Interactive evolution and exploration within latent level-design space of generative adversarial networks Article Swipe
Jacob Schrum
,
Jake Gutierrez
,
Vanessa Volz
,
Jialin Liu
,
Simon M. Lucas
,
Sebastian Risi
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1145/3377930.3389821
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1145/3377930.3389821
Generative Adversarial Networks (GANs) are an emerging form of indirect encoding. The GAN is trained to induce a latent space on training data, and a real-valued evolutionary algorithm can search that latent space. Such Latent Variable Evolution (LVE) has recently been applied to game levels. However, it is hard for objective scores to capture level features that are appealing to players. Therefore, this paper introduces a tool for interactive LVE of tile-based levels for games. The tool also allows for direct exploration of the latent dimensions, and allows users to play discovered levels. The tool works for a variety of GAN models trained for both Super Mario Bros. and The Legend of Zelda, and is easily generalizable to other games. A user study shows that both the evolution and latent space exploration features are appreciated, with a slight preference for direct exploration, but combining these features allows users to discover even better levels. User feedback also indicates how this system could eventually grow into a commercial design tool, with the addition of a few enhancements.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1145/3377930.3389821
- OA Status
- green
- Cited By
- 8
- References
- 46
- Related Works
- 20
- OpenAlex ID
- https://openalex.org/W3014144626
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3014144626Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3377930.3389821Digital Object Identifier
- Title
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Interactive evolution and exploration within latent level-design space of generative adversarial networksWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-06-25Full publication date if available
- Authors
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Jacob Schrum, Jake Gutierrez, Vanessa Volz, Jialin Liu, Simon M. Lucas, Sebastian RisiList of authors in order
- Landing page
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https://doi.org/10.1145/3377930.3389821Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2004.00151.pdfDirect OA link when available
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Generative grammar, Computer science, Space (punctuation), Adversarial system, Variety (cybernetics), Encoding (memory), Machine learning, Artificial intelligence, Human–computer interaction, Preference, Mathematics, Statistics, Operating systemTop concepts (fields/topics) attached by OpenAlex
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8Total citation count in OpenAlex
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2023: 1, 2022: 2, 2021: 1, 2020: 4Per-year citation counts (last 5 years)
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46Number of works referenced by this work
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20Other works algorithmically related by OpenAlex
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| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
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| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | arXiv (Cornell University) |
| best_oa_location.landing_page_url | https://arxiv.org/pdf/2004.00151.pdf |
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| primary_location.is_oa | False |
| primary_location.source | |
| primary_location.license | |
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| primary_location.version | publishedVersion |
| primary_location.raw_type | proceedings-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Proceedings of the 2020 Genetic and Evolutionary Computation Conference |
| primary_location.landing_page_url | https://doi.org/10.1145/3377930.3389821 |
| publication_date | 2020-06-25 |
| publication_year | 2020 |
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