# Latent Combinational Game Design

arXiv: https://arxiv.org/abs/2206.14203 · HTML: https://mlsanity.com/paper/2206.14203/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 44 references that are in the map.
- First posted on arXiv 28 Jun 2022.

## Builds on (most cited first, 21 of 21)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders](https://mlsanity.com/paper/1611.02648/) (2016)
- [Procedural Content Generation via Machine Learning (PCGML)](https://mlsanity.com/paper/1702.00539/) (2017)
- [Attribute2Image: Conditional Image Generation from Visual Attributes](https://mlsanity.com/paper/1512.00570/) (2015)
- [Procedural Content Generation through Quality Diversity](https://mlsanity.com/paper/1907.04053/) (2019)
- [The VGLC: The Video Game Level Corpus](https://mlsanity.com/paper/1606.07487/) (2016)
- [Super Mario as a String: Platformer Level Generation Via LSTMs](https://mlsanity.com/paper/1603.00930/) (2016)
- [Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational Autoencoder](https://mlsanity.com/paper/2102.12463/) (2021)
- [Conditional Level Generation and Game Blending](https://mlsanity.com/paper/2010.07735/) (2020)
- [Tile Pattern KL-Divergence for Analysing and Evolving Game Levels](https://mlsanity.com/paper/1905.05077/) (2019)
- [Gaussian Mixture Generative Adversarial Networks for Diverse Datasets, and the Unsupervised Clustering of Images](https://mlsanity.com/paper/1808.10356/) (2018)
- [Open-Set Recognition with Gaussian Mixture Variational Autoencoders](https://mlsanity.com/paper/2006.02003/) (2020)
- [Controllable Level Blending between Games using Variational Autoencoders](https://mlsanity.com/paper/2002.11869/) (2020)
- [Exploring Level Blending across Platformers via Paths and Affordances](https://mlsanity.com/paper/2009.06356/) (2020)
- [Towards Game Design via Creative Machine Learning (GDCML)](https://mlsanity.com/paper/2008.13548/) (2020)
- [Conceptual Game Expansion](https://mlsanity.com/paper/2002.09636/) (2020)
- [Multi-Domain Level Generation and Blending with Sketches via Example-Driven BSP and Variational Autoencoders](https://mlsanity.com/paper/2006.09807/) (2020)
- [Dungeon and Platformer Level Blending and Generation using Conditional VAEs](https://mlsanity.com/paper/2106.12692/) (2021)
- [Game Level Clustering and Generation using Gaussian Mixture VAEs](https://mlsanity.com/paper/2009.09811/) (2020)
- [Mechanics Automatically Recognized via Interactive Observation: Jumping](https://mlsanity.com/paper/1707.03865/) (2017)

## Abstract (arXiv)

We present latent combinational game design -- an approach for generating playable games that blend a given set of games in a desired combination using deep generative latent variable models. We use Gaussian Mixture Variational Autoencoders (GMVAEs) which model the VAE latent space via a mixture of Gaussian components. Through supervised training, each component encodes levels from one game and lets us define blended games as linear combinations of these components. This enables generating new games that blend the input games as well as controlling the relative proportions of each game in the blend. We also extend prior blending work using conditional VAEs and compare against the GMVAE and additionally introduce a hybrid conditional GMVAE (CGMVAE) architecture which lets us generate whole blended levels and layouts. Results show that these approaches can generate playable games that blend the input games in specified combinations. We use both platformers and dungeon-based games to demonstrate our results.