Examining The Intersection Between Machine Learning and Video Games

Author: ORCID icon orcid.org/0009-0008-6466-5905
Bradford, William, School of Engineering and Applied Science, University of Virginia
Advisors:
Wayland, Kent, EN-Engineering and Society, University of Virginia
Lin, Felix, EN-Comp Science Dept, University of Virginia
Abstract:

Historically, computers have always been worse than humans when it comes to artistic expression. Now, with the rise of artificial intelligence, computers have become increasingly proficient at mimicking human artistic expression, and even perform better than humans in some areas. Specifically, machines are far superior to humans at playing relatively simple games like Connect Four. In the last few decades, computers have even far eclipsed humans at playing more complex games, such as western chess and the Chinese game, Go using novel machine learning techniques. Even despite this, machine learning has yet to crack the most complex of competitive games, like the Pokémon video games. This body of works aims to both advance machine learning in complex games and address the effects of machine learning on the value of human artistic expression.

Degree:
BS (Bachelor of Science)
Keywords:
chess, machine learning, reinforcement learning, artificial intelligence, creative expression, Pokemon
Language:
English
Issued Date:
2025/05/09