Re-trainable Procedural Level Generation via Machine Learning (RT-PLGML) as Game Mechanic-Reference-Cited by-同舟云学术

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Re-trainable Procedural Level Generation via Machine Learning (RT-PLGML) as Game Mechanic

Published:2023-04-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 18th International Conference on the Foundations of Digital Games
language:
Short-container-title:

Author:

Cooper Seth¹^ORCID,Halina Emily²^ORCID,Zhu Jichen³^ORCID,Guzdial Matthew²^ORCID

Affiliation:

1. Northeastern University, United States

2. University of Alberta, Canada

3. IT University of Copenhagen, Denmark

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3582437.3587210

Reference18 articles.

1. Gabor Csapo Jihyun Kim Miha Klasinc and Alia ElKattan. 2019. Survival of the Best Fit. https://www.survivalofthebestfit.com Gabor Csapo Jihyun Kim Miha Klasinc and Alia ElKattan. 2019. Survival of the Best Fit. https://www.survivalofthebestfit.com

2. Linear levels through n-grams

3. Maxim Gumin. 2016. WaveFunctionCollapse. https://github.com/mxgmn/WaveFunctionCollapse Maxim Gumin. 2016. WaveFunctionCollapse. https://github.com/mxgmn/WaveFunctionCollapse

4. Matthew Guzdial , Joshua Reno , Jonathan Chen , Gillian Smith , and Mark Riedl . 2018. Explainable PCGML via game design patterns. arXiv:1809.09419 (Sept . 2018 ). Matthew Guzdial, Joshua Reno, Jonathan Chen, Gillian Smith, and Mark Riedl. 2018. Explainable PCGML via game design patterns. arXiv:1809.09419 (Sept. 2018).

5. Threshold Designer Adaptation: Improved Adaptation for Designers in Co-creative Systems

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