Date of Award
Summer 2025
Document Type
Open Access Dissertation
Department
Computer Science and Engineering
First Advisor
Amit Sheth
Abstract
This dissertation introduces process-grounded knowledge-infused learning and reasoning, a novel framework for integrating domain-expertise-based process knowledge into the learning and reasoning mechanisms of artificial intelligence systems. This approach is designed to produce controlled, transparent, and reliable predictions in critical tasks such as medical diagnosis and recommendation. By focusing on the case study of mental illness diagnosis and recommendation—where decision-making must be grounded in processes such as disorder-specific diagnostic criteria—this work demonstrates methods to embed structured decision-making directly into the system architecture during both training and inference. This integration facilitates end-to-end training and reasoning while ensuring that outputs strictly adhere to established, domain-expertise-based processes. Comparative evaluations with stateof-the-art baseline systems indicate that process-grounded knowledge-infused systems achieve adequate accuracy, real-world scalability, and enhance explainability and safety in reliability-critical applications.
Rights
© 2025, Kaushik Roy
Recommended Citation
Roy, K.(2025). Process-Grounded Knowledge-Infused Learning and Decision Making. (Doctoral dissertation). Retrieved from https://scholarcommons.sc.edu/etd/8563