Automated Playtesting of Matching Tile Games
  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.
