BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract) - Lee, Donghyeon; Kim, Man-Je; Ahn, Chang Wook (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.