Automated Playtesting of Matching Tile Games
This work addresses game design testing for developers by providing an incremental method to simulate human playstyles.
The paper tackled automated playtesting of Match-3 games by developing procedural personas through evolving utility functions for Monte Carlo Tree Search agents, comparing them to standard and random agents, and conducting a user study to evaluate against human play traces.
Matching tile games are an extremely popular game genre. Arguably the most popular iteration, Match-3 games, are simple to understand puzzle games, making them great benchmarks for research. In this paper, we propose developing different procedural personas for Match-3 games in order to approximate different human playstyles to create an automated playtesting system. The procedural personas are realized through evolving the utility function for the Monte Carlo Tree Search agent. We compare the performance and results of the evolution agents with the standard Vanilla Monte Carlo Tree Search implementation as well as to a random move-selection agent. We then observe the impacts on both the game's design and the game design process. Lastly, a user study is performed to compare the agents to human play traces.