Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Game Design via Creative Machine Learning (GDCML)   In recent years, machine learning (ML) systems have been increasingly applied
for performing creative tasks. Such creative ML approaches have seen wide use
in the domains of visual art and music for applications such as image and music
generation and style transfer. However, similar creative ML techniques have not
been as widely adopted in the domain of game design despite the emergence of
ML-based methods for generating game content. In this paper, we argue for
leveraging and repurposing such creative techniques for designing content for
games, referring to these as approaches for Game Design via Creative ML
(GDCML). We highlight existing systems that enable GDCML and illustrate how
creative ML can inform new systems via example applications and a proposed
system.

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The categories of this paper are: patt-sol
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The categories of this paper are: supr-con
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The categories of this paper are: solv-int
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The categories of this paper are: plasm-ph
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The categories of this paper are: adap-org
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The categories of this paper are: physics
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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
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The categories of this paper are: funct-an
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The categories of this paper are: astro-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: math
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-ex
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The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas