Combining genetic algorithm and swarm intelligence for task allocation in a real time strategy game
DOI:
https://doi.org/10.5753/jis.2017.671Abstract
Real time strategy games are complex scenarios where multiple agents must be coordinated in a dynamic, partially observable environment. In this work, we model coordination as a task allocation problem, in which specific tasks must be properly assigned to agents. We employ a task allocation algorithm based on swarm intelligence and adjust its parameters using a genetic algorithm. A fitness estimation method is employed to accelerate execution of the 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 successfully adjusts task allocation parameters. Besides, we assess the trade-off between solution quality and execution time of the genetic algorithm with fitness estimation.Downloads
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Published
2017-09-14
How to Cite
TAVARES, A. R.; ZUIN, G. L.; AZPÚRUA, H.; CHAIMOWICZ, L. Combining genetic algorithm and swarm intelligence for task allocation in a real time strategy game. Journal on Interactive Systems, Porto Alegre, RS, v. 8, n. 1, 2017. DOI: 10.5753/jis.2017.671. Disponível em: https://journals-sol.sbc.org.br/index.php/jis/article/view/671. Acesso em: 8 sep. 2024.
Issue
Section
Special Issue - SBGAMES 2014
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