Illegal Smuggling and Global Wildlife Trade Prevention

global-alligator-trade

Global illegal wildlife trade threatens biodiversity and acts as a potential crisis of invasive species and disease spread. Despite a wide range of national and international policies and regulations designed to stop illegal wildlife trade, high profit margins and increasing demand drive a vigorous global illicit trade network. We aim to use artificial intelligence to understand more about the nature of illegal smuggling and wildlife trade.

Motivation: the next layer of protection after PAWS

PAWS is dedicated to protect wildlife in the first place. However, sometimes poachers can still dodge the patrol performed by the park rangers and successfully conduct poaching. The next step of poaching is to take all the animal parts and ship them to the market in order to get profit. The main motivation of this project is to leverage this neccessary step of poaching activity. We try to strategically allocate checkpoints around the national parks in order to catch the poachers we missed. A properly designed checkpoint allocation can deter poachers and smugglers by increasing their risk of shipping items out of national parks.

Teamcore Members

Milind Tambe
Kai Wang
Lily Xu

Collaborators

P. Jeffrey Brantingham (UCLA)
Rohit Singh, World Wildlife Fund (WWF)

Teamcore Alumn

Andrew Perrault

 

 

NeurIPS 2020 (spotlight presentation)

Kai Wang, Bryan Wilder, Andrew Perrault, and Milind Tambe

Automatically Learning Compact Quality-aware Surrogates for Optimization Problems

AAMAS 2020

Kai Wang, Andrew Perrault, Aditya Mate, and Milind Tambe

Scalable Game-Focused Learning of Adversary Models:Data-to-Decisions in Network Security Games

AAMAS 2020 Doctoral Consortium

Kai Wang 

Balance Between Scalability and Optimality in Network Security Games