DOOM Level Generation Using Generative Adversarial Networks

Giacomello, Edoardo; Lanzi, Pier Luca; Loiacono, Daniele

doi:10.1109/gem.2018.8516539

Cited by 54 publications

(38 citation statements)

References 17 publications

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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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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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“…They trained two GANs: one using plain level images, one using both the images and some of the features. Their results showed that GANs could generate structure of DOOM levels in first person shooter games [34].…”

Section: Related Workmentioning

confidence: 99%

Game Sprite Generator Using a Multi Discriminator GAN

2019

KSII TIIS

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This paper proposes an image generation method using a Multi Discriminator Generative Adversarial Net (MDGAN) as a next generation 2D game sprite creation technique. The proposed GAN is an Autoencoder-based model that receives three areas of information-color, shape, and animation, and combines them into new images. This model consists of two encoders that extract color and shape from each image, and a decoder that takes all the values of each encoder and generates an animated image. We also suggest an image processing technique during the learning process to remove the noise of the generated images. The resulting images show that 2D sprites in games can be generated by independently learning the three image attributes of shape, color, and animation. The proposed system can increase the productivity of massive 2D image modification work during the game development process. The experimental results demonstrate that our MDGAN can be used for 2D image sprite generation and modification work with little manual cost.

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“…With the recent advancement of deep learning technology, various deep learning technologies are influencing game development. The GAN algorithm adopted in level generation has been applied on a pilot basis to a large number of dungeons and level generation [1][2][3]. Convolution neural network (CNN) or parameter-based reinforcement learning algorithms applied to nonplayable characters (NPC) are used to develop NPC that can learn user behavior patterns [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Instance Segmentation Method of User Interface Component of Games

Kang

Choi

2020

Applied Sciences

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On the game screen, the UI interface provides key information for game play. A vision deep learning network exploits pure pixel information in the screen. Apart from this, if we separately extract the information provided by the UI interface and use it as an additional input value, we can enhance the learning efficiency of deep learning networks. To this end, by effectively segmenting UI interface components such as buttons, image icons, and gauge bars on the game screen, we should be able to separately analyze only the relevant images. In this paper, we propose a methodology that segments UI components in a game by using synthetic game images created on a game engine. We developed a tool that approximately detected the UI areas of an image in games on the game screen and generated a large amount of synthetic labeling data through this. By training this data on a Pix2Pix, we applied UI segmentation. The network trained in this way can segment the UI areas of the target game regardless of the position of the corresponding UI components. Our methodology can help analyze the game screen without applying data augmentation to the game screen. It can also help vision researchers who should extract semantic information from game image data.

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DOOM Level Generation Using Generative Adversarial Networks

Cited by 54 publications

References 17 publications

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

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

Game Sprite Generator Using a Multi Discriminator GAN

Instance Segmentation Method of User Interface Component of Games

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