  Procedural content generation via machine learning (PCGML) has demonstrated
its usefulness as a content and game creation approach, and has been shown to
be able to support human creativity. An important facet of creativity is
combinational creativity or the recombination, adaptation, and reuse of ideas
and concepts between and across domains. In this paper, we present a PCGML
approach for level generation that is able to recombine, adapt, and reuse
structural patterns from several domains to approximate unseen domains. We
extend prior work involving example-driven Binary Space Partitioning for
recombining and reusing patterns in multiple domains, and incorporate
Variational Autoencoders (VAEs) for generating unseen structures. We evaluate
our approach by blending across $7$ domains and subsets of those domains. We
show that our approach is able to blend domains together while retaining
structural components. Additionally, by using different groups of training
domains our approach is able to generate both 1) levels that reproduce and
capture features of a target domain, and 2) levels that have vastly different
properties from the input domain.
