Tree-Based Reconstructive Partitioning: A Novel Low-Data Level
  Generation Approach
Emily Halina Matthew Guzdial
Procedural Content Generation (PCG) is the algorithmic generation of content,
often applied to games. PCG and PCG via Machine Learning (PCGML) have appeared
in published games. However, it can prove difficult to apply these approaches
in the early stages of an in-development game. PCG requires expertise in
representing designer notions of quality in rules or functions, and PCGML
typically requires significant training data, which may not be available early
in development. In this paper, we introduce Tree-based Reconstructive
Partitioning (TRP), a novel PCGML approach aimed to address this problem. Our
results, across two domains, demonstrate that TRP produces levels that are more
playable and coherent, and that the approach is more generalizable with less
training data. We consider TRP to be a promising new approach that can afford
the introduction of PCGML into the early stages of game development without
requiring human expertise or significant training data.