Spatial Distribution through Swarm Behavior on a Military Group in the Starcraft Video Game

New levels of challenge are required for real-time strategy games because of their limited learning curve. Usually this learning curve has an upper bound after which a player is able to win the most of the matches against the built-in artificial intelligence. In this research a method based on swarm intelligence is proposed to control a military swarm in the Star craft environment in order to achieve a better performance against the built-in artificial intelligence of the game. This method provides a way to form the units in such a way that they reach a suitable position to attack an enemy group conformed by elements of the same type. The results obtained in the experiments prove that is possible to implement this method and achieve a better performance than the greedy algorithm of the built-in AI. Finally, since the method is based in nature it is scalable, decentralized and robust as shown in the results.