Outwitting Poachers With Artificial Intelligence

by  Aaron Dubrow,   National Science Foundation

A century ago, more than 60,000 tigers roamed the wild. Today, the worldwide estimate has dwindled to around 3,200.

Poaching is one of the main drivers of this precipitous drop. Whether killed for skins, medicine or trophy hunting, humans have pushed tigers to near-extinction. The same applies to other large animal species like elephants and rhinoceros that play unique and crucial roles in the ecosystems where they live.

Human patrols serve as the most direct form of protection of endangered animals, especially in large national parks. However, protection agencies have limited resources for patrols.

“In most parks, ranger patrols are poorly planned, reactive rather than pro-active, and habitual,” according to Fei Fang, a Ph.D. candidate in the computer science department at the University of Southern California (USC).

Fang is part of an NSF-funded team at USC led by Milind Tambe, professor of computer science and industrial and systems engineering and director of the Teamcore Research Group on Agents and Multiagent Systems. Their research builds on the idea of “green security games” — the application of game theory to wildlife protection.

A joint effort between researchers and conservationists in the U.S., Singapore, the Netherlands, and Malaysia seeks to apply artificial intelligence (AI) and game theory to combat poaching and other activities.

“We need to provide actual patrol routes that can be practically followed,” Fang said. “These routes need to go back to a base camp and the patrols can’t be too long. We list all possible patrol routes and then determine which is most effective.”

The application also randomizes patrols to avoid falling into predictable patterns.

“If the poachers observe that patrols go to some areas more often than others, then the poachers place their snares elsewhere,” Fang said.

With backing from the U.S. National Science Foundation and the Army Research Office, a team at the University of Southern California (USC) is behind the development of AI apps such as Protection Assistant for Wildlife Security (PAWS), which aims to boost the efficiency of anti-poaching patrols using data on previous patrols and proof of poaching.

PAWS “learns” and improves its planning strategy as it accumulates more data, and it can incorporate complex terrain information, resulting in practical patrol routes that minimize elevation changes. PAWS also can account for natural transit paths with the most animal traffic and thus where poaching is most frequent to generate a “street map” for patrols.

The USC team recently merged PAWS with the Comprehensive Anti-Poaching Tool with Temporal and Observation Uncertainty Reasoning to predict the likelihood of poaching attacks with greater accuracy. The team also is developing, via a multi-university/nonprofit alliance, methods to prevent illegal logging in Madagascar using game theory. One such method is Simultaneous Optimization of Resource Teams, an algorithm that integrates maps of national parks and security resource costs to gauge the best resource combination to offer maximum protection to the area in question.  Read the article