Honors Program
[Honors-in-Discipline (Choose below)]
Date of Award
8-2017
Thesis Professor(s)
Brian Bennett
Thesis Professor Department
Computing
Thesis Reader(s)
Matthew Harrison
Abstract
Immersion is essential for player experience in video games. Artificial Intelligence serves as an agent that can generate human-like responses and intelligence to reinforce a player’s immersion into their environment. The most common strategy involved in video game AI is using decision trees to guide chosen actions. However, decision trees result in repetitive and robotic actions that reflect an unrealistic interaction. This experiment applies a genetic algorithm that explores selection, crossover, and mutation functions for genetic algorithm implementation in an isolated Super Mario Bros. pathfinding environment. An optimized pathfinding AI can be created by combining an elitist selection strategy with a uniform distribution crossover and minimal mutation rate.
Publisher
East Tennessee State University
Document Type
Honors Thesis - Open Access
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
Recommended Citation
Ambuehl, Nathan, "Investigating Genetic Algorithm Optimization Techniques in Video Games" (2017). Undergraduate Honors Theses. Paper 748. https://dc.etsu.edu/honors/748
Copyright
Copyright by the authors.