The VGLC: The Video Game Level Corpus

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.

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

References in corpus (2)

• Super Mario as a String: Platformer Level Generation Via LSTMs
• Toward Game Level Generation from Gameplay Videos

Cited by in corpus (17)

• CPPN2GAN: Combining Compositional Pattern Producing Networks and GANs for Large-scale Pattern Generation
• 10 Years of the PCG workshop: Past and Future Trends
• Generating Levels That Teach Mechanics
• Controllable Level Blending between Games using Variational Autoencoders
• Lode Encoder: AI-constrained co-creativity
• Efficient Generation of Structured Objects with Constrained Adversarial Networks
• Generating Lode Runner Levels by Learning Player Paths with LSTMs
• Hybrid Encoding For Generating Large Scale Game Level Patterns With Local Variations
• Multi-Domain Level Generation and Blending with Sketches via Example-Driven BSP and Variational Autoencoders
• Tools for Landscape Analysis of Optimisation Problems in Procedural Content Generation for Games
• Explainable PCGML via Game Design Patterns
• Data-driven Design: A Case for Maximalist Game Design
• A Novel CNet-assisted Evolutionary Level Repairer and Its Applications to Super Mario Bros
• Toward Co-creative Dungeon Generation via Transfer Learning
• Conditional Level Generation and Game Blending
• Procedural Content Generation using Behavior Trees (PCGBT)
• Tile Embedding: A General Representation for Procedural Level Generation via Machine Learning