Evolving Swarm Intelligence for Task Allocation in a Real Time Strategy Game

Real time strategy games are complex scenarioswhere multiple agents must be coordinated in a dynamic,partially observable environment. In this work, we model thecoordination of these agents as a task allocation problem, in which specific tasks are given to the agents that are more suited to execute them. We employ a task allocation algorithm based on swarm intelligence and adjust its parameters using a genetic algorithm. To evaluate this approach, we implement this coordination mechanism in the AI of a popular video game: StarCraft: BroodWar. Experiment results show that the genetic algorithm enhances performance of the task allocation algorithm. Besides, performance of the proposed approach in matches against StarCraft´s native AI is comparable to that of a tournament-level software-controlled player for StarCraft.