AAAI Publications, Ninth Artificial Intelligence and Interactive Digital Entertainment Conference

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Predicting Army Combat Outcomes in StarCraft
Marius Stanescu, Sergio Poo Hernandez, Graham Erickson, Russel Greiner, Michael Buro

Last modified: 2013-11-13


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.


AI, RTS, StarCraft, Combat

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