@article{Lee_Kim_Ahn_2020, title={BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)}, volume={34}, url={https://ojs.aaai.org/index.php/AAAI/article/view/7197}, DOI={10.1609/aaai.v34i10.7197}, abstractNote={<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>}, number={10}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Lee, Donghyeon and Kim, Man-Je and Ahn, Chang Wook}, year={2020}, month={Apr.}, pages={13849-13850} }