# DL-DDA -- Deep Learning based Dynamic Difficulty Adjustment with UX and Gameplay constraints

arXiv: https://arxiv.org/abs/2106.03075 · HTML: https://mlsanity.com/paper/2106.03075/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 24 references that are in the map.
- First posted on arXiv 6 Jun 2021.

## Builds on (most cited first, 3 of 3)

- [Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks](https://mlsanity.com/paper/1906.04893/) (2019)
- [Learning Surrogate Losses](https://mlsanity.com/paper/1905.10108/) (2019)
- [Generalized Quantile Loss for Deep Neural Networks](https://mlsanity.com/paper/2012.14348/) (2020)

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

- [Affective Game Computing: A Survey](https://mlsanity.com/paper/2309.14104/) (2023)
- [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)
- [OpenBlock: Constructive and Verified Content Generation for Adaptive Tile-Matching Games](https://mlsanity.com/paper/2609.22177/) (2026)

## Abstract (arXiv)

Dynamic difficulty adjustment (DDA) is a process of automatically changing a game difficulty for the optimization of user experience. It is a vital part of almost any modern game. Most existing DDA approaches concentrate on the experience of a player without looking at the rest of the players. We propose a method that automatically optimizes user experience while taking into consideration other players and macro constraints imposed by the game. The method is based on deep neural network architecture that involves a count loss constraint that has zero gradients in most of its support. We suggest a method to optimize this loss function and provide theoretical analysis for its performance. Finally, we provide empirical results of an internal experiment that was done on 200,000 players and was found to outperform the corresponding manual heuristics crafted by game design experts.