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Generative adversarial networks (GANs) are quickly becoming a ubiquitous approach to procedurally generating video game levels.
Completely derandomized self-adaptation in evolution strategies
Hansen, N.; and Ostermeier, A. 2001 · 2001
Earlier work this paper cites.
Mario Level Generation From Mechanics Using Scene Stitching
Green, M. C.; Mugrai, L.; Khalifa, A.; and Togelius, J. 2020 · 2002
Earlier work this paper cites.
Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error
González-Duque, M.; Palm, R. B.; Ha, D.; and Risi, S. 2020 · 2005
Earlier work this paper cites.
Compositional Pattern Producing Networks: A Novel Abstraction of Development
Stanley, K. O. 2007 · 2007
Earlier work this paper cites.
Answer set programming for procedural content generation: A design space approach
Smith, A. M.; and Mateas, M. 2011 · 2011
Earlier work this paper cites.
Search-based procedural content generation: A taxonomy and survey
Togelius, J.; Yannakakis, G. N.; Stanley, K. O.; and Browne, C. 2011 · 2011
Earlier work this paper cites.
Generative Adversarial Nets
Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y. 2014 · 2014
Earlier work this paper cites.
A Survey on Procedural Modelling for Virtual Worlds
Smelik, R. M.; Tutenel, T.; Bidarra, R.; and Benes, B. 2014 · 2014
Earlier work this paper cites.
Experiments in map generation using Markov chains
Snodgrass, S.; and Ontañón, S. 2014 · 2014
Earlier work this paper cites.
Illuminating search spaces by mapping elites
Mouret, J.-B.; and Clune, J. 2015 · 2015
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Confronting the challenge of quality diversity
Pugh, J. K.; Soros, L. B.; Szerlip, P. A.; and Stanley, K. O. 2015 · 2015
Earlier work this paper cites.
Tutorial on variational autoencoders
Doersch, C. 2016 · 2016
Earlier work this paper cites.
Game Level Generation from Gameplay Videos
Guzdial, M.; and Riedl, M. O. 2016 · 2016
Earlier work this paper cites.
The CMA evolution strategy: A tutorial
Hansen, N. 2016 · 2016
Cited alongside, same era.
Quality Diversity: A New Frontier for Evolutionary Computation
Pugh, J. K.; Soros, L. B.; and Stanley, K. O. 2016 · 2016
Cited alongside, same era.
Procedural Content Generation in Games: A Textbook and an Overview of Current Research
Shaker, N.; Togelius, J.; and Nelson, M. J. 2016 · 2016
Cited alongside, same era.
Rapid phenotypic landscape exploration through hierarchical spatial partitioning
Smith, D.; Tokarchuk, L.; and Wiggins, G. 2016 · 2016
Cited alongside, same era.
Super mario as a string: Platformer level generation via lstms
Summerville, A.; and Mateas, M. 2016 · 2016
Cited alongside, same era.
Evolving mario levels in the latent space of a deep convolutional generative adversarial network
Volz, V.; Schrum, J.; Liu, J.; Lucas, S. M.; Smith, A.; and Risi, S. 2018 · 2018
Later among the works it cites.
Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding Boundaries
Fontaine, M. C.; Lee, S.; Soros, L. B.; de Mesentier Silva, F.; Togelius, J.; and Hoover, A. K. 2019 · 2019
Later among the works it cites.
Procedural content generation through quality diversity
Gravina, D.; Khalifa, A.; Liapis, A.; Togelius, J.; and Yannakakis, G. N. 2019 · 2019
Later among the works it cites.
Tile pattern KL-divergence for analysing and evolving game levels
Lucas, S. M.; and Volz, V. 2019 · 2019
Later among the works it cites.
TOAD-GAN: coherent style level generation from a single example
Awiszus, M.; Schubert, F.; and Rosenhahn, B. 2020 · 2020
Closest in time.
Generative Pretraining from Pixels
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Wasserstein Generative Adversarial Networks
Martin Arjovsky, S.; and Bottou, L. 2017 · 2017
Cited alongside, same era.
DeepMasterPrints: Generating masterprints for dictionary attacks via latent variable evolution
Bontrager, P.; Roy, A.; Togelius, J.; Memon, N.; and Ross, A. 2018 · 2018
Cited alongside, same era.
DOOM level generation using generative adversarial networks
Giacomello, E.; Lanzi, P. L.; and Loiacono, D. 2018 · 2018
Cited alongside, same era.
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Karras, T.; Aila, T.; Laine, S.; and Lehtinen, J. 2018 · 2018
Cited alongside, same era.
Talakat: Bullet Hell Generation Through Constrained Map-elites
Khalifa, A.; Lee, S.; Nealen, A.; and Togelius, J. 2018 · 2018
Cited alongside, same era.
Procedural content generation via machine learning (pcgml)
Summerville, A.; Snodgrass, S.; Guzdial, M.; Holmgård, C.; Hoover, A. K.; Isaksen, A.; Nealen, A.; and Togelius, J. 2018 · 2018
Cited alongside, same era.
Discovering the Elite Hypervolume by Leveraging Interspecies Correlation
Vassiliades, V.; and Mouret, J.-B. 2018 · 2018
Cited alongside, same era.
Chen, M.; Radford, A.; Child, R.; Wu, J.; Jun, H.; Dhariwal, P.; Luan, D.; and Sutskever, I. 2020 · 2020
Closest in time.
Covariance Matrix Adaptation for the Rapid Illumination of Behavior Space
Fontaine, M. C.; Togelius, J.; Nikolaidis, S.; and Hoover, A. K. 2020 · 2020
Closest in time.
Procedural Content Generation of Puzzle Games using Conditional Generative Adversarial Networks
Hald, A.; Hansen, J. S.; Kristensen, J.; and Burelli, P. 2020 · 2020
Closest in time.
On the”steerability” of generative adversarial networks
Jahanian, A.; Chai, L.; and Isola, P. 2020 · 2020
Closest in time.
Conditional Convolutional Generative Adversarial Networks Based Interactive Procedural Game Map Generation
Ping, K.; and Dingli, L. 2020 · 2020
Closest in time.
CPPN2GAN: Combining compositional pattern producing networks and gans for large-scale pattern generation
Schrum, J.; Volz, V.; and Risi, S. 2020 · 2020
Closest in time.
The unexpected consequence of incremental design changes
Sturtevant, N.; Decroocq, N.; Tripodi, A.; and Guzdial, M. 2020 · 2020
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