From Traditional Methods to GPT-based Models for 2D Video Game Level Procedural Content Generation: An Empirical Study

Daniel Cerezo, Isaac Triguero

Abstract: Procedural level generation in video games has made significant strides, yet achieving high-quality automated level design remains a major challenge. Over the years, techniques have evolved from simple constructive algorithms to advanced Artificial Intelligence (AI) models like Generative Adversarial Networks and Large Language Models. However, the lack of a standardised evaluation framework has hindered direct numerical comparisons and the ability to gauge true progress in the field. To address this gap, we propose an evaluation methodology to benchmark key generation techniques and explore the potential of general-purpose AI models. As a case study, we present a Super Mario Bros level generator powered by ChatGPT, leveraging general-purpose natural language for design tasks. The results show the different strengths and weaknesses of existing models, indicating that traditional algorithms still outperform the most advanced AI methods in this domain, highlighting the need for further innovation to bridge the gap.

External IDs:dblp:conf/smc/CerezoT25