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Computers
Article . 2024 . Peer-reviewed
License: CC BY
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Computers
Article . 2024
Data sources: DOAJ
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Article . 2025
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Creating a Newer and Improved Procedural Content Generation (PCG) Algorithm with Minimal Human Intervention for Computer Gaming Development

Authors: Lazaros Lazaridis; George F. Fragulis;

Creating a Newer and Improved Procedural Content Generation (PCG) Algorithm with Minimal Human Intervention for Computer Gaming Development

Abstract

Procedural content generation (PCG) algorithms have become increasingly vital in video games developed by small studios due to their ability to save time while creating diverse and engaging environments, significantly enhancing replayability by ensuring that each gameplay experience is distinct. Previous research has demonstrated the effectiveness of PCG in generating various game elements, such as levels and weaponry, with unique attributes across different playthroughs. However, these studies often face limitations in processing efficiency and adaptability to real-time applications. The current study introduces an improved spawn algorithm designed for 2D map generation, capable of creating maps with multiple room sizes and a decorative object. Unlike traditional methods that rely solely on agent-based evaluations, this constructive algorithm emphasizes reduced processing power, making it suitable for generating small worlds in real time, particularly during loading screens. Our findings highlight the algorithm’s potential to streamline game development processes, especially in resource-constrained environments, while maintaining high-quality content generation.

Related Organizations
Keywords

algorithmic map generation, Electronic computers. Computer science, computer games, resource management, QA75.5-76.95, procedural content generation (PCG), game development, replayability

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    3
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average
gold