PAWS: Protection Assistant for Wildlife Security

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

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.

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. 

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:

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.

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.

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.

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.

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.