Abstract
This thesis explores the development of a personalized chess AI capable of mimicking an individual’s playstyle. The research delves into the evolution of chess AI, from early algorithmic engines to the current state-of-the-art neural network approaches. The study was conducted in two phases. The first phase employed traditional regression models to predict chess moves based on the author’s historical games, achieving moderate success. The second phase investigated the potential of neural networks, specifically the Maia chess engine designed to model human-like play. While fine-tuning Maia on the author’s game data encountered technical challenges, pre-trained Maia models demonstrated significantly higher accuracy in predicting the author’s moves compared to the regression model. This research contributes to the growing field of personalized AI, highlighting the potential for creating systems that adapt to individual behavior and preferences. Although limitations were encountered, the study paves the way for future explorations in personalized chess AI and its potential to enhance learning and enjoyment for players of all levels.
Advisor
Kelvey, Robert
Department
Computer Science
Recommended Citation
Elawad, Eltahir, "Creating a Chess AI of Myself" (2024). Senior Independent Study Theses. Paper 11028.
https://openworks.wooster.edu/independentstudy/11028
Disciplines
Artificial Intelligence and Robotics
Publication Date
2024
Degree Granted
Bachelor of Arts
Document Type
Senior Independent Study Thesis
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