Automatic Critical Mechanic Discovery Using Playtraces in Video Games
This addresses the problem of automating game mechanic analysis for AI game-playing systems, representing an incremental improvement over existing methods.
The paper tackles the problem of automatically discovering critical game mechanics in video games by combining game description parsing with playtrace information. The results show the method matches human-identified mechanics with higher consistency than baseline and improves MCTS agent performance significantly across 4 tested 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.