Journal of Modeling and Simulation in Electrical and Electronics Engineering

Journal of Modeling and Simulation in Electrical and Electronics Engineering

Enthusiasms, Challenges, and Future Trends in Procedural Content Generation in Games Using Machine Learning

Document Type : Research Article

Authors
Shaqayeq Saffari
Morteza Dorrigiv
Farzin Yaghmaee
Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran.
10.22075/mseee.2025.32606.1136
Abstract
-- Procedural Content Generation (PCG) in video games, defined as the automated creation of game content through algorithmic processes, has experienced significant evolution over time. This advancement enables the generation of diverse game elements with minimal designer input. Historically, the development of PCG in games has utilized various methodologies, including search-based, solver-based, rule-based, and grammar-based approaches. These techniques have played a crucial role in producing a broad spectrum of content, such as levels, maps, character models, and textures. More recently, the incorporation of artificial intelligence, notably machine learning, has revolutionized the field of content generation within the gaming industry. Leveraging vast datasets and enhanced computational capabilities, machine learning algorithms offer unprecedented opportunities for creating dynamic and immersive game environments. Despite these advancements, a significant lack of comprehensive research thoroughly examines this field from crucial perspectives. This paper analyzes machine learning applications in game content generation, focusing on key issues in the PCG domain, such as enthusiasm, current challenges, and exploring potential future trends.
Keywords
Artificial intelligence
Machine learning
Game design
Procedural content generation
Subjects
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  • Receive Date 08 December 2023
  • Revise Date 02 May 2024
  • Accept Date 16 February 2025