# Automated Playtesting with Procedural Personas through MCTS with Evolved Heuristics

arXiv: https://arxiv.org/abs/1802.06881 · HTML: https://mlsanity.com/paper/1802.06881/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2018.

## Built on by (most cited first, 28 of 28)

- [Improving Playtesting Coverage via Curiosity Driven Reinforcement Learning Agents](https://mlsanity.com/paper/2103.13798/) (2021)
- [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)
- [Automated Play-Testing Through RL Based Human-Like Play-Styles Generation](https://mlsanity.com/paper/2211.17188/) (2022)
- [Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation](https://mlsanity.com/paper/2207.08379/) (2022)
- [Generative Personas That Behave and Experience Like Humans](https://mlsanity.com/paper/2209.00459/) (2022)
- [CCPT: Automatic Gameplay Testing and Validation with Curiosity-Conditioned Proximal Trajectories](https://mlsanity.com/paper/2202.10057/) (2022)
- [Predicting Game Engagement and Difficulty Using AI Players](https://mlsanity.com/paper/2107.12061/) (2021)
- [Continuous Monte Carlo Graph Search](https://mlsanity.com/paper/2210.01426/) (2022)
- [Configurable Agent With Reward As Input: A Play-Style Continuum Generation](https://mlsanity.com/paper/2211.16221/) (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)
- [Agents for Automated User Experience Testing](https://mlsanity.com/paper/2104.06220/) (2021)
- [Adapting Procedural Content Generation to Player Personas Through Evolution](https://mlsanity.com/paper/2112.04406/) (2021)
- [Predicting Personas Using Mechanic Frequencies and Game State Traces](https://mlsanity.com/paper/2203.13351/) (2022)
- [Automatic Testing and Validation of Level of Detail Reductions Through Supervised Learning](https://mlsanity.com/paper/2208.12674/) (2022)
- [Synergizing Code Coverage and Gameplay Intent: Coverage-Aware Game Playtesting with LLM-Guided Reinforcement Learning](https://mlsanity.com/paper/2512.12706/) (2025)
- [Game Mechanic Alignment Theory and Discovery](https://mlsanity.com/paper/2102.10247/) (2021)
- [Persona-driven Dominant/Submissive Map (PDSM) Generation for Tutorials](https://mlsanity.com/paper/2204.05217/) (2022)
- [NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons](https://mlsanity.com/paper/2506.19530/) (2025)
- [Beyond Asking: A Pipeline for Personalized Game Generation that Reads Players from Behavior](https://mlsanity.com/paper/2608.16196/) (2026)
- [Towards Understanding the Effects of Evolving the MCTS UCT Selection Policy](https://mlsanity.com/paper/2302.03352/) (2023)
- [Injecting Combinatorial Optimization into MCTS: Application to the Board Game boop](https://mlsanity.com/paper/2406.08766/) (2024)
- [A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents](https://mlsanity.com/paper/2505.16801/) (2025)
- [UBCL: A Reinforcement Learning Framework for Controllable and Diverse Player Behaviors](https://mlsanity.com/paper/2512.10835/) (2025)
- [Mazocarta: A Seeded Procedural Deckbuilder for Instrumented Game Development](https://mlsanity.com/paper/2605.08319/) (2026)
- [Reward-Adaptive Iterative Discovery: A Case Study on Automated Game Testing for NHL26](https://mlsanity.com/paper/2607.07498/) (2026)
- [Comparative Analysis of GAT and BERT for Human-Like Playtesting](https://mlsanity.com/paper/2607.11501/) (2026)

## Abstract (arXiv)

This paper describes a method for generative player modeling and its application to the automatic testing of game content using archetypal player models called procedural personas. Theoretically grounded in psychological decision theory, procedural personas are implemented using a variation of Monte Carlo Tree Search (MCTS) where the node selection criteria are developed using evolutionary computation, replacing the standard UCB1 criterion of MCTS. Using these personas we demonstrate how generative player models can be applied to a varied corpus of game levels and demonstrate how different play styles can be enacted in each level. In short, we use artificially intelligent personas to construct synthetic playtesters. The proposed approach could be used as a tool for automatic play testing when human feedback is not readily available or when quick visualization of potential interactions is necessary. Possible applications include interactive tools during game development or procedural content generation systems where many evaluations must be conducted within a short time span.