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.
| Identifer | oai:union.ndltd.org:ETSU/oai:dc.etsu.edu:honors-1151 |
| Date | 01 May 2010 |
| Creators | Courtney, Joshua |
| Publisher | Digital Commons @ East Tennessee State University |
| Source Sets | East Tennessee State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Undergraduate Honors Theses |
| Rights | Copyright by the authors., http://creativecommons.org/licenses/by-nc-nd/3.0/ |
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