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<p>Procedural content generation (PCG) has significantly impacted game design by automating the creation of dynamic game environments, thereby saving time and effort while maintaining the freshness at each play which is required for games as a service. Recent advances in machine learning, particularly Generative Adversarial Networks (GANs), offer exciting possibilities for generating diverse and playable game levels that surpass traditional methods. Despite challenges such as training instability and ensuring playability, GANs present considerable potential for dynamic content generation. This paper explores the advantages of GAN-based approaches, addresses their limitations, and suggests improvement strategies, including combining algorithms or using solvers to mitigate poor generations. This paper explores the advantages and limitations of GAN-based approaches and suggests promising research directions to improve GAN-based procedural level generation, including combining algorithms or using solvers to mitigate poor generations. Future research directions are also identified, such as the need for user studies and improved GAN training techniques to fully harness the potential of GANs in game-level generation.</p>

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Procedural game level generation with GANs: potential, weaknesses, and unresolved challenges in the literature

  • 期刊论文

  • 发表日期：

    2025年1月18日

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  • Daniele F. Silva,
  • Rafael P. Torchelsen,
  • Marilton S. Aguiar

摘要

Procedural content generation (PCG) has significantly impacted game design by automating the creation of dynamic game environments, thereby saving time and effort while maintaining the freshness at each play which is required for games as a service. Recent advances in machine learning, particularly Generative Adversarial Networks (GANs), offer exciting possibilities for generating diverse and playable game levels that surpass traditional methods. Despite challenges such as training instability and ensuring playability, GANs present considerable potential for dynamic content generation. This paper explores the advantages of GAN-based approaches, addresses their limitations, and suggests improvement strategies, including combining algorithms or using solvers to mitigate poor generations. This paper explores the advantages and limitations of GAN-based approaches and suggests promising research directions to improve GAN-based procedural level generation, including combining algorithms or using solvers to mitigate poor generations. Future research directions are also identified, such as the need for user studies and improved GAN training techniques to fully harness the potential of GANs in game-level generation.