Presenter: Martene Stanberry

Date: Friday, April 25, 2025

Time: 3 PM

Room: COHH 3123

Title: Utilizing Control Theory and Reinforcement Learning Theory to Enhance Artificial Intelligence

Abstract: Motivated by recent technological advances, specifically in Artificial Intelligence (AI), this presentation will provide an overview of control theory, reinforcement learning theory, and the potential for utilizing techniques from these theories to enhance AI.  Control theory and reinforcement learning theory involve optimizing the behavior of a system through decision making and AI is technology that enables computers and machines to replicate human learning, understanding, problem solving, decision making, inventiveness, and autonomy (IBM, 2024).  With advanced AI, many tasks can be completed correctly and efficiently without human intelligence or involvement, but modeling and training a system can bolster performance, reliability, and accuracy of results.  This presentation will focus on control theory, reinforcement learning theory, and AI with emphasis on fault detection and error identification.  In addition, the presenters’ pathway to a doctoral degree in applied mathematics and other research interests will be highlighted.