A data mining approach to strategy prediction
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Cooperative learning by replay files in real-time strategy game
CDVE'10 Proceedings of the 7th international conference on Cooperative design, visualization, and engineering
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
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StarCraft is one of the most famous Real-Time Strategy Games and there have been several competitions on AI bots. In order to win StarCraft, players have to predict their opponents strategy and respond properly. Human players used to scout their opponent territory using a unit and gathering information through direct observation to predict their opponents strategy. The accurate prediction of an opponents strategy gives players a big advantage in the early stage of a game. Usually, strategies of StarCraft can be divided into two parts: fast and slow attack strategies. Initial attack timing is an important factor of game strategies. In this paper, we apply a scouting algorithm and various machine learning algorithms to predict an opponents attack timing (strategy). Training data are collected from the games between our Xelnaga bot with the scouting algorithm and various online human players. Experimental results show that the machine learning approach based on realistic scouting data can be beneficial in predicting the opponents early-stage strategy.