# Open-Ended Evolution for Minecraft Building Generation

arXiv: https://arxiv.org/abs/2209.03108 · HTML: https://mlsanity.com/paper/2209.03108/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 49 references that are in the map.
- First posted on arXiv 7 Sep 2022.

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

- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Illuminating search spaces by mapping elites](https://mlsanity.com/paper/1504.04909/) (2015)
- [Procedural Content Generation via Machine Learning (PCGML)](https://mlsanity.com/paper/1702.00539/) (2017)
- [Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network](https://mlsanity.com/paper/1805.00728/) (2018)
- [Autonomous skill discovery with Quality-Diversity and Unsupervised Descriptors](https://mlsanity.com/paper/1905.11874/) (2019)
- [Procedural Content Generation through Quality Diversity](https://mlsanity.com/paper/1907.04053/) (2019)
- [Super Mario as a String: Platformer Level Generation Via LSTMs](https://mlsanity.com/paper/1603.00930/) (2016)
- [Data-Efficient Design Exploration through Surrogate-Assisted Illumination](https://mlsanity.com/paper/1806.05865/) (2018)
- [Growing 3D Artefacts and Functional Machines with Neural Cellular Automata](https://mlsanity.com/paper/2103.08737/) (2021)
- [Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros Study](https://mlsanity.com/paper/2106.15877/) (2021)
- [Generative Design in Minecraft (GDMC), Settlement Generation Competition](https://mlsanity.com/paper/1803.09853/) (2018)
- [Tile Pattern KL-Divergence for Analysing and Evolving Game Levels](https://mlsanity.com/paper/1905.05077/) (2019)
- [World-GAN: a Generative Model for Minecraft Worlds](https://mlsanity.com/paper/2106.10155/) (2021)
- [Transforming Exploratory Creativity with DeLeNoX](https://mlsanity.com/paper/2103.11715/) (2021)
- [Evolutionary Innovations and Where to Find Them: Routes to Open-Ended Evolution in Natural and Artificial Systems](https://mlsanity.com/paper/1806.01883/) (2018)
- [Organic Building Generation in Minecraft](https://mlsanity.com/paper/1906.05094/) (2019)
- [The AI Settlement Generation Challenge in Minecraft: First Year Report](https://mlsanity.com/paper/2103.14950/) (2021)
- [Autoencoder-augmented Neuroevolution for Visual Doom Playing](https://mlsanity.com/paper/1707.03902/) (2017)
- [Generative Design in Minecraft: Chronicle Challenge](https://mlsanity.com/paper/1905.05888/) (2019)
- [Expressivity of Parameterized and Data-driven Representations in Quality Diversity Search](https://mlsanity.com/paper/2105.04247/) (2021)
- [Seeding Diversity into AI Art](https://mlsanity.com/paper/2205.00804/) (2022)

## Built on by (most cited first, 9 of 9)

- [The Procedural Content Generation Benchmark: An Open-source Testbed for Generative Challenges in Games](https://mlsanity.com/paper/2503.21474/) (2025)
- [Measuring Diversity of Game Scenarios](https://mlsanity.com/paper/2404.15192/) (2024)
- [Evolving Flying Machines in Minecraft Using Quality Diversity](https://mlsanity.com/paper/2302.00782/) (2023)
- [Hierarchically Composing Level Generators for the Creation of Complex Structures](https://mlsanity.com/paper/2302.01561/) (2023)
- [Augmentative Topology Agents For Open-Ended Learning](https://mlsanity.com/paper/2210.11442/) (2022)
- [3D Building Generation in Minecraft via Large Language Models](https://mlsanity.com/paper/2406.08751/) (2024)
- [MineAnyBuild: Benchmarking Spatial Planning for Open-world AI Agents](https://mlsanity.com/paper/2505.20148/) (2025)
- [Generating Redstone Style Cities in Minecraft](https://mlsanity.com/paper/2307.09777/) (2023)
- [Evolutionary Machine Learning and Games](https://mlsanity.com/paper/2311.16172/) (2023)

## Abstract (arXiv)

This paper proposes a procedural content generator which evolves Minecraft buildings according to an open-ended and intrinsic definition of novelty. To realize this goal we evaluate individuals' novelty in the latent space using a 3D autoencoder, and alternate between phases of exploration and transformation. During exploration the system evolves multiple populations of CPPNs through CPPN-NEAT and constrained novelty search in the latent space (defined by the current autoencoder). We apply a set of repair and constraint functions to ensure candidates adhere to basic structural rules and constraints during evolution. During transformation, we reshape the boundaries of the latent space to identify new interesting areas of the solution space by retraining the autoencoder with novel content. In this study we evaluate five different approaches for training the autoencoder during transformation and its impact on populations' quality and diversity during evolution. Our results show that by retraining the autoencoder we can achieve better open-ended complexity compared to a static model, which is further improved when retraining using larger datasets of individuals with diverse complexities.