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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

This survey explores Procedural Content Generation via Machine Learning (PCGML), defined as the generation of game content using machine learning models trained on existing content. As the importance of PCG for game development increases,…

Recent procedural content generation via machine learning (PCGML) methods allow learning from existing content to produce similar content automatically. While these approaches are able to generate content for different games (e.g. Super…

Procedural content generation via machine learning (PCGML) is the process of procedurally generating game content using models trained on existing game content. PCGML methods can struggle to capture the true variance present in underlying…

机器学习 · 计算机科学 2021-07-28 Bowei Li , Ruohan Chen , Yuqing Xue , Ricky Wang , Wenwen Li , Matthew Guzdial

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

Procedural Content Generation via Machine Learning (PCGML) faces a significant hurdle that sets it apart from other fields, such as image or text generation, which is limited annotated data. Many existing methods for procedural level…

计算机视觉与模式识别 · 计算机科学 2023-06-30 Negar Mirgati , Matthew Guzdial

Procedural content generation via Machine Learning (PCGML) is the umbrella term for approaches that generate content for games via machine learning. One of the benefits of PCGML is that, unlike search or grammar-based PCG, it does not…

人工智能 · 计算机科学 2018-09-26 Matthew Guzdial , Joshua Reno , Jonathan Chen , Gillian Smith , Mark Riedl

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

Co-creative Procedural Content Generation via Machine Learning (PCGML) refers to systems where a PCGML agent and a human work together to produce output content. One of the limitations of co-creative PCGML is that it requires co-creative…

机器学习 · 计算机科学 2021-07-28 Zisen Zhou , Matthew Guzdial

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

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…

人工智能 · 计算机科学 2025-02-06 Mahdi Farrokhi Maleki , Richard Zhao

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

We investigate how reinforcement learning can be used to train level-designing agents. This represents a new approach to procedural content generation in games, where level design is framed as a game, and the content generator itself is…

机器学习 · 计算机科学 2020-08-14 Ahmed Khalifa , Philip Bontrager , Sam Earle , Julian Togelius

The paper presents the PCGPT framework, an innovative approach to procedural content generation (PCG) using offline reinforcement learning and transformer networks. PCGPT utilizes an autoregressive model based on transformers to generate…

机器学习 · 计算机科学 2023-10-05 Sajad Mohaghegh , Mohammad Amin Ramezan Dehnavi , Golnoosh Abdollahinejad , Matin Hashemi

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) 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 Reinforcement Learning (PCGRL) offers a method for training controllable level designer agents without the need for human datasets, using metrics that serve as proxies for level quality as rewards. Existing…

人工智能 · 计算机科学 2025-10-07 Sam Earle , Zehua Jiang , Eugene Vinitsky , Julian Togelius

We address the problem of game level repair, which consists of taking a designed but non-functional game level and making it functional. This might consist of ensuring the completeness of the level, reachability of objects, or other…

人工智能 · 计算机科学 2025-06-25 Debosmita Bhaumik , Julian Togelius , Georgios N. Yannakakis , Ahmed Khalifa

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…

The evaluation of procedural content generation (PCG) systems for generating video game levels is a complex and contested topic. Ideally, the field would have access to robust, generalisable and widely accepted evaluation approaches that…

人机交互 · 计算机科学 2024-04-30 Oliver Withington , Michael Cook , Laurissa Tokarchuk
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