Automatic Critical Mechanic Discovery in Video Games
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We present a system that automatically discovers critical mechanics in a variety of video games within the General Video Game Artificial Intelligence (GVG-AI) framework using a combination of game description parsing and playtrace information. Critical mechanics are defined as the mechanics most necessary to trigger in order to perform well in the game. In a user study, human-identified mechanics are compared against system-identified mechanics to verify alignment between humans and the system. The results of the study demonstrate that our method is able to match humans with high consistency. Our system is further validated by comparing MCTS agents augmented with critical mechanic information against baseline MCTS agents on 4 games in GVG-AI. The augmented agents show a significant performance improvement over their baseline counterparts for all 4 tested games, demonstrating that knowledge of system-identified mechanics are responsible for improved performance.
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