# Co-Creative Level Design via Machine Learning

arXiv: https://arxiv.org/abs/1809.09420 · HTML: https://mlsanity.com/paper/1809.09420/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2018.

## Built on by (most cited first, 18 of 18)

- [Plug-and-Blend: A Framework for Controllable Story Generation with Blended Control Codes](https://mlsanity.com/paper/2104.04039/) (2021)
- [Learning Controllable 3D Level Generators](https://mlsanity.com/paper/2206.13623/) (2022)
- [CLIP meets GamePhysics: Towards bug identification in gameplay videos using zero-shot transfer learning](https://mlsanity.com/paper/2203.11096/) (2022)
- [TaikoNation: Patterning-focused Chart Generation for Rhythm Action Games](https://mlsanity.com/paper/2107.12506/) (2021)
- [Mutation Models: Learning to Generate Levels by Imitating Evolution](https://mlsanity.com/paper/2206.05497/) (2022)
- [PCGRL+: Scaling, Control and Generalization in Reinforcement Learning Level Generators](https://mlsanity.com/paper/2408.12525/) (2024)
- [Arachnophobia Exposure Therapy using Experience-driven Procedural Content Generation via Reinforcement Learning (EDPCGRL)](https://mlsanity.com/paper/2110.04146/) (2021)
- [A Systematic Review of Human-AI Co-Creativity](https://mlsanity.com/paper/2506.21333/) (2025)
- [Generating Lode Runner Levels by Learning Player Paths with LSTMs](https://mlsanity.com/paper/2107.12532/) (2021)
- [Procedural Content Generation via Knowledge Transformation (PCG-KT)](https://mlsanity.com/paper/2305.00644/) (2023)
- [Toward Co-creative Dungeon Generation via Transfer Learning](https://mlsanity.com/paper/2107.12533/) (2021)
- [The Impact of Visualizing Design Gradients for Human Designers](https://mlsanity.com/paper/2110.04147/) (2021)
- [Threshold Designer Adaptation: Improved Adaptation for Designers in Co-creative Systems](https://mlsanity.com/paper/2205.09269/) (2022)
- [Video Game Level Design as a Multi-Agent Reinforcement Learning Problem](https://mlsanity.com/paper/2510.04862/) (2025)
- [Advances in Electron Microscopy with Deep Learning](https://mlsanity.com/paper/2101.01178/) (2021)
- [Beyond Following: Mixing Active Initiative into Computational Creativity](https://mlsanity.com/paper/2409.16291/) (2024)
- [Text-to-Level Diffusion Models With Various Text Encoders for Super Mario Bros](https://mlsanity.com/paper/2507.00184/) (2025)
- [Representing and Generating Levels Over Time through Playtrace Reconstructive Partitioning](https://mlsanity.com/paper/2607.12097/) (2026)

## Abstract (arXiv)

Procedural Level Generation via Machine Learning (PLGML), the study of generating game levels with machine learning, has received a large amount of recent academic attention. For certain measures these approaches have shown success at replicating the quality of existing game levels. However, it is unclear the extent to which they might benefit human designers. In this paper we present a framework for co-creative level design with a PLGML agent. In support of this framework we present results from a user study and results from a comparative study of PLGML approaches.