Procedural Content Generation of Super Mario Levels Considering Natural Connection

Procedural content generation (PCG) is one of the major research topics in the game research field. The objective of PCG is to automatically generate reusable game content, such as levels, items, or landscapes, that satisfy specific criteria such asp layability, believability, or difficulty. To the...

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Bibliographic Details
Main Authors: Nam, SangGyu, Hsueh, Chu-Hsuan, Ikeda, Kokolo
Format: Conference Proceeding
Language:English
Subjects:
Computer science
Games
Generative adversarial networks
Maintenance engineering
Pattern matching
Software engineering
Online Access:Request full text
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Summary:Procedural content generation (PCG) is one of the major research topics in the game research field. The objective of PCG is to automatically generate reusable game content, such as levels, items, or landscapes, that satisfy specific criteria such asp layability, believability, or difficulty. To the best of our knowledge, this paper i s the first to propose a level generation approach for Super Mario Bros. (Super Mario) that considers natural connection. In this study, a Super Mario level consists of several sublevels called patterns. Without natural connectivity of patterns, the level may be unplayable or not entertaining. We employ Conditional Generative Adversarial Networks (CGAN) to generate naturally connected patterns with a Matching mechanism to repair the defective outputs obtained from CGAN. The experimental results indicate that the levels generated with the proposed CGAN and Matching technique significantly improve natural connectivity compared to randomly generated patterns. The findings c an help improve the PCG's capability to generate content for better players' enjoyment.
ISSN:2642-6579
DOI:10.1109/JCSSE58229.2023.10202001