Deep learning for procedural content generation

Author:

Liu JialinORCID,Snodgrass Sam,Khalifa Ahmed,Risi Sebastian,Yannakakis Georgios N.,Togelius Julian

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Guangdong Provincial Key Laboratory

Program for Guangdong Introducing Innovative and Entrepreneurial Teams

Science and Technology Innovation Committee Foundation of Shenzhen

Shenzhen Science and Technology Program

Program for University Key Laboratory of Guangdong Province

Google Faculty Research award

National Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference152 articles.

1. Abdal R, Qin Y, Wonka P (2019) Image2StyleGAN: how to embed images into the StyleGAN latent space? In: Proceedings of the IEEE International Conference on Computer Vision, pp 4432–4441

2. Ammanabrolu P, Cheung W, Tu D, Broniec W, Riedl MO (2020) Bringing stories alive: generating interactive fiction worlds. In: Proceedings of the sixteenth annual AAAI conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020)

3. Awiszus M, Schubert F, Rosenhahn B (2020) TOAD-GAN: coherent style level generation from a single example. In: Proceedings of the sixteenth annual AAAI conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2020)

4. Bontrager P, Togelius J (2020) Fully differentiable procedural content generation through generative playing networks. arXiv preprint arXiv:200205259

5. Bontrager P, Roy A, Togelius J, Memon N, Ross A (2018) DeepMasterPrints: generating masterprints for dictionary attacks via latent variable evolution. In: 2018 IEEE 9th International Conference on Biometrics Theory. Applications and Systems (BTAS). IEEE, pp 1–9

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