Details
| Original language | English |
|---|---|
| Pages (from-to) | 182-192 |
| Number of pages | 11 |
| Journal | IEEE Transactions on Games |
| Volume | 15 |
| Issue number | 2 |
| Early online date | 23 Feb 2022 |
| Publication status | Published - Jun 2023 |
Abstract
This work presents Wor(l)d-GAN, a method to perform data-driven Procedural Content Generation via Machine Learning in Minecraft from a single example. Based on a 3D Generative Adversarial Network (GAN) architecture, we are able to create arbitrarily sized world snippets from a given sample. Our method applies dense representations used in Natural Language Processing (NLP) in two ways. First, we propose block2vec representations based on word2vec. Secondly, we use the pretrained large language model BERT to generate representations directly from the token names. These representations make Wor(l)d-GAN independent from the number of different blocks, which can vary a lot in Minecraft, and enable the generation of larger levels. We evaluate our approach on creations from the community as well as structures generated with the Minecraft World Generator under several metrics. Wor(l)d-GAN enables its users to generate Minecraft worlds based on parts of their creations.
Keywords
- BERT, GAN, Generation, Level, Minecraft, NLP, PCG, Representation, Scales, SinGAN, Single Example, Style, generation, natural language processing (NLP), generative adversarial network (GAN), level, single example, representation, scales, procedural content generation (PCG), style
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Artificial Intelligence
- Engineering(all)
- Electrical and Electronic Engineering
- Engineering(all)
- Control and Systems Engineering
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In: IEEE Transactions on Games, Vol. 15, No. 2, 06.2023, p. 182-192.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Wor(l)d-GAN
T2 - Towards Natural Language Based PCG in Minecraft
AU - Awiszus, Maren
AU - Schubert, Frederik
AU - Rosenhahn, Bodo
N1 - This work was supported by the Federal Ministry of Education and Research (BMBF), Germany under the project LeibnizKILabor (grant no. 01DD20003) and the Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy within the Cluster of Excellence PhoenixD (EXC 2122).
PY - 2023/6
Y1 - 2023/6
N2 - This work presents Wor(l)d-GAN, a method to perform data-driven Procedural Content Generation via Machine Learning in Minecraft from a single example. Based on a 3D Generative Adversarial Network (GAN) architecture, we are able to create arbitrarily sized world snippets from a given sample. Our method applies dense representations used in Natural Language Processing (NLP) in two ways. First, we propose block2vec representations based on word2vec. Secondly, we use the pretrained large language model BERT to generate representations directly from the token names. These representations make Wor(l)d-GAN independent from the number of different blocks, which can vary a lot in Minecraft, and enable the generation of larger levels. We evaluate our approach on creations from the community as well as structures generated with the Minecraft World Generator under several metrics. Wor(l)d-GAN enables its users to generate Minecraft worlds based on parts of their creations.
AB - This work presents Wor(l)d-GAN, a method to perform data-driven Procedural Content Generation via Machine Learning in Minecraft from a single example. Based on a 3D Generative Adversarial Network (GAN) architecture, we are able to create arbitrarily sized world snippets from a given sample. Our method applies dense representations used in Natural Language Processing (NLP) in two ways. First, we propose block2vec representations based on word2vec. Secondly, we use the pretrained large language model BERT to generate representations directly from the token names. These representations make Wor(l)d-GAN independent from the number of different blocks, which can vary a lot in Minecraft, and enable the generation of larger levels. We evaluate our approach on creations from the community as well as structures generated with the Minecraft World Generator under several metrics. Wor(l)d-GAN enables its users to generate Minecraft worlds based on parts of their creations.
KW - BERT
KW - GAN
KW - Generation
KW - Level
KW - Minecraft
KW - NLP
KW - PCG
KW - Representation
KW - Scales
KW - SinGAN
KW - Single Example
KW - Style
KW - generation
KW - natural language processing (NLP)
KW - generative adversarial network (GAN)
KW - level
KW - single example
KW - representation
KW - scales
KW - procedural content generation (PCG)
KW - style
UR - http://www.scopus.com/inward/record.url?scp=85125353461&partnerID=8YFLogxK
U2 - 10.1109/tg.2022.3153206
DO - 10.1109/tg.2022.3153206
M3 - Article
AN - SCOPUS:85125353461
VL - 15
SP - 182
EP - 192
JO - IEEE Transactions on Games
JF - IEEE Transactions on Games
SN - 2475-1502
IS - 2
ER -