Evolving mario levels in the latent space of a deep convolutional generative adversarial network

Volz, Vanessa; Schrum, Jacob; Liu, Jialin; Lucas, Simon M.; Smith, Adam M.; Risi, Sebastian

doi:10.1145/3205455.3205517

Cited by 216 publications

(259 citation statements)

References 23 publications

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“…Super Mario Bros and The Legend of Zelda). It has been used for PCG research using autoencoders [9], generative adversarial networks (GANs) [10], long short-term memories (LSTMs) [11], multidimensional Markov chains [12, Sec. 3.3.1], and automated game design learning [13].…”

Section: Summerville Et Al Define Procedural Content Generation Via mentioning

confidence: 99%

Addressing the fundamental tension of PCGML with discriminative learning

Karth

Smith

2019

Proceedings of the 14th International Conference on the Foundations of Digital Games

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Procedural content generation via machine learning (PCGML) is typically framed as the task of fitting a generative model to full-scale examples of a desired content distribution. This approach presents a fundamental tension: the more design effort expended to produce detailed training examples for shaping a generator, the lower the return on investment from applying PCGML in the first place. In response, we propose the use of discriminative models (which capture the validity of a design rather the distribution of the content) trained on positive and negative examples. Through a modest modification of WaveFunctionCollapse, a commercially-adopted PCG approach that we characterize as using elementary machine learning, we demonstrate a new mode of control for learning-based generators. We demonstrate how an artist might craft a focused set of additional positive and negative examples by critique of the generator's previous outputs. This interaction mode bridges PCGML with mixed-initiative design assistance tools by working with a machine to define a space of valid designs rather than just one new design.

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Section: Summerville Et Al Define Procedural Content Generation Via mentioning

confidence: 99%

Addressing the fundamental tension of PCGML with discriminative learning

Karth

Smith

2019

Proceedings of the 14th International Conference on the Foundations of Digital Games

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“…One use of DL for game playing in the game production process is for game testing, where artificial agents test that levels are solvable or that the difficulty is appropriate. DL might see its most prominent use in the games industry not for playing games, but for generating game content [130] based on training on existing content [140], [158], or for modeling player experience [169].…”

Section: ) Dealing With Extremely Large Decision Spacesmentioning

confidence: 99%

Deep Learning for Video Game Playing

et al. 2020

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In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards.

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“…We predict that in the near future, generative modelling techniques from machine learning, such as Generative and Adversarial Networks (GANs) [24], will allow users to personalise their avatars to an unprecedented level or allow the creation of an unlimited variety of realistic textures and assets in games. This idea of Procedural Content Generation via Machine Learning (PCGML) [81], is a new emerging research area that has already led to promising results in generating levels for games such as Doom [23] or Super Mario [87].…”

Section: Journals Conferences and Competitionsmentioning

confidence: 99%

From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI

Risi

Preuß

2020

Künstl Intell

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This paper reviews the field of Game AI, which not only deals with creating agents that can play a certain game, but also with areas as diverse as creating game content automatically, game analytics, or player modelling. While Game AI was for a long time not very well recognized by the larger scientific community, it has established itself as a research area for developing and testing the most advanced forms of AI algorithms and articles covering advances in mastering video games such as StarCraft 2 and Quake III appear in the most prestigious journals. Because of the growth of the field, a single review cannot cover it completely. Therefore, we put a focus on important recent developments, including that advances in Game AI are starting to be extended to areas outside of games, such as robotics or the synthesis of chemicals. In this article, we review the algorithms and methods that have paved the way for these breakthroughs, report on the other important areas of Game AI research, and also point out exciting directions for the future of Game AI.

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Evolving mario levels in the latent space of a deep convolutional generative adversarial network

Cited by 216 publications

References 23 publications

Addressing the fundamental tension of PCGML with discriminative learning

Addressing the fundamental tension of PCGML with discriminative learning

Deep Learning for Video Game Playing

From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI

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