综述游戏程序化生成技术，重点分析大模型如何改变内容创作方式。
Procedural Content Generation in Games: A Survey with Insights on Emerging LLM Integration
- 对比搜索、机器学习、噪声函数和大模型等生成方法
- 发现大模型显著提升内容创造的多样性与智能性
- 适合对游戏开发与AI融合感兴趣的从业者和研究者
程序化内容生成（PCG）指通过算法自动创建游戏内容，已在游戏产业和学术界发展多年，能提升玩家参与度并减轻设计负担。尽管深度学习推动了更复杂内容的生成，但大型语言模型（LLMs）的到来真正改变了PCG的发展轨迹。本文综述了多种PCG算法：基于搜索的方法、基于机器学习的方法、常用方法（如噪声函数）以及新兴的LLMs，并深入讨论了混合方法。同时，按生成内容类型与论文发表时间对方法进行比较。最后，识别现有研究的空白，提出未来研究方向。
原文摘要 · Abstract (English)
Procedural Content Generation (PCG) is defined as the automatic creation of game content using algorithms. PCG has a long history in both the game industry and the academic world. It can increase player engagement and ease the work of game designers. While recent advances in deep learning approaches in PCG have enabled researchers and practitioners to create more sophisticated content, it is the arrival of Large Language Models (LLMs) that truly disrupted the trajectory of PCG advancement. This survey explores the differences between various algorithms used for PCG, including search-based methods, machine learning-based methods, other frequently used methods (e.g., noise functions), and the newcomer, LLMs. We also provide a detailed discussion on combined methods. Furthermore, we compare these methods based on the type of content they generate and the publication dates of their respective papers. Finally, we identify gaps in the existing academic work and suggest possible directions for future research.
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