University Honors, Honors in Technology
Date of Award
Thesis Professor Department
Computer and Information Sciences
Martin Barrett, Robert Beeler
Ant colony optimization (ACO) is an algorithm which simulates ant foraging behavior. When ants search for food they leave pheromone trails to tell other ants which paths to take to find food. ACO has been adapted to many different problems in computer science: mainly variations on shortest path algorithms for graphs and networks.
ACO can be adapted to work as a form of communication between separate agents in a video game AI. By controlling the effectiveness of this communication, the difficulty of the game should be able to be controlled. Experimentation has shown that ACO works effectively as a form of communication between agents and supports that ACO is an effective form of difficulty control. However, further experimentation is needed to definitively show that ACO is effective at controlling difficulty and to show that it will also work in a large scale system.
Honors Thesis - Open Access
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
Courtney, Joshua, "Using Ant Colonization Optimization to Control Difficulty in Video Game AI." (2010). Undergraduate Honors Theses. Paper 147. http://dc.etsu.edu/honors/147
Copyright by the authors.