# Exploring Level Blending across Platformers via Paths and Affordances

arXiv: https://arxiv.org/abs/2009.06356 · HTML: https://mlsanity.com/paper/2009.06356/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 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)
- [Procedural Content Generation in Games: A Survey with Insights on Emerging LLM Integration](https://mlsanity.com/paper/2410.15644/) (2024)
- [Dungeon and Platformer Level Blending and Generation using Conditional VAEs](https://mlsanity.com/paper/2106.12692/) (2021)
- [Tile Embedding: A General Representation for Procedural Level Generation via Machine Learning](https://mlsanity.com/paper/2110.03181/) (2021)
- [Generating Lode Runner Levels by Learning Player Paths with LSTMs](https://mlsanity.com/paper/2107.12532/) (2021)
- [tile2tile: Learning Game Filters for Platformer Style Transfer](https://mlsanity.com/paper/2208.07699/) (2022)
- [Procedural Content Generation via Knowledge Transformation (PCG-KT)](https://mlsanity.com/paper/2305.00644/) (2023)
- [Ensemble Learning For Mega Man Level Generation](https://mlsanity.com/paper/2107.12524/) (2021)
- [Clustering-based Tile Embedding (CTE): A General Representation for Level Design with Skewed Tile Distributions](https://mlsanity.com/paper/2210.12789/) (2022)
- [Joint Level Generation and Translation Using Gameplay Videos](https://mlsanity.com/paper/2306.16662/) (2023)
- [Tree-Based Reconstructive Partitioning: A Novel Low-Data Level Generation Approach](https://mlsanity.com/paper/2309.13071/) (2023)
- [Analysis of Robustness of a Large Game Corpus](https://mlsanity.com/paper/2504.03940/) (2025)
- [Latent Combinational Game Design](https://mlsanity.com/paper/2206.14203/) (2022)
- [Game Level Blending using a Learned Level Representation](https://mlsanity.com/paper/2306.16666/) (2023)

## Abstract (arXiv)

Techniques for procedural content generation via machine learning (PCGML) have been shown to be useful for generating novel game content. While used primarily for producing new content in the style of the game domain used for training, recent works have increasingly started to explore methods for discovering and generating content in novel domains via techniques such as level blending and domain transfer. In this paper, we build on these works and introduce a new PCGML approach for producing novel game content spanning multiple domains. We use a new affordance and path vocabulary to encode data from six different platformer games and train variational autoencoders on this data, enabling us to capture the latent level space spanning all the domains and generate new content with varying proportions of the different domains.