# Learning to Blend Computer Game Levels

arXiv: https://arxiv.org/abs/1603.02738 · HTML: https://mlsanity.com/paper/1603.02738/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2016.

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

- [Procedural Content Generation via Knowledge Transformation (PCG-KT)](https://mlsanity.com/paper/2305.00644/) (2023)
- [Clustering-based Tile Embedding (CTE): A General Representation for Level Design with Skewed Tile Distributions](https://mlsanity.com/paper/2210.12789/) (2022)
- [Game Level Blending using a Learned Level Representation](https://mlsanity.com/paper/2306.16666/) (2023)
- [Co-Evolving Complexity: An Adversarial Framework for Automatic MARL Curricula](https://mlsanity.com/paper/2509.03771/) (2025)

## Abstract (arXiv)

We present an approach to generate novel computer game levels that blend different game concepts in an unsupervised fashion. Our primary contribution is an analogical reasoning process to construct blends between level design models learned from gameplay videos. The models represent probabilistic relationships between elements in the game. An analogical reasoning process maps features between two models to produce blended models that can then generate new level chunks. As a proof-of-concept we train our system on the classic platformer game Super Mario Bros. due to its highly-regarded and well understood level design. We evaluate the extent to which the models represent stylistic level design knowledge and demonstrate the ability of our system to explain levels that were blended by human expert designers.