AI for Conservation

Elephant

Game Theory and Machine Learning to Combat Crimes Against the Environment

Motivation

AI for Conservation refers to the application of artificial intelligence to conservation, such as wildlife protection and the protection of natural resources. For example, in the green security domain, the repeated and strategic interaction between those who protect these resources and those who seek to attack or exploit these resources can be modeled using game theory as a repeated game. While our predictive analytics effort focuses on predicting where adversaries (e.g., poachers) will strike, our prescriptive analytics work provides recommendations to defenders (e.g., rangers) to conduct strategic, randomized patrols. These analytics can be supported using machine learning, for example by detecting poachers or animals in unmanned aerial vehicle (UAV) imagery automatically.

Poachers have killed all the tigers in this Cambodian wildlife sanctuary but rangers are fighting back with artificial intelligence

PAWS Projects

AI for Animals

How is AI being used to protect the world’s most endangered animals?

Using ranger-generated data for predictive patrol planning - Evidence to Action #Research4IWT18

Green Security: How can AI help in protecting Forests, Fish and Wildlife

Green Security Game refers to the general framework to model the repeated and strategic interaction in green security domains such as wildlife protection and fishery protection. In Green Security Game framework, the problem in these domains is cast as a repeated game.

Who Is Involved

Teamcore Members

Teamcore Alumni

  • Debarun Kar
  • Benjamin Ford
  • Fei Fang
  • Shahrzad Gholami
  • Thanh Hong Nguyen
  • Rong Yang
  • Francesco Maria Delle Fave

Sponsors

National Science Foundation logo

Related Publications

Harvard CRCS Workshop on AI for Social Good, 2020

Lily Xu, Andrew Perrault, Andrew Plumptre, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, and Milind Tambe
Game Theory on the Ground: The Effect of Increased Patrols on Deterring Poachers

Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), 2020

Elizabeth Bondi, Raghav Jain, Palash Aggrawal, Saket Anand, Robert Hannaford, Ashish Kapoor, Jim Piavis, Shital Shah, Lucas Joppa, Bistra Dilkina, Milind Tambe
BIRDSAI: A Dataset for Detection and Tracking in Aerial Thermal Infrared Videos

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020

Elizabeth Bondi, Hoon Oh, Haifeng Xu, Fei Fang, Bistra Dilkina, Milind Tambe
To Signal or Not To Signal: Exploiting Uncertain Real-Time Information in Signaling Games for Security and Sustainability

IEEE International Conference on Data Engineering (ICDE-20)

Lily Xu, Shahrzad Gholami, Sara Mc Carthy, Bistra Dilkina, Andrew Plumptre, Milind Tambe, Rohit Singh, Mustapha Nsubuga, Joshua Mabonga, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Tom Okello, Eric Enyel
Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations

International Joint Conference on Artificial Intelligence

Elizabeth Bondi, Hoon Oh, Haifeng Xu, Fei Fang, Bistra Dilkina, & Milind Tambe
Biodiversity Conservation with Drones: Using Uncertain Real-Time Information in Signaling Games to Prevent Poaching

International Conference on Machine Learning AI for Social Good Workshop

Elizabeth Bondi, Hoon Oh, Haifeng Xu, Fei Fang, Bistra Dilkina, & Milind Tambe
Wildlife GUARDSS: Using Uncertain Real-Time Information in Signaling Games for Sustainability

In COMPASS ’18: ACM SIGCAS Conference on Computing and Sustainable Societies (COMPASS), June 20–22, 2018, Menlo Park and San Jose, CA, USA. ACM, New York, NY, USA.

Elizabeth Bondi, Debadeepta Dey, Ashish Kapoor, Jim Piavis, Shital Shah, Fei Fang, Bistra Dilkina, Robert Hannaford, Arvind Iyer, Lucas Joppa, Milind Tambe
AirSim-W: A Simulation Environment for Wildlife Conservation with UAVs

International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18), 2018

Haifeng Xu, Shaddin Dughmi, Milind Tambe, Venil Loyd Noronha
Mitigating the Curse of Correlation in Security Games by Entropy Maximization (Extended Abstract)

Proceedings of the Thirteenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2014

Rong Yang, Benjamin Ford, Milind Tambe, Andrew Lemieux
Adaptive Resource Allocation for Wildlife Protection against Illegal Poachers

Proceedings of the Thirteenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2014

Benjamin Ford, Debarun Kar, Francesco M. Delle Fave, Rong Yang, Milind Tambe
PAWS: Adaptive Game-theoretic Patrolling for Wildlife Protection (Demonstration)

6th Conference on Decision and Game Theory for Security (GameSec), 2015

Thanh H. Nguyen, Francesco M. Delle Fave, Debarun Kar, Aravind S. Lakshminarayanan, Amulya Yadav, Milind Tambe, Noa Agmon, Andrew J. Plumptre, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba
Making the most of Our Regrets: Regret-based Solutions to Handle Payoff Uncertainty and Elicitation in Green Security Games

International Joint Conference on Artificial Intelligence (IJCAI), 2015

Fei Fang, Peter Stone, Milind Tambe
“A Game of Thrones”: When Human Behavior Models Compete in Repeated Stackelberg Security Games

The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD Applied Data Science Track), 2017

Shahrzad Gholami, Benjamin Ford, Fei Fang, Andy Plumptre, Milind Tambe, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Mustafa Nsubaga, and Joshua Mabonga
Taking it for a Test Drive: A Hybrid Spatio-temporal Model for Wildlife Poaching Prediction Evaluated through a Controlled Field Test

AI Magazine, 2017

Fei Fang, Thanh H. Nguyen, Rob Pickles, Wai Y. Lam, Gopalasamy R. Clements, Bo An, Amandeep Singh, Brian C. Schwedock, Milind Tambe, Andrew Lemieux
PAWS – A Deployed Game-Theoretic Application to Combat Poaching

Proceedings of the 15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2016

Thanh H. Nguyen, Arunesh Sinha, Shahrzad Gholami, Andrew J. Plumptre, Lucas Joppa, Milind Tambe, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Rob Critchlow, and Colin Beale
CAPTURE: A New Predictive Anti-Poaching Tool for Wildlife Protection

Innovative Applications of Artificial Intelligence Twenty-Eighth IAAI Conference (Winner of Deployed Application Award), January 2016

Fei Fang, Thanh H. Nguyen, Rob Pickles, Wai Y. Lam, Gopalasamy R. Clements, Bo An, Amandeep Singh, Milind Tambe, and Andrew Lemieux
Deploying PAWS: Field Optimization of the Protection Assistant for Wildlife Security

Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2017

Debarun Kar and Benjamin Ford, Shahrzad Gholami, Fei Fang, Andrew Plumptre, Milind Tambe, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba
Cloudy with a Chance of Poaching: Adversary Behavior Modeling and Forecasting with Real-World Poaching Data

PhD thesis, August 2017

Benjamin Ford
Real-World Evaluation and Deployment of Wildlife Crime Prediction Models

Conference on Decision and Game Theory for Security (GameSec), 2017

Elizabeth Bondi, Fei Fang, Debarun Kar, Venil Noronha, Donnabell Dmello, Milind Tambe, Arvind Iyer, and Robert Hannaford
VIOLA: Video Labeling Application for Security Domains

In IWAISe: 1st International Workshop on A.I. in Security held at the International Joint Conference on Artificial Intelligence, 2017

Nitin Kamra, Fei Fang, Debarun Kar, Yan Liu, Milind Tambe
Handling Continuous Space Security Games with Neural Networks

PhD thesis, June 2017

Debarun Kar
When AI helps Wildlife Conservation: Learning Adversary Behaviors in Green Security Games

Proceedings of the Thirtieth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-18), February 2018

Elizabeth Bondi, Fei Fang, Mark Hamilton, Debarun Kar, Donnabell Dmello, Jongmoo Choi, Robert Hannaford, Arvind Iyer, Lucas Joppa, Milind Tambe, Ram Nevatia
SPOT Poachers in Action: Augmenting Conservation Drones with Automatic Detection in Near Real Time

AAAI conference on Artificial Intelligence (AAAI-18), 2018

Shahrzad Gholami, Benjamin Ford, Debarun Kar, Fei Fang, Milind Tambe, Andrew Plumptre, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Mustapha Nsubaga, Joshua Mabonga
Evaluation of Predictive Models for Wildlife Poaching Activity through Controlled Field Test in Uganda

AAAI conference on Artificial Intelligence (AAAI-18), 2018

Shahrzad Gholami

Spatio-temporal Model for Wildlife Poaching Prediction Evaluated through a Controlled Field Test in Uganda

International Conference on Autonomous Agents and Multi-agent Systems (AAMAS 2018), 2018

Shahrzad Gholami, Sara Mc Carthy, Bistra Dilkina, Andrew Plumptre, Milind Tambe, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Mustapha Nsubaga, Joshua Mabonga, Tom Okello, Eric Enyel
Adversary models account for imperfect crime data: Forecasting and planning against real-world poachers (Corrected Version)

 

Collaborators

Rob Pickles, Panthera

Wai Y. Lam Gopalasamy R. Clements, Panthera & Rimba

Andrew Lemieux, Nethelands Institute for the Study of Crime and Law Enforcement

Andrew J, Plumptre, Wildlife Conservation Society

Lucas Joppa, Microsoft Research

Arnaud Lyet, World Wildlife Fund

Nicole Sintov, Sol Price School of Public Policy, USC

Bo An, Nanyang Technological University

Rob Hannaford, Air Shepherd