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Addressing the Fundamental Tension of PCGML with Discriminative Learning

Karth, Isaac ; Smith, Adam M.

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  • Titolo:
    Addressing the Fundamental Tension of PCGML with Discriminative Learning
  • Autore: Karth, Isaac ; Smith, Adam M.
  • Note di contenuto: 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.
  • Soggetti: Computer Science - Machine Learning ; Statistics - Machine Learning
  • Tipo: Articolo
  • Identificativo: Arxiv ID: 1809.04432
  • Fonte: Cornell University

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