Automatic Critical Mechanic Discovery Using Playtraces in Video Games

TL;DR

This paper introduces an automated method for discovering critical game mechanics using playtrace data and game descriptions, validated through human comparison and performance improvements in AI agents across multiple games.

Contribution

It presents a novel approach combining game description parsing and playtrace analysis for critical mechanic discovery, outperforming previous methods.

Findings

Higher consistency with human-identified mechanics

Significant performance improvements in AI agents

Playtrace data enhances mechanic discovery

Abstract

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.