BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

Authors

  • Donghyeon Lee Gwangju Institute of Science and Technology
  • Man-Je Kim Gwangju Institute of Science and Technology
  • Chang Wook Ahn Gwangju Institute of Science and Technology

DOI:

https://doi.org/10.1609/aaai.v34i10.7197

Abstract

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.

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Published

2020-04-03

How to Cite

Lee, D., Kim, M.-J., & Ahn, C. W. (2020). BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13849-13850. https://doi.org/10.1609/aaai.v34i10.7197

Issue

Vol. 34 No. 10: Issue 10: AAAI-20 Student Tracks

Section

Student Abstract Track