An interactive evolution strategy based deep convolutional generative adversarial network for 2D video game level procedural content generation.

Jiang, Ming; Zhang, Li

Abstract

The generation of desirable video game contents has been a challenge of games level design and production. In this research, we propose a game player flow experience driven interactive latent variable evolution strategy incorporated with a Deep Convolutional Generative Adversarial Network (DCGAN) for undertaking game content generation with respect to a 2D Super Mario video game. Since the Generative Adversarial Network (GAN) models tend to capture the high-level style of the input images by learning the latent vectors, they are used to generate game scenarios and context images in this research. However, as GANs employ arbitrary inputs for game image generation without taking specific features into account, they generate game level images in an incoherent manner without the specific playable game level properties, such as a broken pipe in the Mario game level image. In order to overcome such drawbacks, we propose a game player flow experience driven optimised mechanism with human intervention, to guide the game level content generation process so that only plausible and even enjoyable images will be generated as the candidates for the final game design and production.

Citation

JIANG, M. and ZHANG, L. 2021. An interactive evolution strategy based deep convolutional generative adversarial network for 2D video game level procedural content generation. In Proceedings of 2021 International joint conference on neural networks (IJCNN 2021), 18-22 July 2021, [virtual conference]. Piscataway: IEEE [online], article 9533847. Available from: https://doi.org/10.1109/IJCNN52387.2021.9533847

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2021 International joint conference on neural networks (IJCNN 2021)
• Start Date: Jul 18, 2021
• End Date: Jul 22, 2021
• Acceptance Date: Apr 10, 2021
• Online Publication Date: Jul 22, 2021
• Publication Date: Sep 20, 2021
• Deposit Date: Sep 27, 2021
• Publicly Available Date: Sep 28, 2021
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Series ISSN: 2161-4407
• Book Title: Proceedings of 2021 Internationa joint confernce on neural networks (IJCNN 2021)
• ISBN: 9780738133669
• DOI: https://doi.org/10.1109/ijcnn52387.2021.9533847
• Keywords: Interactive evolution strategy; Deep convolutional generative adversarial network; Procedural content generation; Video game
• Public URL: https://rgu-repository.worktribe.com/output/1465450