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Fang, Fei

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Fang, Fei

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    Cloudy with a Chance of Poaching: Adversary Behavior Modeling and Forecasting with Real-World Poaching Data
    (ACM, 2017) Kar, Debarun; Ford, Benjamin; Gholami, Shahrzad; Fang, Fei; Plumptre, Andrew; Tambe, Milind; Driciru, Margaret; Wanyama, Fred; Rwetsiba, Aggrey; Nsubaga, Mustapha; Mabonga, Joshua
    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.
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    Optimizing Peer Teaching to Enhance Team Performance
    (2017) Shi, Zheyuan; Fang, Fei
    Collaboration among human agents with different expertise and capabilities is becoming increasingly pervasive and important for developing new products, providing patient-centered health care, propelling scientific advance, and solving social issues. When the roles of the agents in such collaborative teamwork are highly interdependent, the performance of the team will rely not only on each team member's individual capabilities but also on their shared understanding and mutual support. Without any understanding in other team members' area of expertise, the team members may not be able to work together efficiently due to the high cost of communication and the individual decisions made by different team members may even lead to undesirable results for the team. To improve collaboration and the overall performance of the team, the team members can teach each other and learn from each other, and such peer-teaching practice has shown to have great benefit in various domains such as interdisciplinary research collaboration and collaborative health care. However, the amount of time and effort the team members can spend on peer-teaching is often limited. In this paper, we focus on finding the best peer teaching plan to optimize the performance of the team, given the limited teaching and learning capacity. We (i) provide a formal model of the Peer Teaching problem; (ii) present hardness results for the problem in the general setting, and the subclasses of problems with additive utility functions and submodular utility functions; (iii) propose a polynomial time exact algorithm for problems with additive utility function, as well as a polynomial time approximation algorithm for problems with submodular utility functions.