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In the previous chapter we introduced our first category of PCGML techniques: constraint-based PCGML. Constraint-based PCGML approaches rely on data to build a model representing the structures and relationships that are allowed and not allowed between a set of variables. But what if we want to instead capture how likely or common a relationship is and not just whether it occurs? In these cases, we can leverage probabilistic PCGML approaches.

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Probabilistic PCGML Approaches

  • 章节

  • 发表日期：

    2025年

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  • Matthew Guzdial,
  • Sam Snodgrass,
  • Adam Summerville

摘要

In the previous chapter we introduced our first category of PCGML techniques: constraint-based PCGML. Constraint-based PCGML approaches rely on data to build a model representing the structures and relationships that are allowed and not allowed between a set of variables. But what if we want to instead capture how likely or common a relationship is and not just whether it occurs? In these cases, we can leverage probabilistic PCGML approaches.