  Procedural content generation via machine learning (PCGML) is typically
framed as the task of fitting a generative model to full-scale examples of a
desired content distribution. This approach presents a fundamental tension: the
more design effort expended to produce detailed training examples for shaping a
generator, the lower the return on investment from applying PCGML in the first
place. In response, we propose the use of discriminative models (which capture
the validity of a design rather the distribution of the content) trained on
positive and negative examples. Through a modest modification of
WaveFunctionCollapse, a commercially-adopted PCG approach that we characterize
as using elementary machine learning, we demonstrate a new mode of control for
learning-based generators. We demonstrate how an artist might craft a focused
set of additional positive and negative examples by critique of the generator's
previous outputs. This interaction mode bridges PCGML with mixed-initiative
design assistance tools by working with a machine to define a space of valid
designs rather than just one new design.
