# Procedural Content Generation via Knowledge Transformation (PCG-KT)

arXiv: https://arxiv.org/abs/2305.00644 · HTML: https://mlsanity.com/paper/2305.00644/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 89 references that are in the map.
- First posted on arXiv 1 May 2023.

## Builds on (most cited first, 29 of 29)

- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)
- [Neural Style Transfer: A Review](https://mlsanity.com/paper/1705.04058/) (2017)
- [Procedural Content Generation via Machine Learning (PCGML)](https://mlsanity.com/paper/1702.00539/) (2017)
- [Learning to Simulate Dynamic Environments with GameGAN](https://mlsanity.com/paper/2005.12126/) (2020)
- [Increasing Generality in Machine Learning through Procedural Content Generation](https://mlsanity.com/paper/1911.13071/) (2019)
- [Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics](https://mlsanity.com/paper/1706.04317/) (2017)
- [Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation](https://mlsanity.com/paper/1806.07377/) (2018)
- [General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms](https://mlsanity.com/paper/1802.10363/) (2018)
- [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)
- [Friend, Collaborator, Student, Manager: How Design of an AI-Driven Game Level Editor Affects Creators](https://mlsanity.com/paper/1901.06417/) (2019)
- [Interactive Evolution and Exploration Within Latent Level-Design Space of Generative Adversarial Networks](https://mlsanity.com/paper/2004.00151/) (2020)
- [Co-Creative Level Design via Machine Learning](https://mlsanity.com/paper/1809.09420/) (2018)
- [Conditional Level Generation and Game Blending](https://mlsanity.com/paper/2010.07735/) (2020)
- [Automated Game Design via Conceptual Expansion](https://mlsanity.com/paper/1809.02232/) (2018)
- [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)
- [Visual Transfer between Atari Games using Competitive Reinforcement Learning](https://mlsanity.com/paper/1809.00397/) (2018)
- [Evaluation of a Recommender System for Assisting Novice Game Designers](https://mlsanity.com/paper/1908.04629/) (2019)
- [Learning to Blend Computer Game Levels](https://mlsanity.com/paper/1603.02738/) (2016)
- [Mechanics Automatically Recognized via Interactive Observation: Jumping](https://mlsanity.com/paper/1707.03865/) (2017)
- [Pitako -- Recommending Game Design Elements in Cicero](https://mlsanity.com/paper/1907.03877/) (2019)
- [tile2tile: Learning Game Filters for Platformer Style Transfer](https://mlsanity.com/paper/2208.07699/) (2022)

## Built on by (most cited first, 2 of 2)

- [Multiverse: Language-Conditioned Multi-Game Level Blending via Shared Representation](https://mlsanity.com/paper/2603.26782/) (2026)
- [Expanding Horizons of Level Diversity via Multi-objective Evolutionary Learning](https://mlsanity.com/paper/2509.24341/) (2025)

## Abstract (arXiv)

We introduce the concept of Procedural Content Generation via Knowledge Transformation (PCG-KT), a new lens and framework for characterizing PCG methods and approaches in which content generation is enabled by the process of knowledge transformation -- transforming knowledge derived from one domain in order to apply it in another. Our work is motivated by a substantial number of recent PCG works that focus on generating novel content via repurposing derived knowledge. Such works have involved, for example, performing transfer learning on models trained on one game's content to adapt to another game's content, as well as recombining different generative distributions to blend the content of two or more games. Such approaches arose in part due to limitations in PCG via Machine Learning (PCGML) such as producing generative models for games lacking training data and generating content for entirely new games. In this paper, we categorize such approaches under this new lens of PCG-KT by offering a definition and framework for describing such methods and surveying existing works using this framework. Finally, we conclude by highlighting open problems and directions for future research in this area.