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- W2891635877 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." @default.
- W2891635877 created "2018-09-27" @default.
- W2891635877 creator A5023838834 @default.
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- W2891635877 date "2018-09-10" @default.
- W2891635877 modified "2023-09-23" @default.
- W2891635877 title "Addressing the Fundamental Tension of PCGML with Discriminative Learning" @default.
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