# Automated Playtesting of Matching Tile Games

## TL;DR

This paper introduces an automated playtesting system for Match-3 games using evolving procedural personas to simulate diverse human playstyles, enhancing game design analysis and validation.

## Contribution

It develops evolving procedural personas for Monte Carlo Tree Search agents to better mimic human playstyles in Match-3 games, advancing automated game testing methods.

## Key findings

- Evolved agents outperform standard MCTS and random agents.
- Procedural personas provide diverse playstyle simulations.
- User study shows agents' play traces resemble human behavior.

## Abstract

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.