143 citations · 251 across the 6 of their papers we have counts for
7 papers
Procedural Content Generation via Knowledge Transformation (PCG-KT)
Anurag Sarkar, Matthew Guzdial, Sam Snodgrass +3
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 con…
Deep Learning for Procedural Content Generation
Jialin Liu, Sam Snodgrass, Ahmed Khalifa +3
Procedural content generation in video games has a long history. Existing procedural content generation methods, such as search-based, solver-based, rule-based and grammar-based me…
Exploring Level Blending across Platformers via Paths and Affordances
Anurag Sarkar, Adam Summerville, Sam Snodgrass +2
Techniques for procedural content generation via machine learning (PCGML) have been shown to be useful for generating novel game content. While used primarily for producing new con…
Multi-Domain Level Generation and Blending with Sketches via Example-Driven BSP and Variational Autoencoders
Sam Snodgrass, Anurag Sarkar
Procedural content generation via machine learning (PCGML) has demonstrated its usefulness as a content and game creation approach, and has been shown to be able to support human c…
Capturing Local and Global Patterns in Procedural Content Generation via Machine Learning
Vanessa Volz, Niels Justesen, Sam Snodgrass +5
Recent procedural content generation via machine learning (PCGML) methods allow learning from existing content to produce similar content automatically. While these approaches are…
Procedural Content Generation via Machine Learning (PCGML)
Adam Summerville, Sam Snodgrass, Matthew Guzdial +5
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…