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.

CURRENT PROJECTS

FEATURES VIDEOS

Overview of AI for Conservation

2022 KDD Keynote by Milind Tambe

PAWS: AI to Predict Poaching and Illegal Logging

2021 AI for Climate talk by Lily Xu

Poachers Have Killed All the Tigers in this Cambodian Wildlife Sanctuary but Rangers Are Fighting Back with Artificial Intelligence

Poachers have killed all the tigers in this Cambodian wildlife sanctuary but rangers are fighting back with artificial intelligence pic.twitter.com/QkEo5pVRUR

— BBC World Service (@bbcworldservice) August 14, 2019

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.

Cambridge Conservation Initiative Seminar

Prof Milind Tambe from Harvard University explores the application of AI for conservation

PROJECT PARTICIPANTS

Collaborators

SPONSORS

RELATED PUBLICATIONS

Game Theory on the Ground: The Effect of Increased Patrols on Deterring Poachers
Harvard CRCS Workshop on AI for Social Good, 2020
Lily Xu, Andrew Perrault, Andrew Plumptre, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, and Milind Tambe

BIRDSAI: A Dataset for Detection and Tracking in Aerial Thermal Infrared Videos
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

To Signal or Not To Signal: Exploiting Uncertain Real-Time Information in Signaling Games for Security and Sustainability
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020
Elizabeth Bondi, Hoon Oh, Haifeng Xu, Fei Fang, Bistra Dilkina, Milind Tambe

Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations
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

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

Wildlife GUARDSS: Using Uncertain Real-Time Information in Signaling Games for Sustainability
International Conference on Machine Learning AI for Social Good Workshop
Elizabeth Bondi, Hoon Oh, Haifeng Xu, Fei Fang, Bistra Dilkina, & Milind Tambe

AirSim-W: A Simulation Environment for Wildlife Conservation with UAVs
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

Mitigating the Curse of Correlation in Security Games by Entropy Maximization (Extended Abstract)
International Conference on Autonomous Agents and Multiagent Systems (AAMAS-18), 2018
Haifeng Xu, Shaddin Dughmi, Milind Tambe, Venil Loyd Noronha

Adaptive Resource Allocation for Wildlife Protection against Illegal Poachers
Proceedings of the Thirteenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2014
Rong Yang, Benjamin Ford, Milind Tambe, Andrew Lemieux

PAWS: Adaptive Game-theoretic Patrolling for Wildlife Protection (Demonstration)
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

Making the most of Our Regrets: Regret-based Solutions to Handle Payoff Uncertainty and Elicitation in Green Security Games
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

“A Game of Thrones”: When Human Behavior Models Compete in Repeated Stackelberg Security Games
International Joint Conference on Artificial Intelligence (IJCAI), 2015
Fei Fang, Peter Stone, Milind Tambe

Taking it for a Test Drive: A Hybrid Spatio-temporal Model for Wildlife Poaching Prediction Evaluated through a Controlled Field Test
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

PAWS – A Deployed Game-Theoretic Application to Combat Poaching
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

CAPTURE: A New Predictive Anti-Poaching Tool for Wildlife Protection
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

Deploying PAWS: Field Optimization of the Protection Assistant for Wildlife Security
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

Cloudy with a Chance of Poaching: Adversary Behavior Modeling and Forecasting with Real-World Poaching Data
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

Real-World Evaluation and Deployment of Wildlife Crime Prediction Models
PhD thesis, August 2017
Benjamin Ford

VIOLA: Video Labeling Application for Security Domains
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

Handling Continuous Space Security Games with Neural Networks
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

When AI helps Wildlife Conservation: Learning Adversary Behaviors in Green Security Games
PhD thesis, June 2017
Debarun Kar

SPOT Poachers in Action: Augmenting Conservation Drones with Automatic Detection in Near Real Time
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

Evaluation of Predictive Models for Wildlife Poaching Activity through Controlled Field Test in Uganda
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

Spatio-temporal Model for Wildlife Poaching Prediction Evaluated through a Controlled Field Test in Uganda
AAAI conference on Artificial Intelligence (AAAI-18), 2018
Shahrzad Gholami

Adversary models account for imperfect crime data: Forecasting and planning against real-world poachers (Corrected Version)
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