Conservation

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.

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.

In Stackelberg security games, a defender seeks to randomly allocate limited security resources to protect critical targets from an attack. In this paper, we study a fundamental, yet underexplored, phenomenon in security games, which we term the Curse of Correlation (CoC). Specifically, we observe that there are inevitable correlations among the protection status of different targets. Such correlation is a crucial concern, especially in spatio-temporal domains like conservation area patrolling, where attackers can surveil patrollers at certain areas and then infer their patrolling routes using such correlations. To mitigate this issue, we propose to design entropy-maximizing defending strategies for spatio-temporal security games, which frequently suffer from CoC. We prove that the problem is #P-hard in general. However, it admits efficient algorithms in well-motivated special settings.

The unrelenting threat of poaching has led to increased development of new technologies to combat it. One such example is the use of long wave thermal infrared cameras mounted on unmanned aerial vehicles (UAVs or drones) to spot poachers at night and report them to park rangers before they are able to harm animals. However, monitoring the live video stream from these conservation UAVs all night is an arduous task. Therefore, we build SPOT (Systematic POacher deTector), a novel application that augments conservation drones with the ability to automatically detect poachers and animals in near real time. SPOT illustrates the feasibility of building upon state-of-the-art AI techniques, such as Faster RCNN, to address the challenges of automatically detecting animals and poachers in infrared images. This paper reports (i) the design and architecture of SPOT, (ii) a series of efforts towards more robust and faster processing to make SPOT usable in the field and provide detections in near real time, and (iii) evaluation of SPOT based on both historical videos and a real-world test run by the end users in the field. The promising results from the test in the field have led to a plan for larger-scale deployment in a national park in Botswana. While SPOT is developed for conservation drones, its design and novel techniques have wider application for automated detection from UAV videos.

Wildlife conservation organizations task rangers to deter and capture wildlife poachers. Since rangers are responsible for patrolling vast areas, adversary behavior modeling can help more effectively direct future patrols. In this innovative application track paper, we present an adversary behavior modeling system, INTERCEPT (INTERpretable Classification Ensemble to Protect Threatened species), and provide the most extensive evaluation in the AI literature of one of the largest poaching datasets from Queen Elizabeth National Park (QENP) in Uganda, comparing INTERCEPT with its competitors; we also present results from a month-long test of INTERCEPT in the field. We present three major contributions. First, we present a paradigm shift in modeling and forecasting wildlife poacher behavior. Some of the latest work in the AI literature (and in Conservation) has relied on models similar to the Quantal Response model from Behavioral Game Theory for poacher behavior prediction. In contrast, INTERCEPT presents a behavior model based on an ensemble of decision trees (i) that more effectively predicts poacher attacks and (ii) that is more effectively interpretable and verifiable. We augment this model to account for spatial correlations and construct an ensemble of the best models, significantly improving performance. Second, we conduct an extensive evaluation on the QENP dataset, comparing 41 models in prediction performance over two years. Third, we present the results of deploying INTERCEPT for a one-month field test in QENP - a first for adversary behavior modeling applications in this domain. This field test has led to finding a poached elephant and more than a dozen snares (including a roll of elephant snares) before they were deployed, potentially saving the lives of multiple animals - including endangered elephants.

Despite significant research in Security Games, limited efforts have been made to handle game domains with continuous space. Addressing such limitations, in this paper we propose: (i) a continuous space security game model that considers infinitesize action spaces for players; (ii) OptGradFP, a novel and general algorithm that searches for the optimal defender strategy in a parametrized search space; (iii) OptGradFP-NN, a convolutional neural network based implementation of OptGradFP for continuous space security games; (iv) experiments and analysis with OptGradFP-NN. This is the first time that neural networks have been used for security games, and it shows the promise of applying deep learning to complex security games which previous approaches fail to handle.

Poaching is considered a major driver for the population drop of key species such as tigers, elephants, and rhinos, which can be detrimental to whole ecosystems. While conducting foot patrols is the most commonly used approach in many countries to prevent poaching, such patrols often do not make the best use of the limited patrolling resources. This paper presents PAWS, a game-theoretic application deployed in Southeast Asia for optimizing foot patrols to combat poaching. In this paper, we report on the significant evolution of PAWS from a proposed decision aid introduced in 2014 to a regularly deployed application. We outline key technical advances that lead to PAWS’s regular deployment: (i) incorporating complex topographic features, e.g., ridgelines, in generating patrol routes; (ii) handling uncertainties in species distribution (game theoretic payoffs); (iii) ensuring scalability for patrolling large-scale conservation areas with fine-grained guidance; and (iv) handling complex patrol scheduling constraints.

Conservation agencies worldwide must make the most efficient use of their limited resources to protect natural resources from over-harvesting and animals from poaching. Predictive modeling, a tool to increase efficiency, is seeing increased usage in conservation domains such as to protect wildlife from poaching. Many works in this wildlife protection domain, however, fail to train their models on real-world data or test their models in the real world. My thesis proposes novel poacher behavior models that are trained on real-world data and are tested via first-of-their-kind tests in the real world. First, I proposed a paradigm shift in traditional adversary behavior modeling techniques from Quantal Response-based models to decision tree-based models. Based on this shift, I proposed an ensemble of spatially-aware decision trees, INTERCEPT, that outperformed the prior stateof-the-art and then also presented results from a one-month pilot field test of the ensemble’s predictions in Uganda’s Queen Elizabeth Protected Area (QEPA). This field test represented the first time that a machine learning-based poacher behavior modeling application was tested in the field. Second, I proposed a hybrid spatio-temporal model that led to further performance improvements. To validate this model, I designed and conducted a large-scale, eight-month field test of this model’s predictions in QEPA. This field test, where rangers patrolled over 450 km in the largest and longest field test of a machine learning-based poacher behavior model to date in this domain, successfully demonstrated the selectiveness of the model’s predictions; the model successfully predicted, with statistical significance, where rangers would find more snaring activity and also where rangers would not find as much snaring activity. I also conducted detailed analysis of the behavior of my predictive model. Third, beyond wildlife poaching, I also provided novel graph-aware models for modeling human adversary behavior in wildlife or other contraband smuggling networks and tested them against human subjects. Lastly, I examined human considerations of deployment in new domains and the importance of easily-interpretable models and results. While such interpretability has been a recurring theme in all my thesis work, I also created a game-theoretic inspection strategy application that generated randomized factory inspection schedules and also contained visualization and explanation components for users.

Worldwide, conservation agencies employ rangers to protect conservation areas from poachers. However, agencies lack the manpower to have rangers effectively patrol these vast areas frequently. While past work has modeled poachers’ behavior so as to aid rangers in planning future patrols, those models’ predictions were not validated by extensive field tests. In this paper, we present a hybrid spatio-temporal model that predicts poaching threat levels and results from a five-month field test of our model in Uganda’s Queen Elizabeth Protected Area (QEPA). To our knowledge, this is the first time that a predictive model has been evaluated through such an extensive field test in this domain. We present two major contributions. First, our hybrid model consists of two components: (i) an ensemble model which can work with the limited data common to this domain and (ii) a spatio-temporal model to boost the ensemble’s predictions when sufficient data are available. When evaluated on real-world historical data from QEPA, our hybrid model achieves significantly better performance than previous approaches with either temporally-aware dynamic Bayesian networks or an ensemble of spatially-aware models. Second, in collaboration with the Wildlife Conservation Society and Uganda Wildlife Authority, we present results from a five-month controlled experiment where rangers patrolled over 450 sq km across QEPA. We demonstrate that our model successfully predicted (1) where snaring activity would occur and (2) where it would not occur; in areas where we predicted a high rate of snaring activity, rangers found more snares and snared animals than in areas of lower predicted activity. These findings demonstrate that (1) our model’s predictions are selective, (2) our model’s superior laboratory performance extends to the real world, and (3) these predictive models can aid rangers in focusing their efforts to prevent wildlife poaching and save animals.

Advances in computational game theory have led to several successfully deployed applications in security domains. These gametheoretic approaches and security applications learn game payoff values or adversary behaviors from annotated input data provided by domain experts and practitioners in the field, or collected through experiments with human subjects. Beyond these traditional methods, unmanned aerial vehicles (UAVs) have become an important surveillance tool used in security domains to collect the required annotated data. However, collecting annotated data from videos taken by UAVs efficiently, and using these data to build datasets that can be used for learning payoffs or adversary behaviors in game-theoretic approaches and security applications, is an under-explored research question. This paper presents VIOLA, a novel labeling application that includes (i) a workload distribution framework to efficiently gather human labels from videos in a secured manner; (ii) a software interface with features designed for labeling videos taken by UAVs in the domain of wildlife security. We also present the evolution of VIOLA and analyze how the changes made in the development process relate to the efficiency of labeling, including when seemingly obvious improvements surprisingly did not lead to increased efficiency. VIOLA enables collecting massive amounts of data with detailed information from challenging security videos such as those collected aboard UAVs for wildlife security. VIOLA will lead to the development of a new generation of game-theoretic approaches for security domains, including approaches that integrate deep learning and game theory for real-time detection and response.

Whereas previous real-world game-theoretic applications in security focused on protection of critical infrastructure in the absence of past attack data, more recent work has focused on datadriven security and sustainability applications for protecting the environment, including forests, fish and wildlife. One key challenge in such “Green Security Game” (GSG) domains is to model the adversary’s decision making process based on available attack data. This thesis, for the first time, explores the suitability of different adversary behavior modeling approaches in such domains that differ in the type and amount of historical data available. The first contribution is to provide a detailed comparative study, based on actual human subject experiments, of competing adversary behavior models in domains where attack data is available in plenty (e.g., via a large number of sensors). This thesis demonstrates a new human behavior model, SHARP, which mitigates the limitations of previous models in three key ways. First, SHARP reasons based on successes or failures of the adversary’s past actions to model adversary adaptivity. Second, SHARP reasons about similarity between exposed and unexposed areas of the attack surface to handle the adversary’s lack of exposure to enough of the attack surface. Finally, SHARP integrates a non-linear probability weighting function to capture the adversary’s true weighting of probabilities.The second contribution relates to domains requiring predictions over a large set of targets by learning from limited (and in some cases, noisy) data. One example dataset on which we demonstrate our approaches to handle such challenges is a real-world poaching dataset collected over a large geographical area at the Queen Elizabeth National Park in Uganda. This data is too sparse to construct a detailed model. The second contribution of this thesis delivers a surprising result by presenting an adversary behavior modeling system, INTERCEPT, which is based on an ensemble of decision trees (i) that effectively learns and predicts poacher attacks based on limited noisy attack data over a large set of targets, and (ii) has fast execution speed. This has led to a successful month-long test of INTERCEPT in the field, a first for adversary behavior modeling applications in the wildlife conservation domain. Finally, for the my third contribution, we examine one common assumption in adversary behavior modeling that the adversary perfectly observes the defender’s randomized protection strategy. However, in domains such as wildlife conservation, the adversary only observes a limited sequence of defender patrols and forms beliefs about the defender’s strategy. In the absence of a comparative analysis and a principled study of the strengths and weaknesses of belief models, no informed decision could be made to incorporate belief models in adversary behavior models such as SHARP and INTERCEPT. This thesis provides the first-of-its-kind systematic comparison of existing and new proposed belief models and demonstrates based on human subjects experiments data that identifying heterogeneous belief update behavior is essential in making effective predictions. We also propose and evaluate customized models for settings that differ in the type of belief data available and quantify the value of having such historical data on the accuracy of belief prediction.

Wildlife poaching presents a serious extinction threat to many animal species. Agencies (“defenders”) focused on protecting such animals need tools that help analyze, model and predict poacher activities, so they can more effectively combat such poaching; such tools could also assist in planning effective defender patrols, building on the previous security games research. To that end, we have built a new predictive anti-poaching tool, CAPTURE (Comprehensive Anti-Poaching tool with Temporal and observation Uncertainty REasoning). CAPTURE provides four main contributions. First, CAPTURE’s modeling of poachers provides significant advances over previous models from behavioral game theory and conservation biology. This accounts for: (i) the defender’s imperfect detection of poaching signs; (ii) complex temporal dependencies in the poacher’s behaviors; (iii) lack of knowledge of numbers of poachers. Second, we provide two new heuristics: parameter separation and target abstraction to reduce the computational complexity in learning the poacher models. Third, we present a new game-theoretic algorithm for computing the defender’s optimal patrolling given the complex poacher model. Finally, we present detailed models and analysis of realworld poaching data collected over 12 years in Queen Elizabeth National Park in Uganda to evaluate our new model’s prediction accuracy. This paper thus presents the largest dataset of real-world defender-adversary interactions analyzed in the security games literature. CAPTURE will be tested in Uganda in early 2016.

Poaching is a serious threat to the conservation of key species and whole ecosystems. While conducting foot patrols is the most commonly used approach in many countries to prevent poaching, such patrols often do not make the best use of limited patrolling resources. To remedy this situation, prior work introduced a novel emerging application called PAWS (Protection Assistant for Wildlife Security); PAWS was proposed as a game-theoretic (“security games”) decision aid to optimize the use of patrolling resources. This paper reports on PAWS’s significant evolution from a proposed decision aid to a regularly deployed application, reporting on the lessons from the first tests in Africa in Spring 2014, through its continued evolution since then, to current regular use in Southeast Asia and plans for future worldwide deployment. In this process, we have worked closely with two NGOs (Panthera and Rimba) and incorporated extensive feedback from professional patrolling teams. We outline key technical advances that lead to PAWS’s regular deployment: (i) incorporating complex topographic features, e.g., ridgelines, in generating patrol routes; (ii) handling uncertainties in species distribution (game theoretic payoffs); (iii) ensuring scalability for patrolling large-scale conservation areas with fine-grained guidance; and (iv) handling complex patrol scheduling constraints.

While human behavior models based on repeated Stackelberg games have been proposed for domains such as “wildlife crime” where there is repeated interaction between the defender and the adversary, there has been no empirical study with human subjects to show the effectiveness of such models. This paper presents an initial study based on extensive human subject experiments with participants on Amazon Mechanical Turk (AMT). Our findings include: (i) attackers may view the defender’s coverage probability in a non-linear fashion; specifically it follows an S-shaped curve, and (ii) there are significant losses in defender utility when strategies generated by existing models are deployed in repeated Stackelberg game settings against human subjects.

Several competing human behavior models have been proposed to model and protect against boundedly rational adversaries in repeated Stackelberg security games (SSGs). However, these existing models fail to address three main issues which are extremely detrimental to defender performance. First, while they attempt to learn adversary behavior models from adversaries’ past actions (“attacks on targets”), they fail to take into account adversaries’ future adaptation based on successes or failures of these past actions. Second, they assume that sufficient data in the initial rounds will lead to a reliable model of the adversary. However, our analysis reveals that the issue is not the amount of data, but that there just is not enough of the attack surface exposed to the adversary to learn a reliable model. Third, current leading approaches have failed to include probability weighting functions, even though it is well known that human beings’ weighting of probability is typically nonlinear. The first contribution of this paper is a new human behavior model, SHARP, which mitigates these three limitations as follows: (i) SHARP reasons based on success or failure of the adversary’s past actions on exposed portions of the attack surface to model adversary adaptiveness; (ii) SHARP reasons about similarity between exposed and unexposed areas of the attack surface, and also incorporates a discounting parameter to mitigate adversary’s lack of exposure to enough of the attack surface; and (iii) SHARP integrates a non-linear probability weighting function to capture the adversary’s true weighting of probability. Our second contribution is a first “longitudinal study” – at least in the context of SSGs – of competing models in settings involving repeated interaction between the attacker and the defender. This study, where each experiment lasted a period of multiple weeks with individual sets of human subjects, illustrates the strengths and weaknesses of different models and shows the advantages of SHARP.

Recent research on Green Security Games (GSG), i.e., security games for the protection of wildlife, forest and fisheries, relies on the promise of an abundance of available data in these domains to learn adversary behavioral models and determine game payoffs. This research suggests that adversary behavior models (capturing bounded rationality) can be learned from real-world data on where adversaries have attacked, and that game payoffs can be determined precisely from data on animal densities. However, previous work has, as yet, failed to demonstrate the usefulness of these behavioral models in capturing adversary behaviors based on real-world data in GSGs. Previous work has also been unable to address situations where available data is insufficient to accurately estimate behavioral models or to obtain the required precision in the payoff values. In addressing these limitations, as our first contribution, this paper, for the first time, provides validation of the aforementioned adversary behavioral models based on real-world data from a wildlife park in Uganda. Our second contribution addresses situations where real-world data is not precise enough to determine exact payoffs in GSG, by providing the first algorithm to handle payoff uncertainty in the presence of adversary behavioral models. This algorithm is based on the notion of minimax regret. Furthermore, in scenarios where the data is not even sufficient to learn adversary behaviors, our third contribution is to provide a novel algorithm to address payoff uncertainty assuming a perfectly rational attacker (instead of relying on a behavioral model); this algorithm allows for a significant scaleup for large security games. Finally, to reduce the problems due to paucity of data, given mobile sensors such as Unmanned Aerial Vehicles (UAV), we introduce new payoff elicitation strategies to strategically reduce uncertainty.

Building on the successful applications of Stackelberg Security Games (SSGs) to protect infrastructure, researchers have begun focusing on applying game theory to green security domains such as protection of endangered animals and fish stocks. Previous efforts in these domains optimize defender strategies based on the standard Stackelberg assumption that the adversaries become fully aware of the defender’s strategy before taking action. Unfortunately, this assumption is inappropriate since adversaries in green security domains often lack the resources to fully track the defender strategy. This paper (i) introduces Green Security Games (GSGs), a novel game model for green security domains with a generalized Stackelberg assumption; (ii) provides algorithms to plan effective sequential defender strategies — such planning was absent in previous work; (iii) proposes a novel approach to learn adversary models that further improves defender performance; and (iv) provides detailed experimental analysis of proposed approaches.

Endangered species around the world are in danger of extinction from poaching. From the start of the 20th century, the African rhino population has dropped over 98% [4] and the global tiger population has dropped over 95% [5], resulting in multiple species extinctions in both groups. Species extinctions have negative consequences on local ecosystems, economies, and communities. To protect these species, countries have set up conservation agencies and national parks, such as Uganda’s Queen Elizabeth National Park (QENP). However, a common lack of funding for these agencies results in a lack of law enforcement resources to protect these large, rural areas. As an example of the scale of disparity, one wildlife crime study in 2007 reported an actual coverage density of one ranger per 167 square kilometers [2]. Because of the hazards involved (e.g., armed poachers, wild animals), rangers patrol in groups, further increasing the amount of area they are responsible for patrolling. Security game research has typically been concerned with combating terrorism, and this field has indeed benefited from a range of successfully deployed applications [1, 6]. These applications have enabled security agencies to make more efficient use of their limited resources. In this previous research, adversary data has been absent during the development of these solutions, and thus, it has been difficult to make accurate adversary behavior models during algorithm development. In a domain such as wildlife crime, interactions with the adversary are frequent and repeated, thus enabling conservation agencies to collect data. This presence of data enables security game researchers to begin developing algorithms that incorporate this data into, potentially, more accurate behavior models and consequently better security solutions. Developed in conjunction with staff at QENP, the Protection Assistant for Wildlife Security (PAWS) generates optimized defender strategies for use by park rangers [7]. Due to the repeated nature of wildlife crime, PAWS is able to leverage crime event data - a previously unrealized capability in security games research. Thus, PAWS implements a novel adaptive algorithm that processes crime event data, builds multiple human behavior models, and, based on those models, predicts where adversaries will attack next. These predictions are then used to generate a patrol strategy for the rangers (i.e., a set of patrol waypoints) that can be viewed on a GPS unit. Against this background, the demonstration presented in this paper introduces two contributions. First, we present the PAWS system which incorporates the algorithm in [7] into a scheduling system and a GPS visualizer. Second, we present a software interface to run a number of human subject experiments (HSE) to evaluate and improve the efficacy of PAWS before its deployment in QENP. By conducting these HSEs, we can (i) test the PAWS algorithms with repeated interactions with humans, thus providing a more realistic testing environment than in its previous simulations; (ii) generate data that can be used to initialize PAWS’s human behavior models for deployment, and (iii) compare the current PAWS algorithms’ performance to alternatives and determine if additional improvements are needed prior to deployment. To provide proper context for the presentation, this paper also presents a brief overview of the PAWS system data flow and its adaptive algorithms. The demonstration will engage audience members by having them participate in the HSEs and using the GPS unit to visualize a patrol schedule in QENP.

Illegal poaching is an international problem that leads to the extinction of species and the destruction of ecosystems. As evidenced by dangerously dwindling populations of endangered species, existing anti-poaching mechanisms are insufficient. This paper introduces the Protection Assistant for Wildlife Security (PAWS) application - 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. In addressing these challenges, our first contribution is 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.