Automatic Critical Mechanic Discovery Using Playtraces in Video Games
  We present a new method of automatic critical mechanic discovery for video
games using a combination of game description parsing and playtrace
information. This method is applied to several games within the General Video
Game Artificial Intelligence (GVG-AI) framework. In a user study,
human-identified mechanics are compared against system-identified critical
mechanics to verify alignment between humans and the system. The results of the
study demonstrate that the new method is able to match humans with higher
consistency than baseline. Our system is further validated by comparing MCTS
agents augmented with critical mechanics and vanilla MCTS agents on $4$ games
from GVG-AI. Our new playtrace method shows a significant performance
improvement over the baseline for all 4 tested games. The proposed method also
shows either matched or improved performance over the old method, demonstrating
that playtrace information is responsible for more complete critical mechanic
discovery.
