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Medientyp:
E-Artikel
Titel:
Game Level Generation from Gameplay Videos
Beteiligte:
Guzdial, Matthew;
Riedl, Mark
Erschienen:
Association for the Advancement of Artificial Intelligence (AAAI), 2021
Erschienen in:Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
Sprache:
Nicht zu entscheiden
DOI:
10.1609/aiide.v12i1.12861
ISSN:
2334-0924;
2326-909X
Entstehung:
Anmerkungen:
Beschreibung:
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We present an unsupervised process to generate full video game levels from a model trained on gameplay video. The model represents probabilistic relationships between shapes properties, and relates the relationships to stylistic variance within a domain. We utilize the classic platformer game Super Mario Bros. to evaluate this process due to its highly-regarded level design. We evaluate the output in comparison to other data-driven level generation techniques via a user study and demonstrate its ability to produce novel output more stylistically similar to exemplar input.
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