Search Results for author: Sam Snodgrass

Found 6 papers, 1 papers with code

Deep Learning for Procedural Content Generation

no code implementations9 Oct 2020 Jialin Liu, Sam Snodgrass, Ahmed Khalifa, Sebastian Risi, Georgios N. Yannakakis, Julian Togelius

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

no code implementations22 Aug 2020 Anurag Sarkar, Adam Summerville, Sam Snodgrass, Gerard Bentley, Joseph Osborn

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

no code implementations17 Jun 2020 Sam Snodgrass, Anurag Sarkar

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

no code implementations26 May 2020 Vanessa Volz, Niels Justesen, Sam Snodgrass, Sahar Asadi, Sami Purmonen, Christoffer Holmgård, Julian Togelius, Sebastian Risi

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)

no code implementations2 Feb 2017 Adam Summerville, Sam Snodgrass, Matthew Guzdial, Christoffer Holmgård, Amy K. Hoover, Aaron Isaksen, Andy Nealen, Julian Togelius

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.

Card Games Style Transfer

The VGLC: The Video Game Level Corpus

1 code implementation23 Jun 2016 Adam James Summerville, Sam Snodgrass, Michael Mateas, Santiago Ontañón

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.

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