Predicting Army Combat Outcomes in StarCraft

Authors

  • 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

Keywords:

AI, RTS, StarCraft, Combat

Abstract

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.

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Published

2021-06-30

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. Retrieved from https://ojs.aaai.org/index.php/AIIDE/article/view/12683