uganda. While the system revealed some limitations, there were many improvements that resulted from the first tests.
PAWS uses computer and mathematical models to predict the poachers’ behaviors
PAWS analyzes the terrain and topography of an area, incorporating the paths where animals most often travel–which means poachers will be more likely to travel them as well. In addition, the app can prescribe randomized patrols, which can keep poachers from learning a pattern and predicting when a wildlife officer may be nearby.
“This new app will help protect wildlife from being hunted and killed by poachers” by Paul Mannix is licensed under CC BY 2.0
Recently, PAWS was combined with another algorithm, called CAPTURE (Comprehensive Anti-Poaching Tool with Temporal and Observation Uncertainty Reasoning), which is used to predict how likely a poacher attack will be.
And to combat illegal loggers, researchers have developed a method known as SORT (which stands for Simultaneous Optimization of Resource Teams). SORT takes into account the maps of national parks, and models using the budget constraints, and so far has been tested using data from Madagascar.
H/T to popsci; techtimes; teamcore
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