Conservation

2022
Lily Xu, Arpita Biswas, Fei Fang, and Milind Tambe. 7/23/2022. “Ranked Prioritization of Groups in Combinatorial Bandit Allocation.” International Joint Conference on Artificial Intelligence (IJCAI) 31. Vienna, Austria. arXiv linkAbstract
Preventing poaching through ranger patrols is critical for protecting endangered wildlife. Combinatorial bandits have been used to allocate limited patrol resources, but existing approaches overlook the fact that each location is home to multiple species in varying proportions, so a patrol benefits each species to differing degrees. When some species are more vulnerable, we ought to offer more protection to these animals; unfortunately, existing combinatorial bandit approaches do not offer a way to prioritize important species. To bridge this gap, (1) We propose a novel combinatorial bandit objective that trades off between reward maximization and also accounts for prioritization over species, which we call ranked prioritization. We show this objective can be expressed as a weighted linear sum of Lipschitz-continuous reward functions. (2) We provide RankedCUCB, an algorithm to select combinatorial actions that optimize our prioritization-based objective, and prove that it achieves asymptotic no-regret. (3) We demonstrate empirically that RankedCUCB leads to up to 38% improvement in outcomes for endangered species using real-world wildlife conservation data. Along with adapting to other challenges such as preventing illegal logging and overfishing, our no-regret algorithm addresses the general combinatorial bandit problem with a weighted linear objective.
Susobhan Ghosh, Pradeep Varakantham, Aniket Bhatkhande, Tamanna Ahmad, Anish Andheria, Wenjun Li, Aparna Taneja, Divy Thakkar, and Milind Tambe. 2/15/2022. “Facilitating Human-Wildlife Cohabitation through Conflict Prediction.” Innovative Applications of Artificial Intelligence. iaai_wct.pdf
2021
Lily Xu. 10/24/2021. “Learning, Optimization, and Planning Under Uncertainty for Wildlife Conservation.” INFORMS Doing Good with Good OR.Abstract

Wildlife poaching fuels the multi-billion dollar illegal wildlife trade and pushes countless species to the brink of extinction. To aid rangers in preventing poaching in protected areas around the world, we have developed PAWS, the Protection Assistant for Wildlife Security. We present technical advances in multi-armed bandits and robust sequential decision-making using reinforcement learning, with research questions that emerged from on-the-ground challenges. We also discuss bridging the gap between research and practice, presenting results from field deployment in Cambodia and large-scale deployment through integration with SMART, the leading software system for protected area management used by over 1,000 wildlife parks worldwide.

paws_informs.pdf
Lily Xu. 8/21/2021. “Learning and Planning Under Uncertainty for Green Security.” 30th International Joint Conference on Artificial Intelligence (IJCAI). xu-dc-ijcai21.pdf
Lily Xu, Andrew Perrault, Fei Fang, Haipeng Chen, and Milind Tambe. 7/27/2021. “Robust Reinforcement Learning Under Minimax Regret for Green Security.” Conference on Uncertainty in Artificial Intelligence (UAI).Abstract
Green security domains feature defenders who plan patrols in the face of uncertainty about the adversarial behavior of poachers, illegal loggers, and illegal fishers. Importantly, the deterrence effect of patrols on adversaries' future behavior makes patrol planning a sequential decision-making problem. Therefore, we focus on robust sequential patrol planning for green security following the minimax regret criterion, which has not been considered in the literature. We formulate the problem as a game between the defender and nature who controls the parameter values of the adversarial behavior and design an algorithm MIRROR to find a robust policy. MIRROR uses two reinforcement learning-based oracles and solves a restricted game considering limited defender strategies and parameter values. We evaluate MIRROR on real-world poaching data.
xu_uai21_robust_rl.pdf
Lily Xu, Andrew Perrault, Fei Fang, Haipeng Chen, and Milind Tambe. 5/5/2021. “Robustness in Green Security: Minimax Regret Optimality with Reinforcement Learning.” AAMAS Workshop on Autonomous Agents for Social Good. robust_rl_aamas_aasg.pdf
Lily Xu, Elizabeth Bondi, Fei Fang, Andrew Perrault, Kai Wang, and Milind Tambe. 2/2021. “Dual-Mandate Patrols: Multi-Armed Bandits for Green Security.” In Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21).Abstract
Conservation efforts in green security domains to protect wildlife and forests are constrained by the limited availability of defenders (i.e., patrollers), who must patrol vast areas to protect from attackers (e.g., poachers or illegal loggers). Defenders must choose how much time to spend in each region of the protected area, balancing exploration of infrequently visited regions and exploitation of known hotspots. We formulate the problem as a stochastic multi-armed bandit, where each action represents a patrol strategy, enabling us to guarantee the rate of convergence of the patrolling policy. However, a naive bandit approach would compromise short-term performance for long-term optimality, resulting in animals poached and forests destroyed. To speed up performance, we leverage smoothness in the reward function and decomposability of actions. We show a synergy between Lipschitz-continuity and decomposition as each aids the convergence of the other. In doing so, we bridge the gap between combinatorial and Lipschitz bandits, presenting a no-regret approach that tightens existing guarantees while optimizing for short-term performance. We demonstrate that our algorithm, LIZARD, improves performance on real-world poaching data from Cambodia.
dual_mandate_patrols_aaai.pdf
Anika Puri and Elizabeth Bondi. 2021. “Space, Time, and Counts: Improved Human vs Animal Detection in Thermal Infrared Drone Videos for Prevention of Wildlife Poaching” Fragile Earth (FEED) Workshop at KDD 2021. feed_kdd_2021_final.pdf
2020
Rachel Guo, Lily Xu, Drew Cronin, Francis Okeke, Andrew Plumptre, and Milind Tambe. 12/12/2020. “Enhancing Poaching Predictions for Under-Resourced Wildlife Conservation Parks Using Remote Sensing Imagery”. Publisher's Version
Kai Wang, Bryan Wilder, Andrew Perrault, and Milind Tambe. 12/5/2020. “Automatically Learning Compact Quality-aware Surrogates for Optimization Problems.” In NeurIPS 2020 (spotlight). Vancouver, Canada.Abstract
Solving optimization problems with unknown parameters often requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using these values. Recent work has shown that including the optimization problem as a layer in the model training pipeline results in predictions of the unobserved parameters that lead to higher decision quality. Unfortunately, this process comes at a large computational cost because the optimization problem must be solved and differentiated through in each training iteration; furthermore, it may also sometimes fail to improve solution quality due to  non-smoothness issues that arise when training through a complex optimization layer. To address these shortcomings, we learn a low-dimensional surrogate model of a large optimization problem by representing the feasible space in terms of meta-variables, each of which is a linear combination of the original variables. By training a low-dimensional surrogate model end-to-end, and jointly with the predictive model, we achieve: i) a large reduction in training and inference time; and ii) improved performance by focusing attention on the more important variables in the optimization and learning in a smoother space. Empirically, we demonstrate these improvements on a non-convex adversary modeling task, a submodular recommendation task and a convex portfolio optimization task.
automatically_learning_compact_quality_aware_surrogates_for_optimization_problems.pdf
Lily Xu, Andrew Perrault, Andrew Plumptre, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, and Milind Tambe. 7/20/2020. “Game Theory on the Ground: The Effect of Increased Patrols on Deterring Poachers.” Harvard CRCS Workshop on AI for Social Good. Publisher's VersionAbstract
Applications of artificial intelligence for wildlife protection have focused on learning models of poacher behavior based on historical patterns. However, poachers' behaviors are described not only by their historical preferences, but also their reaction to ranger patrols. Past work applying machine learning and game theory to combat poaching have hypothesized that ranger patrols deter poachers, but have been unable to find evidence to identify how or even if deterrence occurs. Here for the first time, we demonstrate a measurable deterrence effect on real-world poaching data. We show that increased patrols in one region deter poaching in the next timestep, but poachers then move to neighboring regions. Our findings offer guidance on how adversaries should be modeled in realistic game-theoretic settings.
poaching_deterrence.pdf
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, and Eric Enyel. 4/20/2020. “Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations.” In IEEE International Conference on Data Engineering (ICDE-20).Abstract
Illegal wildlife poaching threatens ecosystems and drives endangered species toward extinction. However, efforts for wildlife protection are constrained by the limited resources of law enforcement agencies. To help combat poaching, the Protection Assistant for Wildlife Security (PAWS) is a machine learning pipeline that has been developed as a data-driven approach to identify areas at high risk of poaching throughout protected areas and compute optimal patrol routes. In this paper, we take an end-to-end approach to the data-to-deployment pipeline for anti-poaching. In doing so, we address challenges including extreme class imbalance (up to 1:200), bias, and uncertainty in wildlife poaching data to enhance PAWS, and we apply our methodology to three national parks with diverse characteristics. (i) We use Gaussian processes to quantify predictive uncertainty, which we exploit to improve robustness of our prescribed patrols and increase detection of snares by an average of 30%. We evaluate our approach on real-world historical poaching data from Murchison Falls and Queen Elizabeth National Parks in Uganda and, for the first time, Srepok Wildlife Sanctuary in Cambodia. (ii) We present the results of large-scale field tests conducted in Murchison Falls and Srepok Wildlife Sanctuary which confirm that the predictive power of PAWS extends promisingly to multiple parks. This paper is part of an effort to expand PAWS to 800 parks around the world through integration with SMART conservation software. 
icde_arxiv_version.pdf
Kai Wang. 2020. “Balance Between Scalability and Optimality in Network Security Games.” In Doctoral Consortium at International Conference on Autonomous Agents and Multi-agent Systems (AAMAS).Abstract
Network security games (NSGs) are widely used in security related domain to model the interaction between the attacker and the defender. However, due to the complex graph structure of the entire network, finding a Nash equilibrium even when the attacker is fully rational is not well-studied yet. There is no efficient algorithms known with valid guarantees. We identify two major issues of NSGs: i) non-linearity ii) correlation between edges. NSGs with non-linear objective function are usually hard to optimize, while correlated edges might create exponentially many strategies and impact the scalability. In this paper, we analyze the distortion of linear and non-linear formulations of NSGs with fully rational attacker. We provide theoretical bounds on these different formulations, which can quantify the approximation ratio between linear and non-linear assumption. This result can help us understand how much loss will the linearization incur in exchange for the scalability.
2020_10_teamcore_gcn_interdiction_bound.pdf
Elizabeth Bondi, Raghav Jain, Palash Aggrawal, Saket Anand, Robert Hannaford, Ashish Kapoor, Jim Piavis, Shital Shah, Lucas Joppa, Bistra Dilkina, and Milind Tambe. 2020. “BIRDSAI: A Dataset for Detection and Tracking in Aerial Thermal Infrared Videos.” In WACV. 2020_07_teamcore_wacv_birdsai.pdf
Andrew Perrault, Bryan Wilder, Eric Ewing, Aditya Mate, Bistra Dilkina, and Milind Tambe. 2020. “End-to-End Game-Focused Learning of Adversary Behavior in Security Games.” AAAI Conference on Artificial Intelligence. New York. 1903.00958.pdf
Ayan Mukhopadhyay, Kai Wang, Andrew Perrault, Mykel Kochenderfer, Milind Tambe, and Yevgeniy Vorobeychik. 2020. “Robust Spatial-Temporal Incident Prediction.” Conference on Uncertainty in Artificial Intelligence (UAI). Toronto. uai_1.pdf
Kai Wang, Andrew Perrault, Aditya Mate, and Milind Tambe. 2020. “Scalable Game-Focused Learning of Adversary Models: Data-to-Decisions in Network Security Games.” In International Conference on Autonomous Agents and Multi-agent Systems (AAMAS).Abstract
Previous approaches to adversary modeling in network security games (NSGs) have been caught in the paradigm of first building a full adversary model, either from expert input or historical attack data, and then solving the game. Motivated by the need to disrupt the multibillion dollar illegal smuggling networks, such as wildlife and drug trafficking, this paper introduces a fundamental shift in learning adversary behavior in NSGs by focusing on the accuracy of the model using the downstream game that will be solved. Further, the paper addresses technical challenges in building such a game-focused learning model by i) applying graph convolutional networks to NSGs to achieve tractability and differentiability and ii) using randomized block updates of the coefficients of the defender's optimization in order to scale the approach to large networks. We show that our game-focused approach yields scalability and higher defender expected utility than models trained for accuracy only.
2020_09_teamcore_gcn_interdiction.pdf
Elizabeth Bondi, Hoon Oh, Haifeng Xu, Fei Fang, Bistra Dilkina, and Milind Tambe. 2020. “To Signal or Not To Signal: Exploiting Uncertain Real-Time Information in Signaling Games for Security and Sustainability.” In AAAI conference on Artificial Intelligence. 2020_02_teamcore_aaai_signaluncertainty.pdf
2018
Elizabeth Bondi, Debadeepta Dey, Ashish Kapoor, Jim Piavis, Shital Shah, Fei Fang, Bistra Dilkina, Robert Hannaford, Arvind Iyer, Lucas Joppa, and Milind Tambe. 6/20/2018. “AirSim-W: A Simulation Environment for Wildlife Conservation with UAVs.” In 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.Abstract
Increases in poaching levels have led to the use of unmanned aerial vehicles (UAVs or drones) to count animals, locate animals in parks, and even find poachers. Finding poachers is often done at night through the use of long wave thermal infrared cameras mounted on these UAVs. Unfortunately, monitoring the live video stream from the conservation UAVs all night is an arduous task. In order to assist in this monitoring task, new techniques in computer vision have been developed. This work is based on a dataset which took approximately six months to label. However, further improvement in detection and future testing of autonomous flight require not only more labeled training data, but also an environment where algorithms can be safely tested. In order to meet both goals efficiently, we present AirSim-W, a simulation environment that has been designed specifically for the domain of wildlife conservation. This includes (i) creation of an African savanna environment in Unreal Engine, (ii) integration of a new thermal infrared model based on radiometry, (iii) API code expansions to follow objects of interest or fly in zig-zag patterns to generate simulated training data, and (iv) demonstrated detection improvement using simulated data generated by AirSim-W. With these additional simulation features, AirSim-W will be directly useful for wildlife conservation research.
2018_15_teamcore_bondi_camera_ready_airsim_w.pdf
Shahrzad Gholami, Sara Mc Carthy, Bistra Dilkina, Andrew Plumptre, Milind Tambe, Margaret Driciru, Fred Wanyama, and Aggrey Rwetsiba. 2018. “Adversary models account for imperfect crime data: Forecasting and planning against real-world poachers (Corrected Version).” In International Conference on Autonomous Agents and Multi-agent Systems (AAMAS 2018).Abstract
Poachers are engaged in extinction level wholesale slaughter, so it is critical to harness historical data for predicting poachers’ behavior. However, in these domains, data collected about adversarial actions are remarkably imperfect, where reported negative instances of crime may be mislabeled or uncertain. Unfortunately, past attempts to develop predictive and prescriptive models to address this problem suffer from shortcomings from a modeling perspective as well as in the implementability of their techniques. Most notably these models i) neglect the uncertainty in crime data, leading to inaccurate and biased predictions of adversary behavior, ii) use coarse-grained crime analysis and iii) do not provide a convincing evaluation as they only look at a single protected area. Additionally, they iv) proposed time-consuming techniques which cannot be directly integrated into low resource outposts. In this innovative application paper, we (I) 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 stateof-the-art. We also demonstrate the country-wide efficiency of the models and are the first to (II) evaluate our adversary behavioral model across different protected areas in Uganda, i.e., Murchison Fall and Queen Elizabeth National Park, (totaling about 7500 km2) as well as (III) on fine-grained temporal resolutions. Lastly, (IV) 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.
2018_28_teamcore_sgholami_aamas18.pdf

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