Predicting Army Combat Outcomes in StarCraft


  • Marius Stanescu University of Alberta
  • Sergio Poo Hernandez University of Alberta
  • Graham Erickson University of Alberta
  • Russel Greiner University of Alberta
  • Michael Buro University of Alberta



AI, RTS, StarCraft, Combat


Smart decision making at the tactical level is important for Artificial Intelligence (AI) agents to perform well in the domain of real-time strategy (RTS) games.  This paper presents a Bayesian model that can be used to predict the outcomes of isolated battles, as well as predict what units are needed to defeat a given army.  Model parameters are learned from simulated battles, in order to minimize the dependency on player skill.  We apply our model to the game of StarCraft,  with the end-goal of using the predictor as a module for making high-level combat decisions, and show that the model is capable of making accurate predictions.




How to Cite

Stanescu, M., Poo Hernandez, S., Erickson, G., Greiner, R., & Buro, M. (2021). Predicting Army Combat Outcomes in StarCraft. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 9(1), 86-92.