An Approach for the Evaluation and Correction of Manually Designed Video Game Levels Using Deep Neural Networks

Abstract

Abstract In the current state of the video game productions, most of the video game levels are created by the human operators working as level designers. This manual process is not only time-consuming and resource-intensive but also hard to guarantee uniform quality in the contents created by the level designers. One way to address this issue is to use computer-assisted level design techniques. In this paper, we have proposed a novel framework for computer-assisted video game level design that leverages neural networks, particularly generative adversarial networks (GANs) and autoencoders. The general idea is to learn over a dataset of high-quality levels and subsequently improve the ones created by the level designers. The proposed method is independent of the graphical dimensionality of the game and will work for 2D and 3D games in general. The autoencoder is used to create an intermediate representation of the level that is itself changed using the backpropagation technique according to the feedback obtained by feeding the output of the autoencoder to the discriminator component of the GAN. After performing a series of evaluations on the proposed framework and by automatically improving a series of purposefully corrupted game levels, the results demonstrate a noticeable improvement compared with the usage of simple autoencoders used to improve the video game levels in the previous researches.