Evolving Swarm Intelligence for Task Allocation in a Real Time Strategy Game
Anderson Rocha Tavares, Héctor Azpúrua, Luiz Chaimowicz · 2014
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.