# Automated Playtesting of Matching Tile Games

arXiv: https://arxiv.org/abs/1907.06570 · HTML: https://mlsanity.com/paper/1907.06570/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2019.

## Built on by (most cited first, 10 of 10)

- [Towards Informed Design and Validation Assistance in Computer Games Using Imitation Learning](https://mlsanity.com/paper/2208.07811/) (2022)
- [Technical Challenges of Deploying Reinforcement Learning Agents for Game Testing in AAA Games](https://mlsanity.com/paper/2307.11105/) (2023)
- [Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation](https://mlsanity.com/paper/2207.08379/) (2022)
- [CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectories](https://mlsanity.com/paper/2202.10057/) (2022)
- [Estimating player completion rate in mobile puzzle games using reinforcement learning](https://mlsanity.com/paper/2306.14626/) (2023)
- [Generating Personas for Games with Multimodal Adversarial Imitation Learning](https://mlsanity.com/paper/2308.07598/) (2023)
- [Reinforcement Learning for High-Level Strategic Control in Tower Defense Games](https://mlsanity.com/paper/2406.07980/) (2024)
- [Playtesting: What is Beyond Personas](https://mlsanity.com/paper/2107.11965/) (2021)
- [Difficulty Modelling in Mobile Puzzle Games: An Empirical Study on Different Methods to Combine Player Analytics and Simulated Data](https://mlsanity.com/paper/2401.17436/) (2024)
- [UBCL: A Reinforcement Learning Framework for Controllable and Diverse Player Behaviors](https://mlsanity.com/paper/2512.10835/) (2025)

## Abstract (arXiv)

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.