# Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error

arXiv: https://arxiv.org/abs/2005.07677 · HTML: https://mlsanity.com/paper/2005.07677/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2020.

## Built on by (most cited first, 14 of 14)

- [Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencoder](https://mlsanity.com/paper/2102.12463/) (2021)
- [Games for Artificial Intelligence Research: A Review and Perspectives](https://mlsanity.com/paper/2304.13269/) (2023)
- [Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity](https://mlsanity.com/paper/2210.09918/) (2022)
- [Hierarchical Quality-Diversity for Online Damage Recovery](https://mlsanity.com/paper/2204.05726/) (2022)
- [Fast Game Content Adaptation Through Bayesian-based Player Modelling](https://mlsanity.com/paper/2105.08484/) (2021)
- [Statistical Modelling of Level Difficulty in Puzzle Games](https://mlsanity.com/paper/2107.03305/) (2021)
- [Dynamic Difficulty Adjustment in Virtual Reality Exergames through Experience-driven Procedural Content Generation](https://mlsanity.com/paper/2108.08762/) (2021)
- [Generative Design through Quality-Diversity Data Synthesis and Language Models](https://mlsanity.com/paper/2405.09997/) (2024)
- [Personalized Game Difficulty Prediction Using Factorization Machines](https://mlsanity.com/paper/2209.13495/) (2022)
- [Towards Objective Metrics for Procedurally Generated Video Game Levels](https://mlsanity.com/paper/2201.10334/) (2022)
- [Rinascimento: searching the behaviour space of Splendor](https://mlsanity.com/paper/2106.08371/) (2021)
- [Level Assembly as a Markov Decision Process](https://mlsanity.com/paper/2304.13922/) (2023)
- [Quality-Diversity Optimization as Multi-Objective Optimization](https://mlsanity.com/paper/2602.00478/) (2026)
- [Procedural Generation of First Person Shooter Maps using Map-Elites](https://mlsanity.com/paper/2605.30570/) (2026)

## Abstract (arXiv)

Methods for dynamic difficulty adjustment allow games to be tailored to particular players to maximize their engagement. However, current methods often only modify a limited set of game features such as the difficulty of the opponents, or the availability of resources. Other approaches, such as experience-driven Procedural Content Generation (PCG), can generate complete levels with desired properties such as levels that are neither too hard nor too easy, but require many iterations. This paper presents a method that can generate and search for complete levels with a specific target difficulty in only a few trials. This advance is enabled by through an Intelligent Trial-and-Error algorithm, originally developed to allow robots to adapt quickly. Our algorithm first creates a large variety of different levels that vary across predefined dimensions such as leniency or map coverage. The performance of an AI playing agent on these maps gives a proxy for how difficult the level would be for another AI agent (e.g. one that employs Monte Carlo Tree Search instead of Greedy Tree Search); using this information, a Bayesian Optimization procedure is deployed, updating the difficulty of the prior map to reflect the ability of the agent. The approach can reliably find levels with a specific target difficulty for a variety of planning agents in only a few trials, while maintaining an understanding of their skill landscape.