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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.

Chapter 10 PAWS: Game Theory Based Protection Assistant for Wildlife Security Fei Fang, Fei FangSearch for more papers by this authorBenjamin Ford, Benjamin FordSearch for more papers by this authorRong Yang, Rong YangSearch for more papers by this authorMilind Tambe, Milind TambeSearch for more papers by this authorAndrew M. Lemieux, Andrew M. LemieuxSearch for more papers by this author Fei Fang, Fei FangSearch for more papers by this authorBenjamin Ford, Benjamin FordSearch for more papers by this authorRong Yang, Rong YangSearch for more papers by this authorMilind Tambe, Milind TambeSearch for more papers by this authorAndrew M. Lemieux, Andrew M. LemieuxSearch for more papers by this author Book Editor(s):Meredith L. Gore, Meredith L. Gore Michigan State University, USASearch for more papers by this author First published: 05 May 2017 https://doi.org/10.1002/9781119376866.ch10Citations: 23 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onFacebookTwitterLinked InRedditWechat Summary This chapter introduces Protection Assistant for Wildlife Security (PAWS) as a joint effort by computer scientists, conservation researchers, and conservation practitioners from two nongovernmental organizations—Panthera, and Rimba. It also discusses the related work of PAWS and provides a detailed description of the wildlife poaching problem domain. The chapter then presents an overview of the PAWS system and explains how it works, in detail, with respect to game-theoretic analysis, human behavior modeling, and domain feature modeling. PAWS builds on concepts and models from game theory, in particular, security games and provides an automated approach that generates efficient and randomized patrol schedules. Research on security games focuses on overcoming the security and conservation agencies' challenge of limited law enforcement resources. In optimizing security resource allocation, previous work on Stackelberg Security Games (SSGs) has led to many successfully deployed applications to improve the security of airports, ports and flights. Citing Literature Conservation Criminology RelatedInformation

Motivated by the problem of protecting endangered animals, there has been a surge of interests in optimizing patrol planning for conservation area protection. Previous efforts in these domains have mostly focused on optimizing patrol routes against a specific boundedly rational poacher behavior model that describes poachers’ choices of areas to attack. However, these planning algorithms do not apply to other poaching prediction models, particularly, those complex machine learning models which are recently shown to provide better prediction than traditional bounded-rationality-based models. Moreover, previous patrol planning algorithms do not handle the important concern whereby poachers infer the patrol routes by partially monitoring the rangers’ movements. In this paper, we propose OPERA, a general patrol planning framework that: (1) generates optimal implementable patrolling routes against a black-box attacker which can represent a wide range of poaching prediction models; (2) incorporates entropy maximization to ensure that the generated routes are more unpredictable and robust to poachers’ partial monitoring. Our experiments on a real-world dataset from Uganda’s Queen Elizabeth Protected Area (QEPA) show that OPERA results in better defender utility, more efficient coverage of the area and more unpredictability than benchmark algorithms and the past routes used by rangers at QEPA.

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 elephants.

Leather is an integral part of the world economy and a substantial income source for developing countries. Despite government regulations on leather tannery waste emissions, inspection agencies lack adequate enforcement resources, and tanneries’ toxic wastewaters wreak havoc on surrounding ecosystems and communities. Previous works in this domain stop short of generating executable solutions for inspection agencies. We introduce NECTAR the first security game application to generate environmental compliance inspection schedules. NECTAR’s game model addresses many important real-world constraints: a lack of defender resources is alleviated via a secondary inspection type; imperfect inspectors are modeled via a heterogeneous failure rate; and uncertainty, in traveling through a road network and in conducting inspections, is addressed via a Markov Decision Process. Previously unexplored in security game literature, NECTAR features a novel explanation system to improve user understanding of inspection schedules; understandability is a critical component to build trust and facilitate user adoption. This explanation system generalizes to any security game type, and we demonstrate its application to NECTAR. To evaluate our model, we conduct a series of simulations and analyze their policy implications. We also conduct a preliminary survey to assess explanation systems’ potential impact on understandability.

Leather is an integral part of the world economy and a substantial income source for developing countries. Despite government regulations on leather tannery waste emissions, inspection agencies lack adequate enforcement resources, and tanneries' toxic wastewaters wreak havoc on surrounding ecosystems and communities. Previous works in this domain stop short of generating executable solutions for inspection agencies. We introduce NECTAR - the first security game application to generate environmental compliance inspection schedules. NECTAR's game model addresses many important real-world constraints: a lack of defender resources is alleviated via a secondary inspection type; imperfect inspections are modeled via a heterogeneous failure rate; and uncertainty, in traveling through a road network and in conducting inspections, is addressed via a Markov Decision Process. To evaluate our model, we conduct a series of simulations and analyze their policy implications.

Interdicting the flow of illegal goods (such as drugs and ivory) is a major security concern for many countries. The massive scale of these networks, however, forces defenders to make judicious use of their limited resources. While existing solutions model this problem as a Network Security Game (NSG), they do not consider humans' bounded rationality. Previous human behavior modeling works in Security Games, however, make use of large training datasets that are unrealistic in real-world situations; the ability to effectively test many models is constrained by the time-consuming and complex nature of field deployments. In addition, there is an implicit assumption in these works that a model's prediction accuracy strongly correlates with the performance of its corresponding defender strategy (referred to as predictive reliability). If the assumption of predictive reliability does not hold, then this could lead to substantial losses for the defender. In the following paper, we (1) first demonstrate that predictive reliability is indeed strong for previous Stackelberg Security Game experiments. We also run our own set of human subject experiments in such a way that models are restricted to learning on dataset sizes representative of real-world constraints. In the analysis on that data, we demonstrate that (2) predictive reliability is extremely weak for NSGs. Following that discovery, however, we identify (3) key factors that influence predictive reliability results: the training set's exposed attack surface and graph structure.

It is common knowledge that the pollution of India's rivers is a major environmental concern. For example, the Ganga is ranked the fifth dirtiest river in the world [1]. Generated from various sources such as untreated sewage and industrial effluents, the pollution inflicts serious health conditions on all life that depends on the river. In Kanpur, villagers suffer from conditions including cholera and miscarriages, and livestock yield less milk and, many times, die suddenly [2].

Conservation agencies around the world are tasked with protecting endangered wildlife from poaching. Despite their substantial efforts, however, species are continuing to be poached to critical status and, in some cases, extinction. In South Africa, rhino poaching has seen a recent escalation in frequency; while only 122 rhinos were poached in 2009, a record 1215 rhinos were poached in 2014 (approximately 1 rhino every eight hours)[the Rhino International, 2015]. To combat poaching, conservation agencies send well-trained rangers to patrol designated protected areas. However, these agencies have limited resources and are unable to provide 100% coverage to the entire area at all times. Thus, it is important that agencies make the most efficient use of their patrolling resources, and we introduce Green Security Games (GSGs) as a tool to aid agencies in designing effective patrols. First introduced by [Von Stengel and Zamir, 2004] as a Leadership Game, Stackelberg Games have been applied in a variety of Security Game research (i.e., Stackelberg Security Games, or SSGs). In particular, the focus on randomization in Stackelberg Games lends itself to solving real-world security problems where defenders have limited resources, such as randomly allocating Federal Air Marshals to international flights [Tsai et al., 2009]. However, the SSG model focuses on generating an optimal defender strategy against a single defender-attacker interaction (e.g., a single terrorist attack). For domains where attacks occur frequently, such as in wildlife conservation, another type of Security Game is needed that effectively models the repeated interactions between the defender and the attacker. While still following the Leader-Follower paradigm of SSGs, GSGs have been developed as a way of applying Game Theory to assist wildlife conservation efforts, whether its to prevent illegal fishing [Haskell et al., 2014], illegal logging [Johnson et al., 2012], or wildlife poaching [Yang et al., 2014]. GSGs are similar to SSGs except that, in GSGs, the game takes place over N rounds. In SSGs, once the attacker makes a decision, the game is over, but in GSGs, the attacker (e.g., the poacher) and defender have multiple rounds in which they can adapt to each other’s choices in previous rounds. This multi-round feature of GSGs introduces some key research challenges that are being studied: (1) how can we incorporate the attacker’s previous choices into our model of their behavior, in order to improve the defender’s strategy, [Yang et al., 2014; Kar et al., 2015] and (2) how do we choose a strategy such that the long-term payoff (i.e., cumulative expected utility) is maximized [Fang et al., 2015]? In addition to exploring these open research questions, we also discuss field tests of the Protection Assistant for Wildlife Security (PAWS) software in Uganda and Malaysia.

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 lever-

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.

With the significant increase of available item listings in popular online auction houses nowadays, it becomes nearly impossible to manually investigate the large amount of auctions and bidders for shill bidding activities, which are a major type of auction fraud in online auctions. Automated mechanisms such as data mining techniques were proved to be necessary to process this type of increasing workload. In this paper, we first present a framework of Real-Time Self-Adaptive Classifier (RT-SAC) for identifying suspicious bidders in online auctions using an incremental neural network approach. Then, we introduce a clustering module that characterizes bidder behaviors in measurable attributes and uses a hierarchical clustering mechanism to create training datasets. The neural network in RT-SAC is initialized with the training datasets, which consist of labeled historical auction data. Once initialized, the network can be trained incrementally to gradually adapt to new bidding data in real time, and thus, it supports efficient detection of suspicious bidders in online auctions. Finally, we utilize a case study to demonstrate how parameters in RT-SAC can be tuned for optimal operations and how our approach can be used to effectively identify suspicious online bidders in real time.

Identifying bidders with suspicious bidding activities related to possible online auction fraud is a difficult task due to a large number of users participating in online auctions. In order to reduce the number of users to be investigated, we examine observable features of a bidder’s behavior, and utilize a hierarchical clustering technique to divide a collection of bidders into normal and deviant groups. Based on the clustering results, we generate a decision tree that can be used to efficiently characterize new bidders as normal, suspicious, or highly suspicious. To illustrate the effectiveness of our proposed approach, we collected real auction datasets from online auctions, and used 3-fold validation approach to show that the error rates of the generated decision trees are reasonably low.

In an agent-based online auction system, a bidding agent can automatically place bids on behalf of a human user according to a user-specified bidding strategy. Current implementations of bidding agents only support a set of simple predefined bidding strategies. In this paper, we introduce a formal bidding strategy model that supports specification of complex bidding strategies for autonomous bidding agents. The formal model is defined as a layered bidding strategy model (LBSM), which can be represented using notations adapted from UML activity diagrams. For real-time and efficient reasoning, the formal model is converted into a rule-based bidding strategy model (RBSM) represented in bidding strategy language (BSL), which can be directly executed by a reasoning module of an autonomous bidding agent. We present an algorithm for converting an LBSM to a rule-based bidding strategy model, and an algorithm to drive the reasoning engine. Finally, we develop a prototype agent-based online auction system using JADE, and demonstrate how layered bidding strategies can be precisely specified, and how our approach may support analysis of impacts on bidding histories by using different bidding strategies in agent-based online auctions.

Current implementations of agent-based online auction systems only support simple predefined bidding strategies for bidding agents. In this paper, we introduce a formal bidding strategy model for specification of flexible and complex bidding strategies. The formal model is defined as a layered bidding strategy model (LBSM), which can be represented using notations borrowed from UML activity diagrams. To support real-time and efficient reasoning, the formal model is converted into a rule-based bidding strategy model (RBSM) specified in bidding strategy language (BSL) that can be directly executed by a reasoning module of a bidding agent. We present an algorithm for converting an LBSM to an RBSM, and an algorithm to drive the reasoning engine. Finally, we develop a prototype agent-based online auction system using JADE, and illustrate how flexible and complex bidding strategies can be precisely specified and efficiently executed.