TY - JOUR AU - Lee, Donghyeon AU - Kim, Man-Je AU - Ahn, Chang Wook PY - 2020/04/03 Y2 - 2024/03/29 TI - BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract) JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 10 SE - Student Abstract Track DO - 10.1609/aaai.v34i10.7197 UR - https://ojs.aaai.org/index.php/AAAI/article/view/7197 SP - 13849-13850 AB - <p>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.</p> ER -