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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