An Interactive Evolution Strategy based Deep Convolutional Generative Adversarial Network for 2D Video Game Level Procedural Content Generation

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.