# Automatic Playtesting for Game Parameter Tuning via Active Learning

arXiv: https://arxiv.org/abs/1908.01417 · HTML: https://mlsanity.com/paper/1908.01417/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2019.

## Built on by (most cited first, 9 of 9)

- [Augmenting Automated Game Testing with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.15819/) (2021)
- [AdaptiFont: Increasing Individuals' Reading Speed with a Generative Font Model and Bayesian Optimization](https://mlsanity.com/paper/2104.10741/) (2021)
- [Fast Game Content Adaptation Through Bayesian-based Player Modelling](https://mlsanity.com/paper/2105.08484/) (2021)
- [Active Learning for Classifying 2D Grid-Based Level Completability](https://mlsanity.com/paper/2309.04367/) (2023)
- [NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons](https://mlsanity.com/paper/2506.19530/) (2025)
- [Fly, Fail, Fix: Iterative Game Repair with Reinforcement Learning and Large Multimodal Models](https://mlsanity.com/paper/2507.12666/) (2025)
- [Rinascimento: searching the behaviour space of Splendor](https://mlsanity.com/paper/2106.08371/) (2021)
- [A Framework for Predicting the Impact of Game Balance Changes through Meta Discovery](https://mlsanity.com/paper/2409.07340/) (2024)
- [Mazocarta: A Seeded Procedural Deckbuilder for Instrumented Game Development](https://mlsanity.com/paper/2605.08319/) (2026)

## Abstract (arXiv)

Game designers use human playtesting to gather feedback about game design elements when iteratively improving a game. Playtesting, however, is expensive: human testers must be recruited, playtest results must be aggregated and interpreted, and changes to game designs must be extrapolated from these results. Can automated methods reduce this expense? We show how active learning techniques can formalize and automate a subset of playtesting goals. Specifically, we focus on the low-level parameter tuning required to balance a game once the mechanics have been chosen. Through a case study on a shoot-`em-up game we demonstrate the efficacy of active learning to reduce the amount of playtesting needed to choose the optimal set of game parameters for two classes of (formal) design objectives. This work opens the potential for additional methods to reduce the human burden of performing playtesting for a variety of relevant design concerns.