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Found 7 papers, 1 papers with code
Procedural Content Generation via Knowledge Transformation (PCG-KT)
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.
Deep Learning for Procedural Content Generation
This article surveys the various deep learning methods that have been applied to generate game content directly or indirectly, discusses deep learning methods that could be used for content generation purposes but are rarely used today, and envisages some limitations and potential future directions of deep learning for procedural content generation.
Exploring Level Blending across Platformers via Paths and Affordances
Techniques for procedural content generation via machine learning (PCGML) have been shown to be useful for generating novel game content.
Multi-Domain Level Generation and Blending with Sketches via Example-Driven BSP and Variational Autoencoders
In this paper, we present a PCGML approach for level generation that is able to recombine, adapt, and reuse structural patterns from several domains to approximate unseen domains.
Capturing Local and Global Patterns in Procedural Content Generation via Machine Learning
Recent procedural content generation via machine learning (PCGML) methods allow learning from existing content to produce similar content automatically.
Procedural Content Generation via Machine Learning (PCGML)
This survey explores Procedural Content Generation via Machine Learning (PCGML), defined as the generation of game content using machine learning models trained on existing content.
The VGLC: The Video Game Level Corpus
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.
