# Fast Game Content Adaptation Through Bayesian-based Player Modelling

arXiv: https://arxiv.org/abs/2105.08484 · HTML: https://mlsanity.com/paper/2105.08484/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 18 May 2021.

## Builds on (most cited first, 9 of 9)

- [Robots that can adapt like animals](https://mlsanity.com/paper/1407.3501/) (2014)
- [Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network](https://mlsanity.com/paper/1805.00728/) (2018)
- [Illuminating Mario Scenes in the Latent Space of a Generative Adversarial Network](https://mlsanity.com/paper/2007.05674/) (2020)
- [Bootstrapping Conditional GANs for Video Game Level Generation](https://mlsanity.com/paper/1910.01603/) (2019)
- [Conditional Level Generation and Game Blending](https://mlsanity.com/paper/2010.07735/) (2020)
- [Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error](https://mlsanity.com/paper/2005.07677/) (2020)
- [Automatic Playtesting for Game Parameter Tuning via Active Learning](https://mlsanity.com/paper/1908.01417/) (2019)
- [Dynamic Difficulty Adjustment via Fast User Adaptation](https://mlsanity.com/paper/2006.15545/) (2020)
- [Dynamic Difficulty Adjustment on MOBA Games](https://mlsanity.com/paper/1706.02796/) (2017)

## Built on by (most cited first, 3 of 3)

- [Adapting Procedural Content Generation to Player Personas Through Evolution](https://mlsanity.com/paper/2112.04406/) (2021)
- [Personalized Game Difficulty Prediction Using Factorization Machines](https://mlsanity.com/paper/2209.13495/) (2022)
- [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)

## Abstract (arXiv)

In games, as well as many user-facing systems, adapting content to users' preferences and experience is an important challenge. This paper explores a novel method to realize this goal in the context of dynamic difficulty adjustment (DDA). Here the aim is to constantly adapt the content of a game to the skill level of the player, keeping them engaged by avoiding states that are either too difficult or too easy. Current systems for DDA rely on expensive data mining, or on hand-crafted rules designed for particular domains, and usually adapts to keep players in the flow, leaving no room for the designer to present content that is purposefully easy or difficult. This paper presents Fast Bayesian Content Adaption (FBCA), a system for DDA that is agnostic to the domain and that can target particular difficulties. We deploy this framework in two different domains: the puzzle game Sudoku, and a simple Roguelike game. By modifying the acquisition function's optimization, we are reliably able to present a content with a bespoke difficulty for players with different skill levels in less than five iterations for Sudoku and fifteen iterations for the simple Roguelike. Our method significantly outperforms simpler DDA heuristics with the added benefit of maintaining a model of the user. These results point towards a promising alternative for content adaption in a variety of different domains.