Addressing the Fundamental Tension of PCGML with Discriminative Learning

Abstract--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. Procedural Content Generation via Machine Learning (PCGML) is the recent term for the strategy of controlling content generators using examples [1]. Existing PCGML approaches train their statistical models based on preexisting artist-provided samples of the desired content. However, there is a fundamental tension here: machine learning often works better with more training data, but the effort to produce quality training data is frequently costly enough that the artists might be better off just making the content themselves. Rather than attempting to train a generative statistical model (capturing the distribution of desired content), we focus on applying discriminative learning. In discriminative learning, the model learns to judge whether a candidate content artifact would be valid or desirable, but it does not learn how to generate candidates. Pairing a discriminative model with a preexisting content generator, we realize example-driven generation that can be influenced by both positive and negative examples of valid design patterns.