The VGLC: The Video Game Level Corpus

2016/06/23 by Adam Summerville, Adam James Summerville, Sam Snodgrass +6 · 5 citations
Computer Science · Social Sciences · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #Digital Games and Media #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Video Analysis and Summarization #cs.AI #cs.HC #cs.LG

paper · pdf · doi:10.48550/arxiv.1606.07487

To appear in proceedings of the 7th Workshop on Procedural Content Generation

openalex publication_date 2016/06/23 · arxiv created 2016/07/03 · arxiv updated 2016/07/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Levels are a key component of many different video games, and a large body of work has been produced on how to procedurally generate game levels. Recently, Machine Learning techniques have been applied to video game level generation towards the purpose of automatically generating levels that have the properties of the training corpus. Towards that end we have made available a corpora of video game levels in an easy to parse format ideal for different machine learning and other game AI research purposes.

Citations

• Super Mario as a String: Platformer Level Generation Via LSTMs

Cited by

• Text-to-Level Diffusion Models With Various Text Encoders for Super Mario Bros
• Controllable Level Blending between Games using Variational Autoencoders
• Procedural Content Generation using Behavior Trees (PCGBT)
• Conditional Level Generation and Game Blending
• Generating levels that teach mechanics

Related

• Game Generation via Large Language Models
• Level generation and style enhancement -- deep learning for game development overview
• Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network
• Deep Learning for Procedural Content Generation
• Procedural Content Generation via Machine Learning (PCGML)