Nguyen, ThanhKinsey, Sarah2025-02-242025-02-242025-02-24https://hdl.handle.net/1794/30454As big data and computing capability continue to grow, an ever-increasingamount of artificial intelligence approaches are being deployed in the real world, across various domains. With real world deployments come additional complexities, challenges, and vulnerabilities, particularly concerning data reliance. Among others, these data related vulnerabilities include the potential for intelligently sabotaged data, and incomplete data. This work explores vulnerabilities from three perspectives: adversarial learning, game theory, and online reinforcement learning. First, we investigate whether a directly targeted end-to-end poisoning attack on a data-based decision making learning-planning model is feasible. Next, through the lens of security games, we investigate how a data-based decision maker can form a useful behavioral model, despite the observable data being maliciously manipulated. Lastly, we examine how, in a real world online RL setting, limited and sparse data can be overcome to build an effective data-based decision making model.en-USAll Rights Reserved.Adversarial LearningArtificial IntelligenceGame TheoryElectronic Thesis or Dissertation