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Procedural content generation (PCG) is of great interest to game design anddevelopment as it generates game content automatically. Motivated by the recentlearning-based PCG framework and other existing PCG works, we propose analternative approach to online content generation and adaptation in Super MarioBros (SMB). Unlike most of existing works in SMB, our approach exploits thesynergy between rule-based and learning-based methods to produce constructiveprimitives, quality yet controllable game segments in SMB. As a result, acomplete quality game level can be generated online by integrating relevantconstructive primitives via controllable parameters regarding geometricalfeatures and procedure-level properties. Also the adaptive content can begenerated in real time by dynamically selecting proper constructive primitivesvia an adaptation criterion, e.g., dynamic difficulty adjustment (DDA). Ourapproach is of several favorable properties in terms of content qualityassurance, generation efficiency and controllability. Extensive simulationresults demonstrate that the proposed approach can generate controllable yetquality game levels online and adaptable content for DDA in real time.

Learning Constructive Primitives for Online Level Generation and Real-time Content Adaptation in Super Mario Bros