Date of Graduation
5-2015
Document Type
Thesis
Degree Name
Bachelor of Science in Computer Engineering
Degree Level
Undergraduate
Department
Computer Science and Computer Engineering
Advisor/Mentor
Gauch, John
Abstract
Human beings have been playing games for centuries, and over time, mankind has learned how to excel at these fun competitions. With the ever-growing interest in the field of Machine Learning and Artificial Intelligence (AI), developers have been finding ways to let the game compete against the player much like another human would. While there are many approaches to humanlike learning in machines, this article will focus on using Evolutionary Optimization as a method to develop different levels of pseudo-thinking inan AI used for ato effectively play the Connect Four game.
Citation
Kordsmeier, D. A. (2015). Using Genetic Learning in Weight-Based Game AI. Computer Science and Computer Engineering Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/csceuht/32
