Interactive Evolutionary Computation in the Latent Space of Deep Learning Models for Creative Game Content Generation
Yu‐Cheng Cheng, Yanan Wang, Yan Pei · 2025
Recent game content generation using artificial intelligence (AI) has been limited by a lack of diversity in content styles, and the impact of human input on generated content has not received enough attention. To address these, we present an innovative game content generation framework for enhancing the game style of Super Mario Bros. This framework leverages the dual advantages of interactive evolutionary computation (IEC) for capturing players' implementation preferences and style-based generative adversarial network (StyleGAN) for generating high-quality and diverse solutions. Additionally, we introduce a novel human-computer interaction (HCI) console that allows users to explore the generative adversarial network's latent space, guiding content generation towards their preferences. Finally, we analyze the performance of the proposed framework from both quantitative and qualitative perspectives. The results demonstrate that the framework model significantly enhances the diversity of the generated content. Moreover, it sheds light on the role of human participation in the content creation process.