Learning Constructive Primitives for Online Level Generation and Real-time Content Adaptation in Super Mario Bros
This work addresses procedural content generation for game design, offering an incremental improvement in efficiency and controllability for Super Mario Bros.
The paper tackles online level generation and real-time content adaptation in Super Mario Bros by combining rule-based and learning-based methods to produce constructive primitives, enabling the generation of quality game levels and adaptive content for dynamic difficulty adjustment.
Procedural content generation (PCG) is of great interest to game design and development as it generates game content automatically. Motivated by the recent learning-based PCG framework and other existing PCG works, we propose an alternative approach to online content generation and adaptation in Super Mario Bros (SMB). Unlike most of existing works in SMB, our approach exploits the synergy between rule-based and learning-based methods to produce constructive primitives, quality yet controllable game segments in SMB. As a result, a complete quality game level can be generated online by integrating relevant constructive primitives via controllable parameters regarding geometrical features and procedure-level properties. Also the adaptive content can be generated in real time by dynamically selecting proper constructive primitives via an adaptation criterion, e.g., dynamic difficulty adjustment (DDA). Our approach is of several favorable properties in terms of content quality assurance, generation efficiency and controllability. Extensive simulation results demonstrate that the proposed approach can generate controllable yet quality game levels online and adaptable content for DDA in real time.