# Dynamic Difficulty Adjustment via Fast User Adaptation

arXiv: https://arxiv.org/abs/2006.15545 · HTML: https://mlsanity.com/paper/2006.15545/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2020.

## Built on by (most cited first, 4 of 4)

- [Fast Game Content Adaptation Through Bayesian-based Player Modelling](https://mlsanity.com/paper/2105.08484/) (2021)
- [Machine Learning-Based GPS Multipath Detection Method Using Dual Antennas](https://mlsanity.com/paper/2204.14001/) (2022)
- [Personalized Game Difficulty Prediction Using Factorization Machines](https://mlsanity.com/paper/2209.13495/) (2022)
- [Strength Estimation and Human-Like Strength Adjustment in Games](https://mlsanity.com/paper/2502.17109/) (2025)

## Abstract (arXiv)

Dynamic difficulty adjustment (DDA) is a technology that adapts a game's challenge to match the player's skill. It is a key element in game development that provides continuous motivation and immersion to the player. However, conventional DDA methods require tuning in-game parameters to generate the levels for various players. Recent DDA approaches based on deep learning can shorten the time-consuming tuning process, but require sufficient user demo data for adaptation. In this paper, we present a fast user adaptation method that can adjust the difficulty of the game for various players using only a small amount of demo data by applying a meta-learning algorithm. In the video game environment user test (n=9), our proposed DDA method outperformed a typical deep learning-based baseline method.