Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Controllable Level Blending between Games using Variational Autoencoders   Previous work explored blending levels from existing games to create levels
for a new game that mixes properties of the original games. In this paper, we
use Variational Autoencoders (VAEs) for improving upon such techniques. VAEs
are artificial neural networks that learn and use latent representations of
datasets to generate novel outputs. We train a VAE on level data from Super
Mario Bros. and Kid Icarus, enabling it to capture the latent space spanning
both games. We then use this space to generate level segments that combine
properties of levels from both games. Moreover, by applying evolutionary search
in the latent space, we evolve level segments satisfying specific constraints.
We argue that these affordances make the VAE-based approach especially suitable
for co-creative level design and compare its performance with similar
generative models like the GAN and the VAE-GAN.

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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
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The categories of this paper are: q-bio
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The categories of this paper are: nlin
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The categories of this paper are: dg-ga
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The categories of this paper are: mtrl-th
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The categories of this paper are: patt-sol
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: math-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: ao-sci
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The categories of this paper are: q-alg
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The categories of this paper are: hep-lat
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The categories of this paper are: astro-ph
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The categories of this paper are: chao-dyn