Identifying players and predicting actions from RTS game replays
This paper investigates the problem of identifying a Real- Time Strategy game player and predicting what a player will do next based on previous actions in the game. Being able to recognize a player's playing style and their strategies helps us learn the strengths and weaknesses of a specific player, devise counter-strategies that beat the player, and eventually helps us to build better game AI.We use machine learning algorithms in the WEKA toolkit to learn how to identify a StarCraft II player and predict the next move of the player from features extracted from game replays. Our results reveal that using a J48 decision tree correctly identifies a specific player out of forty-one players 75% of the time. This decision tree can identify a player within the first nine minutes of the game. Finally, we can correctly predict what action out of fifty possible actions a player will choose to do next 81% of the time. For a player we have already identified, we can correctly predict the action 89% of the time. This work informs our research on identifying and learning from Real-Time Strategy players and building better game AI.
BWAPI, Data Mining, Machine Learning, Real-Time Strategy Game, WEKA
Ballinger, Christopher, Siming Liu, and Sushil J. Louis. "Identifying Players and Predicting Actions from RTS Game Replays." In 28th International Conference on Computer Applications in Industry and Engineering, San Diego, CA, October 12-14, 2015, 9-16.
28th International Conference on Computer Applications in Industry and Engineering, CAINE 2015