Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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Description
Recent advances in Artificial Intelligence (AI) have brought AI closer to laypeople than ever before. This leads to a pervasive problem: how would a user ascertain whether an AI system will be safe, reliable, or useful in a given situation? This problem becomes particularly challenging when it is considered that most autonomous systems are not designed by their users; the internal software of these systems may be unavailable or difficult to understand; and the functionality of these systems may even change from initial specifications as a result of learning. To overcome these challenges, this dissertation proposes a paradigm for third-party autonomous assessment of black-box taskable AI systems. The four main desiderata of such assessment systems are: (i) interpretability: generating a description of the AI system's functionality in a language that the target user can understand; (ii) correctness: ensuring that the description of AI system's working is accurate; (iii) generalizability creating a solution approach that works well for different types of AI systems; and (iv) minimal requirements: creating an assessment system that does not place complex requirements on AI systems to support the third-party assessment, otherwise the manufacturers of AI system's might not support such an assessment. To satisfy these properties, this dissertation presents algorithms and requirements that would enable user-aligned autonomous assessment that helps the user understand the limits of a black-box AI system's safe operability. This dissertation proposes a personalized AI assessment module that discovers the high-level ``capabilities'' of an AI system with arbitrary internal planning algorithms/policies and learns an accurate symbolic description of these capabilities in terms of concepts that a user understands. Furthermore, the dissertation includes the associated theoretical results and the empirical evaluations. The results show that (i) a primitive query-response interface can enable the development of autonomous assessment modules that can derive a causally accurate user-interpretable model of the system's capabilities efficiently, and (ii) such descriptions are easier to understand and reason with for the users than the agent's primitive actions.
ContributorsVerma, Pulkit (Author) / Srivastava, Siddharth (Thesis advisor) / Cooke, Nancy (Committee member) / Fainekos, Georgios (Committee member) / Zhang, Yu (Committee member) / Arizona State University (Publisher)
Created2024
Description
The present study sought to understand traumatic brain injuries (TBI) impact on executive function (EF) in terms of anticipation amongst individuals with a background in soccer; along with other contributing factors of EF curtailments that inhibit athletes. Within this study 57 participants, with a background in soccer (high school, collegiate, and semi-professional), completed five EF tasks: working memory, cognitive flexibility, attentional control, and anticipation; pattern detection and athletic cues (temporal occlusion). The results of this study concluded that when TBI history, gender, and soccer athletic level are factors, athletes with a soccer level of collegiate and semi-professional had decrements related to pattern detection anticipation; meaning athletes at higher levels had lower average scores on the Brixton Spatial Anticipation Test (BSAT). Additionally, female athletes showed more anticipation decrements related to athletic cues, especially those that are reliant on the initiation of judgment. Overall undiagnosed TBIs and limited understanding on how to approach rehabilitation to mitigate EF decrements, continue to impede individual autonomy amongst athletes. Keywords: Traumatic brain injury, executive function, anticipation, soccer, temporal occlusion, Brixton Spatial Anticipation Test (BSAT), collegiate, semi-professional, pattern detection, rehabilitation
ContributorsEzenyilimba, Akuadasuo (Author) / Gray, Rob (Thesis advisor) / Chiou, Erin (Committee member) / Cooke, Nancy (Committee member) / Gutzwiller, Robert (Committee member) / Arizona State University (Publisher)
Created2021