PAWS: Protection Assistant for Wildlife Security

Jungle landscape

PAWS:

Protection Assistant for Wildlife Security

Applying AI to Wildlife Conservation

 

 

 

PAWS &
ARTIFICIAL
INTELLEGENCE

Milind Tambe and Lily Xu speaking with Ly Bora, park manager at Srepok Wildlife Sanctuary in Cambodia.

The Protection Assistant for Wildlife Security (PAWS) is an artificial intelligence (AI) software for wildlife protection. PAWS helps conservation managers plan more informed patrol strategies to combat wildlife poaching, illegal logging, and illegal fishing. Taking in past poaching records and data about the geography of the protected area, PAWS uses machine learning to predict poachers’ behavior. After learning a model of poaching behavior, PAWS produces poaching risk maps and suggests patrol routes for rangers.

This AI-based anti-poaching tool has been developed in collaboration with conservation specialists at the Uganda Wildlife Authority, Wildlife Conservation Society, and the World Wide Fund for Nature. In December 2018, we began conducting field tests in Srepok Wildlife Sanctuary (SWS) in Cambodia, an area identified as the most suitable site for tiger reintroduction in Southeast Asia. During the first month of field tests, rangers conducted patrols based on regions we suggested, during which they detected and removed 1,000 snares — more than double before the deployment of the AI software. Rangers also confiscated 42 chainsaws, 24 motorbikes, and one truck. Additionally, PAWS is being integrated with SMART conservation software, a monitoring and reporting tool used in over 800 protected areas worldwide

  

MOTIVATION:
The Snaring Crisis

A sampling of all the snares and chainsaws that rangers have removed from Srepok Wildlife Sanctuary.
A sampling of all the snares and chainsaws that rangers have removed from Srepok Wildlife Sanctuary.


The Protection Assistant for Wildlife Security (PAWS) is an artificial intelligence (AI) software for wildlife protection. PAWS helps conservation managers plan more informed patrol strategies to combat wildlife poaching, illegal logging, and illegal fishing. Taking in past poaching records and data about the geography of the protected area, PAWS uses machine learning to predict poachers’ behavior. After learning a model of poaching behavior, PAWS produces poaching risk maps and suggests patrol routes for rangers.

This AI-based anti-poaching tool has been developed in collaboration with conservation specialists at the Uganda Wildlife Authority, Wildlife Conservation Society, and the World Wide Fund for Nature. In December 2018, we began conducting field tests in Srepok Wildlife Sanctuary (SWS) in Cambodia, an area identified as the most suitable site for tiger reintroduction in Southeast Asia. During the first month of field tests, rangers conducted patrols based on regions we suggested, during which they detected and removed 1,000 snares — more than double before the deployment of the AI software. Rangers also confiscated 42 chainsaws, 24 motorbikes, and one truck. Additionally, PAWS is being integrated with SMART conservation software, a monitoring and reporting tool used in over 800 protected areas worldwide.

  

GLOBAL DEPLOYMENT
OF PAWS

SMART software is used in over 800 protected areas across 60 countries.

SMART is a software used for protected area management by over 800 national parks around the globe. The SMART partnership is comprised of nine leading conservation NGOs including the World Wildlife Fund and Wildlife Conservation Society. In collaboration with SMART and Microsoft AI for Earth, we are making PAWS available to parks around the world to predict and plan against snaring, illegal logging, and illegal fishing. Read about how we're integrating PAWS into SMART to deploy our predictive models globally. To help under-resourced parks, we have also worked to integrate remote sensing imagery to augment their data and improve predictions. 

  

CURRENT PROJECTS

Robust Reinforcement Learning Under Minimax Regret for Green Security 
(UAI'21)
[
paper]

Our paper Robust Reinforcement Learning Under Minimax Regret for Green Security, which appeared at UAI 2021, addresses the problem of planning patrols when we have uncertainty about the exact behavior of the adversary. Specifically, we focus on robust patrol planning under the minimax regret criterion (as opposed to maximin reward, which tends to be overly conservative). To do so, we model the uncertainty as a "nature" adversary; the patrol planning problem then can be set up as a zero-sum game between the patrol planner and nature. Our algorithm, MIRROR, is the first to calculate minimax regret-optimal policies using reinforcement learning. Watch the following talk for more details:

 

Robust Reinforcement Learning Under Minimax Regret for Green Security on YouTube

  

Multi-armed bandits to balance exploration and exploitation
(AAAI'21)

[paper] [talk]

Lily Xu joining rangers on a patrol in Srepok Wildlife Sanctuary.

Our paper Dual-Mandate Patrols: Multi-Armed Bandits for Green Security, which was selected as a Best Paper Runner Up at AAAI 2021, focuses on helping rangers choose where to patrol in a protected area, balancing exploration of infrequently visited regions and exploitation of known hotspots. Our proposed LIZARD algorithm bridges the gap between combinatorial and Lipschitz bandits, presenting an approach that achieves theoretical no-regret and performs well in experiments on real-world poaching data.

  

Predicting Poaching, Planning Patrols, and Evaluating in the Field
(ICDE'20)
[paper]

Our ICDE 2020 paper Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations addresses uncertainty in poaching predictions to help design patrol plans that are robust to uncertainty. We evaluate our predictions in months-long field tests in Murchison Falls National Park in Uganda and Srepok Wildlife Sanctuary in Cambodia.

  

Adversary Models Account for Imperfect Crime Data: Forecasting and planning against real-world poachers
(AAMAS'18)
[paper]

Andrew Lemieux demonstrating features of the GPS
Andrew Lemieux demonstrating
features of the GPS
A QENP ranger using the GPS
A QENP ranger using the GPS

In this innovative application paper, we introduce iWare-E a novel imperfect-observation aWare Ensemble (iWare-E) technique, which is designed to handle the uncertainty in crime information efficiently. This approach leads to superior accuracy and efficiency for adversary behavior prediction compared to the previous state-of-the-art. We also demonstrate the country-wide efficiency of the models and are the first to evaluate our adversary behavioral model across different protected areas in Uganda. Lastly, we provide a scalable planning algorithm to design fine-grained patrol routes for the rangers, which achieves up to 150% improvement in number of predicted attacks detected.

 

Security Games to Prevent Poaching
& Illegal Fishing
(IJCAI'15)

[
Paper]

 Protection Assistant for Wildlife Security (PAWS) on YouTube


When Security Games Go Green: Designing Defender Strategies to Prevent Poaching and Illegal Fishing was selected as a Outstanding Paper Award Winner in IJCAI-15 Computational Sustainability Track at IJCAI 2015. This paper introduces Green Security Games (GSGs), a novel game model for green security domains with a generalized Stackelberg assumption; provides algorithms to plan effective sequential defender strategies — such planning was absent in previous work; proposes a novel approach to learn adversary models that further improves defender performance; and (provides detailed experimental analysis of proposed approaches.

 

People in the jungle
PAWS team and collaborators patrol in a
tropical forest in Southeast Asia
People in the jungle
Steep elevation ascent

 

 

Introducing PAWS
(AAMAS'14)

PAWS Core diagram

Adaptive resource allocation for wildlife protection against illegal poachers introduces the Protection Assistant for Wildlife Security (PAWS) as a joint deployment effort done with researchers at Uganda’s Queen Elizabeth National Park (QENP) with the goal of improving wildlife ranger patrols. While previous works have deployed applications with a game-theoretic approach (specifically Stackelberg Games) for counter-terrorism, wildlife crime is an important domain that promotes a wide range of new deployments. Additionally, this domain presents new research challenges and opportunities related to learning behavioral models from collected poaching data. To address these challenges, we contribute a behavioral model extension that captures the heterogeneity of poachers’ decision making processes. Second, we provide a novel framework, PAWS-Learn, that incrementally improves the behavioral model of the poacher population with more data. Third, we develop a new algorithm, PAWS-Adapt, that adaptively improves the resource allocation strategy against the learned model of poachers. Fourth, we demonstrate PAWS’s potential effectiveness when applied to patrols in QENP, where PAWS will be deployed.

 

PROJECT
PARTICIPANTS

Teamcore Members

Milind Tambe
Lily Xu
Andrew Perrault
Kai Wang
Susobhan Ghosh
Rachel Guo
April Chen

                                                                 

 

Teamcore Alumni

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

 

  
 

SPONSORS

National Science Foundation logo

 

 

 

RELATED
PUBLICATIONS

Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI) 2021

Lily Xu, Andrew Perrault, Fei Fang, Haipeng Chen, Milind Tambe

Robust Reinforcement Learning Under Minimax Regret for Green Security

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI) 2021

Lily Xu, Elizabeth Bondi, Fei Fang, Andrew Perrault, Kai Wang, Milind Tambe
Dual-Mandate Patrols: Multi-Armed Bandits for Green Security

NeurIPS 2020 Workshop on Machine Learning for the Developing World

Rachel Guo, Lily Xu, Drew Cronin, Francis Okeke, Andrew Plumptre, Milind Tambe
Enhancing Poaching Predictions for Under-Resourced Wildlife Conservation Parks Using Remote Sensing Imagery

Conference on Uncertainty in Artificial Intelligence (UAI) 2020

Ayan Mukhopadhyay, Kai Wang, Andrew Perrault, Mykel Kochenderfer, Milind Tambe, and Yevgeniy Vorobeychik

Robust Spatial-Temporal Incident Prediction

IEEE International Conference on Data Engineering (ICDE), 2020

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

AAAI 2020

Andrew Perrault, Bryan Wilder, Eric Ewing, Aditya Mate, Bistra Dilkina and Milind Tambe

End-to-End Game-Focused Learning of Adversary Behavior in Security Games

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)

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

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 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

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

Computational Sustainability Workshop held at The Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI-15), 2015

Debarun Kar, Fei Fang, Francesco Delle Fave, Nicole Sintov, Milind Tambe, Arlette van Wissen
Effectiveness of Probability Perception Modeling and Defender Strategy Generation Algorithms in Repeated Stackelberg Games: An Initial Report

Proceedings of the Fourteenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2015

Debarun Kar, Fei Fang, Francesco Maria Delle Fave, Nicole Sintov, Milind Tambe
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
When Security Games Go Green: Designing Defender Strategies to Prevent Poaching and Illegal Fishing

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

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)

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