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Procedural Content Generation (PCG) is a technique to generate complex and diverse environments in an automated way.
Learning internal representations by error propagation
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1985
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Exploiting open-endedness to solve problems through the search for novelty
Joel Lehman, Kenneth O Stanley, et al · 2008
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The mario AI framework
Ahmed. Khalifa · 2009
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Evolutionary game design
Cameron Browne and Frederic Maire · 2010
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The 2009 Mario AI competition
Julian Togelius, Sergey Karakovskiy, and Robin Baumgarten · 2010
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Abandoning Objectives: Evolution Through the Search for Novelty Alone
Joel Lehman and Kenneth O. Stanley · 2011
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Answer set programming for procedural content generation: A design space approach
Adam M Smith and Michael Mateas · 2011
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, KyungHyun Cho, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization, 2014
Diederik P. Kingma and Jimmy Ba · 2014
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Constrained novelty search: A study on game content generation
Antonios Liapis, Georgios N Yannakakis, and Julian Togelius · 2015
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Illuminating search spaces by mapping elites, 2015
Jean-Baptiste Mouret and Jeff Clune · 2015
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Dynamic time warping and geometric edit distance: Breaking the quadratic barrier, 2016
Omer Gold and Micha Sharir · 2016
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The CMA evolution strategy: A tutorial, 2016
Nikolaus Hansen · 2016
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Categorical reparameterization with gumbel-softmax, 2016
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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Procedural content generation in games
Noor Shaker, Julian Togelius, and Mark J Nelson · 2016
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Super Mario as a string: Platformer level generation via lstms, 2016
Adam Summerville and Michael Mateas · 2016
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The VGLC: The video game level corpus, 2016
Adam James Summerville, Sam Snodgrass, Michael Mateas, and Santiago Ontañón · 2016
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Open-endedness: The last grand challenge you’ve never heard of
Joel Lehman, Kenneth O. Stanley, and Lisa Soros · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding, 2018
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Doom level generation using generative adversarial networks
Edoardo Giacomello, Pier Luca Lanzi, and Daniele Loiacono · 2018
Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
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Increasing generality in machine learning through procedural content generation
Sebastian Risi and Julian Togelius · 2020
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Conditional level generation and game blending
Anurag Sarkar, Zhihan Yang, and Seth Cooper · 2020
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Input augmentation improves constrained beam search for neural machine translation: Ntt at wat 2021, 2021
Katsuki Chousa and Makoto Morishita · 2021
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Deep learning for procedural content generation
Jialin Liu, Sam Snodgrass, Ahmed Khalifa, Sebastian Risi, Georgios N Yannakakis, and Julian Togelius · 2021
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Dungeon and platformer level blending and generation using conditional vaes
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Procedural content generation via machine learning (PCGML)
Adam Summerville, Sam Snodgrass, Matthew Guzdial, Christoffer Holmgård, Amy K Hoover, Aaron Isaksen, Andy Nealen, and Julian Togelius · 2018
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Evolving Mario levels in the latent space of a deep convolutional generative adversarial network, 2018
Vanessa Volz, Jacob Schrum, Jialin Liu, Simon M. Lucas, Adam Smith, and Sebastian Risi · 2018
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Artificial intelligence and games
Georgios N Yannakakis and Julian Togelius · 2018
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How can we know what language models know?, 2019
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig · 2019
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Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension, 2019
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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Anurag Sarkar and Seth Cooper · 2021
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Generating and blending game levels via quality-diversity in the latent space of a variational autoencoder
Anurag Sarkar and Seth Cooper · 2021
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Procedural content generation using neuroevolution and novelty search for diverse video game levels
Michael Beukman, Christopher W Cleghorn, and Steven James · 2022
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Quantifying memorization across neural language models, 2022
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, and Chiyuan Zhang · 2022
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Mario plays on a manifold: Generating functional content in latent space through differential geometry, 2022
Miguel González-Duque, Rasmus Berg Palm, Søren Hauberg, and Sebastian Risi · 2022
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Preventing verbatim memorization in language models gives a false sense of privacy, 2022
Daphne Ippolito, Florian Tramèr, Milad Nasr, Chiyuan Zhang, Matthew Jagielski, Katherine Lee, Christopher A. Choquette-Choo, and Nicholas Carlini · 2022
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Evolution through large models, 2022
Joel Lehman, Jonathan Gordon, Shawn Jain, Kamal Ndousse, Cathy Yeh, and Kenneth O. Stanley · 2022
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Memorization without overfitting: Analyzing the training dynamics of large language models, 2022
Kushal Tirumala, Aram H. Markosyan, Luke Zettlemoyer, and Armen Aghajanyan · 2022
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Diff models - a new way to edit code
Herbie Bradley, Honglu Fan, Harry Saini, Reshinth Adithyan, Shivanshu Purohit, and Joel Lehman · 2023
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Level generation through large language models
Graham Todd, Sam Earle, Muhammad Umair Nasir, Michael Cerny Green, and Julian Togelius · 2023
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