Predicting and planning against real-world adversaries: an end-to-end pipeline to combat illegal wildlife poachers on a global scale

Security is a global concern and a unifying theme in various security projects is strategic reasoning where the mathematical framework of machine learning and game theory can be integrated and applied. For example, in the environmental sustainability domain, the problem of protecting endangered wildlife from attacks (i.e., poachers’ strikes) can be abstracted as a game between defender(s) and attacker(s). Applying previous research on security games to sustainability domains (denoted as Green Security Games) introduce several novel challenges that I address in my thesis to create computationally feasible and accurate algorithms in order to model complex adversarial behavior based on real-world data and to generate optimal defender strategy. My thesis provides four main contributions to the emerging body of research in using machine learning and game theory framework for the fundamental challenges existing in the environmental sustainability domain, namely (i) novel spatio-temporal and uncertainty-aware machine learning models for complex adversarial behavior based on the imperfect real-world data, (ii) the first large-scale field test evaluation of the machine learning models in the adversarial settings concerning the environmental sustainability, (iii) a novel multi-expert online learning model for constrained patrol planning, and (iv) the first game theoretical model to generate optimal defender strategy against collusive adversaries. In regard to the first contribution, I developed bounded rationality models for adversaries based on the real-world data that account for the naturally occurring uncertainty in past attack evidence collected by defenders. To that end, I proposed two novel predictive behavioral models, which I improved progressively. The second major contribution of my thesis is a large-scale field test evaluation of the proposed adversarial behavior model beyond the laboratory. Particularly, my thesis is motivated by the challenges in wildlife poaching, where I directed the defenders (i.e., rangers) to the hotspots of adversaries that they would have missed. During these experiments across multiple vast national parks, several snares and snared animals were detected, and poachers were arrested, potentially more wildlife saved. The algorithm I proposed, that combines machine learning and game-theoretic patrol planning is planned to be deployed at 600 national parks around the world in the near future to combat illegal poaching. The third contribution in my thesis introduces a novel multi-expert online learning model for constrained and randomized patrol planning, which benefits from several expert planners where insufficient or imperfect historical records of past attacks are available to learn adversarial behavior. The final contribution of my thesis is developing an optimal solution against collusive adversaries in security games assuming both rational and boundedly rational adversaries. I conducted human subject experiments on Amazon Mechanical Turk involving 700 human subjects using a web-based game that simulates collusive security games.

安全问题是全球共同关切的议题，而各类安全项目的核心统一主题均为战略推理——即可整合并应用机器学习与博弈论的数学框架开展研究的领域。例如，在环境可持续发展领域，保护濒危野生动物免受袭击（即偷猎者的攻击）的问题可被抽象为防御方与攻击方之间的博弈。将过往安全博弈研究应用于可持续发展领域（即绿色安全博弈（Green Security Games））会带来若干全新挑战，本论文针对这些挑战展开研究，旨在构建基于真实世界数据、可计算且精准的算法，以对复杂对抗行为进行建模，并生成最优防御策略。本论文针对环境可持续发展领域现存的核心挑战，在机器学习与博弈论框架的应用研究这一新兴方向上作出四项主要贡献：(i) 针对基于不完备真实世界数据的复杂对抗行为，提出新型时空感知与不确定性感知机器学习模型；(ii) 首次针对环境可持续相关对抗场景中的机器学习模型开展大规模实地测试评估；(iii) 针对受限巡逻规划问题，提出新型多专家在线学习模型；(iv) 首次提出可针对合谋型对抗方生成最优防御策略的博弈论模型。针对第一项贡献，本研究基于防御方收集的过往袭击证据中存在的固有不确定性，结合真实世界数据构建了对抗方的有限理性模型。为此，本文提出两种新型预测行为模型，并对其进行了逐步优化。本论文的第二项主要贡献，是在实验室场景之外，对所提出的对抗行为模型开展大规模实地测试评估。具体而言，本研究的研究动机源于野生动物偷猎面临的诸多挑战：研究中，我们将防御方（即护林员）引导至其原本可能遗漏的对抗方活动热点区域。在横跨多个大型国家公园的实验期间，研究团队共查获多处陷阱与被困动物，并抓获了偷猎者，潜在挽救了更多野生动物。本文提出的融合机器学习与博弈论巡逻规划的算法，计划于近期在全球600座国家公园部署，以打击非法偷猎行为。本论文的第三项贡献，针对受限与随机化巡逻规划问题，提出了新型多专家在线学习模型：该模型可利用若干专家规划器，在过往袭击历史记录不足或不完备的场景下学习对抗行为。本论文的最后一项贡献，是在假设对抗方兼具理性与有限理性的前提下，构建了安全博弈中针对合谋型对抗方的最优解决方案。本研究基于模拟合谋型安全博弈的网页游戏，在亚马逊机械 Turk（Amazon Mechanical Turk）平台开展了涉及700名受试者的人类行为实验。