Predicting the Outcome of Small Battles in StarCraft
نویسنده
چکیده
Real-Time Strategy (RTS) games are popular testbeds for AI researchers. In this paper we compare different machine learning algorithms to predict the outcome of small battles of marines in StarCraft, a popular RTS game. The predictions are made from the perspective of an external observer of the game and they are based only on the actions that the different units perform in the battlefield. Our empirical results show that case-based approaches based on k-Nearest Neighbor classification outperform other standard classification algorithms like Linear and Quadratic Discriminant Analysis or Support Vector Machines.
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