research-article

Procedural Content Generation of Puzzle Games using Conditional Generative Adversarial Networks

Authors:

Jens Struckmann Hansen,

Jeppe Kristensen,

Paolo BurelliAuthors Info & Claims

FDG '20: Proceedings of the 15th International Conference on the Foundations of Digital Games

Article No.: 99, Pages 1 - 9

https://doi.org/10.1145/3402942.3409601

Published: 17 September 2020 Publication History

Abstract

In this article, we present an experimental approach to using parameterized Generative Adversarial Networks (GANs) to produce levels for the puzzle game Lily’s Garden1. We extract two condition-vectors from the real levels in an effort to control the details of the GAN’s outputs. While the GANs performs well in approximating the first condition (map-shape), they struggle to approximate the second condition (piece distribution). We hypothesize that this might be improved by trying out alternative architectures for both the Generator and Discriminator of the GANs.

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Digital Library

[19]

Ruben Rodriguez Torrado, Ahmed Khalifa, Michael Cerny Green, Niels Justesen, Sebastian Risi, and Julian Togelius. 2019. Bootstrapping Conditional GANs for Video Game Level Generation. (2019). arxiv:1910.01603http://arxiv.org/abs/1910.01603

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Vanessa Volz, Jacob Schrum, Jialin Liu, Simon M. Lucas, Adam Smith, and Sebastian Risi. 2018. Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network. (may 2018). arxiv:1805.00728http://arxiv.org/abs/1805.00728

Cited By

de Trenquelleon GChoukarah ARoucairol MAddoum MCazenave T(2023)Procedural Generation of Rush Hour LevelsComputers and Games10.1007/978-3-031-34017-8_15(181-190)Online publication date: 24-May-2023
https://doi.org/10.1007/978-3-031-34017-8_15
Zhang KBai JLiu J(2022)Generating Game Levels of Diverse Behaviour Engagement2022 IEEE Conference on Games (CoG)10.1109/CoG51982.2022.9893697(167-174)Online publication date: 21-Aug-2022
https://dl.acm.org/doi/10.1109/CoG51982.2022.9893697
Maurer TGuzdial M(2021)Adversarial Random Forest Classifier for Automated Game DesignProceedings of the 16th International Conference on the Foundations of Digital Games10.1145/3472538.3472587(1-6)Online publication date: 3-Aug-2021
https://dl.acm.org/doi/10.1145/3472538.3472587

Procedural Content Generation of Puzzle Games using Conditional Generative Adversarial Networks
1. Applied computing
  1. Computers in other domains
    1. Personal computers and PC applications

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cover image ACM Other conferences

FDG '20: Proceedings of the 15th International Conference on the Foundations of Digital Games

September 2020

804 pages

ISBN:9781450388078

DOI:10.1145/3402942

Editors:
Georgios N. Yannakakis
University of Malta
,
Antonios Liapis
University of Malta
,
Penny Kyburz
Australian National University
,
Vanessa Volz
Modl.AI
,
Foaad Khosmood
California Polytechnic State University
,
Phil Lopes
École Polytechnique Fédèrale de Lausanne

Copyright © 2020 ACM.

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New York, NY, United States

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Published: 17 September 2020

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FDG '20

FDG '20: International Conference on the Foundations of Digital Games

September 15 - 18, 2020

Bugibba, Malta

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Cited By

de Trenquelleon GChoukarah ARoucairol MAddoum MCazenave T(2023)Procedural Generation of Rush Hour LevelsComputers and Games10.1007/978-3-031-34017-8_15(181-190)Online publication date: 24-May-2023
https://doi.org/10.1007/978-3-031-34017-8_15
Zhang KBai JLiu J(2022)Generating Game Levels of Diverse Behaviour Engagement2022 IEEE Conference on Games (CoG)10.1109/CoG51982.2022.9893697(167-174)Online publication date: 21-Aug-2022
https://dl.acm.org/doi/10.1109/CoG51982.2022.9893697
Maurer TGuzdial M(2021)Adversarial Random Forest Classifier for Automated Game DesignProceedings of the 16th International Conference on the Foundations of Digital Games10.1145/3472538.3472587(1-6)Online publication date: 3-Aug-2021
https://dl.acm.org/doi/10.1145/3472538.3472587

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