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Multi-Domain Level Generation and Blending with Sketches via Example-Driven BSP and Variational Autoencoders

Authors:

Anurag SarkarAuthors Info & Claims

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

Article No.: 4, Pages 1 - 11

https://doi.org/10.1145/3402942.3402948

Published: 17 September 2020 Publication History

Abstract

Procedural content generation via machine learning (PCGML) has demonstrated its usefulness as a content and game creation approach, and has been shown to be able to support human creativity. An important facet of creativity is combinational creativity or the recombination, adaptation, and reuse of ideas and concepts between and across domains. 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. We extend prior work involving example-driven Binary Space Partitioning for recombining and reusing patterns in multiple domains, and incorporate Variational Autoencoders (VAEs) for generating unseen structures. We evaluate our approach by blending across 7 domains and subsets of those domains. We show that our approach is able to blend domains together while retaining structural components. Additionally, by using different groups of training domains our approach is able to generate both 1) levels that reproduce and capture features of a target domain, and 2) levels that have vastly different properties from the input domain.

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Cited By

Sarkar AGuzdial MSnodgrass SSummerville AMachado TSmith G(2024)Procedural Content Generation via Knowledge Transformation (PCG-KT)IEEE Transactions on Games10.1109/TG.2023.327042216:1(36-50)Online publication date: Mar-2024
https://doi.org/10.1109/TG.2023.3270422
Todd GEarle SNasir MGreen MTogelius J(2023)Level Generation Through Large Language ModelsProceedings of the 18th International Conference on the Foundations of Digital Games10.1145/3582437.3587211(1-8)Online publication date: 12-Apr-2023
https://dl.acm.org/doi/10.1145/3582437.3587211
Atmaja PSugiarto (2022)Using Genetic Algorithm for Wide yet Even Scattering of Game Objects: Applications on Irregular Levels and Involving Multiple Objects2022 IEEE 8th Information Technology International Seminar (ITIS)10.1109/ITIS57155.2022.10010220(301-306)Online publication date: 19-Oct-2022
https://doi.org/10.1109/ITIS57155.2022.10010220
Show More Cited By

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Information

Published In

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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Association for Computing Machinery

New York, NY, United States

Publication History

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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Overall Acceptance Rate 152 of 415 submissions, 37%

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Cited By

Sarkar AGuzdial MSnodgrass SSummerville AMachado TSmith G(2024)Procedural Content Generation via Knowledge Transformation (PCG-KT)IEEE Transactions on Games10.1109/TG.2023.327042216:1(36-50)Online publication date: Mar-2024
https://doi.org/10.1109/TG.2023.3270422
Todd GEarle SNasir MGreen MTogelius J(2023)Level Generation Through Large Language ModelsProceedings of the 18th International Conference on the Foundations of Digital Games10.1145/3582437.3587211(1-8)Online publication date: 12-Apr-2023
https://dl.acm.org/doi/10.1145/3582437.3587211
Atmaja PSugiarto (2022)Using Genetic Algorithm for Wide yet Even Scattering of Game Objects: Applications on Irregular Levels and Involving Multiple Objects2022 IEEE 8th Information Technology International Seminar (ITIS)10.1109/ITIS57155.2022.10010220(301-306)Online publication date: 19-Oct-2022
https://doi.org/10.1109/ITIS57155.2022.10010220
Withington OTokarchuk L(2022)Compressing and Comparing the Generative Spaces of Procedural Content Generators2022 IEEE Conference on Games (CoG)10.1109/CoG51982.2022.9893615(143-150)Online publication date: 21-Aug-2022
https://dl.acm.org/doi/10.1109/CoG51982.2022.9893615
Blasco DFont JPérez FCetina C(2022)Procedural content improvement of game bosses with an evolutionary algorithmMultimedia Tools and Applications10.1007/s11042-022-13674-682:7(10277-10309)Online publication date: 25-Aug-2022
https://doi.org/10.1007/s11042-022-13674-6
Sarkar ACooper S(2021)Generating and Blending Game Levels via Quality-Diversity in the Latent Space of a Variational AutoencoderProceedings of the 16th International Conference on the Foundations of Digital Games10.1145/3472538.3472545(1-11)Online publication date: 3-Aug-2021
https://dl.acm.org/doi/10.1145/3472538.3472545
Sarkar ACooper S(2021)Dungeon and Platformer Level Blending and Generation using Conditional VAEs2021 IEEE Conference on Games (CoG)10.1109/CoG52621.2021.9619051(1-8)Online publication date: 17-Aug-2021
https://dl.acm.org/doi/10.1109/CoG52621.2021.9619051
Bhaumik DKhalifa ATogelius J(2021)Lode Encoder: AI-constrained co-creativity2021 IEEE Conference on Games (CoG)10.1109/CoG52621.2021.9619009(01-08)Online publication date: 17-Aug-2021
https://dl.acm.org/doi/10.1109/CoG52621.2021.9619009
Liu JSnodgrass SKhalifa ARisi SYannakakis GTogelius J(2021)Deep learning for procedural content generationNeural Computing and Applications10.1007/s00521-020-05383-833:1(19-37)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1007/s00521-020-05383-8
Sarkar ACooper S(2020)Sequential Segment-based Level Generation and Blending using Variational AutoencodersProceedings of the 15th International Conference on the Foundations of Digital Games10.1145/3402942.3409604(1-9)Online publication date: 15-Sep-2020
https://dl.acm.org/doi/10.1145/3402942.3409604
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