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Procedural content generation (PCG) is a growing field, with numerous applications in the video game industry and great potential to help create better games at a fraction of the cost of manual creation. However, much of the work in PCG is…

Recent advances in large language models (LLMs) enable compelling story generation, but connecting narrative text to playable visual environments remains an open challenge in procedural content generation (PCG). We present a lightweight…

图形学 · 计算机科学 2026-01-05 Yi-Chun Chen , Arnav Jhala

Three-dimensional building generation is vital for applications in gaming, virtual reality, and digital twins, yet current methods face challenges in producing diverse, structured, and hierarchically coherent buildings. We propose…

图形学 · 计算机科学 2025-05-08 Junming Huang , Chi Wang , Letian Li , Changxin Huang , Qiang Dai , Weiwei Xu

Recently, the emergence of large language models (LLMs) has unlocked new opportunities for procedural content generation. However, recent attempts mainly focus on level generation for specific games with defined game rules such as Super…

人工智能 · 计算机科学 2024-05-31 Chengpeng Hu , Yunlong Zhao , Jialin Liu

Search-based procedural content generation (PCG) is a well-known method for level generation in games. Its key advantage is that it is generic and able to satisfy functional constraints. However, due to the heavy computational costs to run…

人工智能 · 计算机科学 2022-08-26 Ahmed Khalifa , Michael Cerny Green , Julian Togelius

Machine learning has been a popular tool in many different fields, including procedural content generation. However, procedural content generation via machine learning (PCGML) approaches can struggle with controllability and coherence. In…

机器学习 · 计算机科学 2021-07-28 Kynan Sorochan , Jerry Chen , Yakun Yu , Matthew Guzdial

Techniques for procedural content generation via machine learning (PCGML) have been shown to be useful for generating novel game content. While used primarily for producing new content in the style of the game domain used for training,…

机器学习 · 计算机科学 2020-09-15 Anurag Sarkar , Adam Summerville , Sam Snodgrass , Gerard Bentley , Joseph Osborn

Procedural Level Generation via Machine Learning (PLGML), the study of generating game levels with machine learning, has received a large amount of recent academic attention. For certain measures these approaches have shown success at…

人工智能 · 计算机科学 2018-09-26 Matthew Guzdial , Nicholas Liao , Mark Riedl

Procedural Content Generation (PCG) is a technique to generate complex and diverse environments in an automated way. However, while generating content with PCG methods is often straightforward, generating meaningful content that reflects…

Procedural content generation via machine learning (PCGML) has demonstrated its usefulness as a content and game creation approach, and has been shown to be able to support human creativity. An important facet of creativity is combinational…

机器学习 · 计算机科学 2020-06-18 Sam Snodgrass , Anurag Sarkar

Machine learning for procedural content generation has recently become an active area of research. Levels vary in both form and function and are mostly unrelated to each other across games. This has made it difficult to assemble suitably…

人工智能 · 计算机科学 2021-08-11 Philip Bontrager , Julian Togelius

Procedural content generation (PCG) is of great interest to game design and development as it generates game content automatically. Motivated by the recent learning-based PCG framework and other existing PCG works, we propose an alternative…

人工智能 · 计算机科学 2015-11-03 Peizhi Shi , Ke Chen

The procedural generation of levels and content in video games is a challenging AI problem. Often such generation relies on an intelligent way of evaluating the content being generated so that constraints are satisfied and/or objectives…

人工智能 · 计算机科学 2019-04-22 Ahmed Khalifa , Michael Cerny Green , Gabriella Barros , Julian Togelius

In recent years, Procedural Level Generation via Machine Learning (PLGML) techniques have been applied to generate game levels with machine learning. These approaches rely on human-annotated representations of game levels. Creating…

机器学习 · 计算机科学 2021-10-08 Mrunal Jadhav , Matthew Guzdial

Procedural content generation (PCG) has made substantial progress in shaping static 2D/3D geometry, while most methods treat gameplay mechanics as auxiliary and optimize only over space. We argue that this limits controllability and…

人工智能 · 计算机科学 2026-02-24 Kaijie Xu , Clark Verbrugge

Recently, procedural content generation has exhibited considerable advancements in the domain of 2D game level generation such as Super Mario Bros. and Sokoban through large language models (LLMs). To further validate the capabilities of…

人工智能 · 计算机科学 2024-06-14 Shiying Hu , Zengrong Huang , Chengpeng Hu , Jialin Liu

Procedural Content Generation (PCG) is the algorithmic generation of content, often applied to games. PCG and PCG via Machine Learning (PCGML) have appeared in published games. However, it can prove difficult to apply these approaches in…

人工智能 · 计算机科学 2023-09-26 Emily Halina , Matthew Guzdial

Large Language Models (LLMs) have shown strong potential for narrative generation, but their use in complex, multi-layered role-playing game (RPG) worlds is still limited by issues of coherence, controllability, and structural consistency.…

计算与语言 · 计算机科学 2026-04-29 Dominik Borawski , Marta Szulc , Robert Chudy , Małgorzata Giedrowicz , Piotr Mironowicz

The design of video game levels is a complex and critical task. Levels need to elicit fun and challenge while avoiding frustration at all costs. In this paper, we present a framework to assist designers in the creation of levels for 2D…

人工智能 · 计算机科学 2018-04-25 Antonio Umberto Aramini , Pier Luca Lanzi , Daniele Loiacono

Procedural generation techniques in 3D rendering engines have revolutionized the creation of complex environments, reducing reliance on manual design. Recent approaches using Large Language Models (LLMs) for 3D scene generation show promise…

计算机视觉与模式识别 · 计算机科学 2026-02-13 Arafa Yoncalik , Wouter Jansen , Nico Huebel , Mohammad Hasan Rahmani , Jan Steckel
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