ITU

Procedural Content Generation of Puzzle Games using Conditional Generative Adversarial Networks

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

Standard

Procedural Content Generation of Puzzle Games using Conditional Generative Adversarial Networks. / Hald, Andreas; Stuckermann Hansen, Jens; Kristensen, Jeppe Theiss; Burelli, Paolo.

FDG '20: International Conference on the Foundations of Digital Games. Association for Computing Machinery, 2020. 99.

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

Harvard

Hald, A, Stuckermann Hansen, J, Kristensen, JT & Burelli, P 2020, Procedural Content Generation of Puzzle Games using Conditional Generative Adversarial Networks. in FDG '20: International Conference on the Foundations of Digital Games., 99, Association for Computing Machinery, Foundations of Digital Games, 15/09/2020. https://doi.org/10.1145/3402942.3409601

APA

Hald, A., Stuckermann Hansen, J., Kristensen, J. T., & Burelli, P. (2020). Procedural Content Generation of Puzzle Games using Conditional Generative Adversarial Networks. In FDG '20: International Conference on the Foundations of Digital Games [99] Association for Computing Machinery. https://doi.org/10.1145/3402942.3409601

Vancouver

Hald A, Stuckermann Hansen J, Kristensen JT, Burelli P. Procedural Content Generation of Puzzle Games using Conditional Generative Adversarial Networks. In FDG '20: International Conference on the Foundations of Digital Games. Association for Computing Machinery. 2020. 99 https://doi.org/10.1145/3402942.3409601

Author

Hald, Andreas ; Stuckermann Hansen, Jens ; Kristensen, Jeppe Theiss ; Burelli, Paolo. / Procedural Content Generation of Puzzle Games using Conditional Generative Adversarial Networks. FDG '20: International Conference on the Foundations of Digital Games. Association for Computing Machinery, 2020.

Bibtex

@inproceedings{390fd2a89fc1463098c7e4691cf744ef,
title = "Procedural Content Generation of Puzzle Games using Conditional Generative Adversarial Networks",
abstract = "In this article, we present an experimental approach to using parameterized Generative Adversarial Networks (GANs) to produce levels for the puzzle game Lily{\textquoteright}s Garden1. We extract two condition-vectors from the real levels in an effort to control the details of the GAN{\textquoteright}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.",
author = "Andreas Hald and {Stuckermann Hansen}, Jens and Kristensen, {Jeppe Theiss} and Paolo Burelli",
year = "2020",
doi = "10.1145/3402942.3409601",
language = "English",
booktitle = "FDG '20: International Conference on the Foundations of Digital Games",
publisher = "Association for Computing Machinery",
address = "United States",
note = "Foundations of Digital Games, FDG ; Conference date: 15-09-2020 Through 18-09-2020",
url = "http://fdg2020.org",

}

RIS

TY - GEN

T1 - Procedural Content Generation of Puzzle Games using Conditional Generative Adversarial Networks

AU - Hald, Andreas

AU - Stuckermann Hansen, Jens

AU - Kristensen, Jeppe Theiss

AU - Burelli, Paolo

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

U2 - 10.1145/3402942.3409601

DO - 10.1145/3402942.3409601

M3 - Article in proceedings

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

PB - Association for Computing Machinery

T2 - Foundations of Digital Games

Y2 - 15 September 2020 through 18 September 2020

ER -

ID: 85512878