BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)
Donghyeon Lee, Man-Je Kim, Chang Wook Ahn · Proceedings of the AAAI Conference on Artificial Intelligence · 2020
In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.