Search Results for author: Christoffer Holmgård

Found 5 papers, 1 papers with code

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.

Automated Playtesting with Procedural Personas through MCTS with Evolved Heuristics

no code implementations19 Feb 2018 Christoffer Holmgård, Michael Cerny Green, Antonios Liapis, Julian Togelius

This paper describes a method for generative player modeling and its application to the automatic testing of game content using archetypal player models called procedural personas.

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

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