@mastersthesis {1499549,
	title = {Predicting and Planning against Real-world Adversaries: An End-to-end Pipeline to Combat Illegal Wildlife Poachers on a Global Scale},
	year = {2019},
	abstract = {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 integratedand applied. For example, in the environmental sustainability domain, the problem of protectingendangered wildlife from attacks (i.e., poachers{\textquoteright} strikes) can be abstracted as a game betweendefender(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 inmy thesis to create computationally feasible and accurate algorithms in order to model complexadversarial 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 machinelearning and game theory framework for the fundamental challenges existing in the environmental sustainability domain, namely (i) novel spatio-temporal and uncertainty-aware machinelearning models for complex adversarial behavior based on the imperfect real-world data, (ii) thefirst large-scale field test evaluation of the machine learning models in the adversarial settingsconcerning the environmental sustainability, (iii) a novel multi-expert online learning model forconstrained patrol planning, and (iv) the first game theoretical model to generate optimal defenderstrategy against collusive adversaries.In regard to the first contribution, I developed bounded rationality models for adversariesbased on the real-world data that account for the naturally occurring uncertainty in past attackevidence 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 fieldtest 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, andpoachers were arrested, potentially more wildlife saved. The algorithm I proposed, that combinesmachine learning and game-theoretic patrol planning is planned to be deployed at 600 nationalparks 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 forconstrained and randomized patrol planning, which benefits from several expert planners whereinsufficient 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 collusiveadversaries in security games assuming both rational and boundedly rational adversaries. I conducted human subject experiments on Amazon Mechanical Turk involving 700 human subjectsusing a web-based game that simulates collusive security games.},
	author = {Gholami, Shahrzad}
}
