Player identification from RTS game replays


This paper investigates the problem of identifying an RTS game player from their playing style. More specifically, we use machine learning algorithms in the WEKA toolkit to learn how to identify a StarCraft II player from features extracted from game replays. Results reveal that using AdaBoost on a decision tree and Random Forest decision trees perform best on identifying a player from replay data. For a particular player, the results also help us identify the most frequently used strategy against different opponent types and provide some insight into the player's strengths and weaknesses. We believe that these results will help us design better RTS game AI.

Document Type

Conference Proceeding

Publication Date


Journal Title

28th International Conference on Computers and Their Applications 2013, CATA 2013