Date Approved

12-15-2024

Graduate Degree Type

Project

Degree Name

Applied Computer Science (M.S.)

Degree Program

School of Computing and Information Systems

First Advisor

Byron DeVries

Academic Year

2024/2025

Abstract

Machine learning (ML) and artificial intelligence (AI) are terms that are often used synonymously, but they are ever-so slightly different. Machine learning is really a subset of artificial intelligence and involves creating an algorithm so that a computer can learn patterns. Artificial intelligence extends machine learning with the goal to go beyond pattern recognition by having a computer that is capable of mimicking human intelligence. Video games often use the term AI in reference to the bots or non-playable characters (NPCs) that players may interact with. Generally, these bots do not actually implement AI, nor do they use ML, but there has been some movement by indie game developers to develop games that implement large language models (LLMs). LLMs utilize machine learning to generate text based on a given context. In video games, these LLMs are used to generate dynamic conversations that players have with NPCs. While this advancement may allow for players to feel more immersed in the game, there is not as much of a push to use machine learning to challenge the players. Current games may utilize a difficulty setting for their bots, but this is generally used to control settings related to a bot’s health, damage, and aggressiveness. This just ensures that the bots are either pushovers or brutes, but they do not utilize any form of tactics or strategies to challenge the players. One of the goals of this project was to prove that bots in video games could implement machine learning to learn how to play the game and strategize the best way to win the game. The bots will learn what strategies players employ and will develop their own tactics to counteract players.

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