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