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Searching the Latent Space of a Generative Adversarial Network to Generate DOOM Levels

Published: 20 August 2019 Publication History

Abstract

In this work, following the same approach successfully applied to evolve Super Mario levels, we applied the CMA-ES to search the latent space of a GAN previously trained to generate DOOM levels. Combining a search algorithm with a model trained in a supervised setting, allows to take advantage from both these paradigms. From one hand, the GAN is able to generate contents exploiting the design patterns learned from all the examples it was trained from. On the other hand, the CMA-ES can effectively search this design space for specific contents that meet some given design objectives. In particular, we tested our approach evolving three very different type of levels: an arena level (i.e., few large areas), a labyrinth level (i.e., many corridors and small areas), and a complex level (i.e., a balanced mix of large and small areas). Our results show that the latent space of a GAN can be effectively searched by the CMA-ES to find DOOM levels that fit accurately the objectives but, at the same time, are also novel.

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        2019 IEEE Conference on Games (CoG)
        Aug 2019
        1060 pages

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        Published: 20 August 2019

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        • (2024)Empirical comparison of evolutionary approaches for searching the latent space of Generative Adversarial Networks for the human face generation problemProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664147(1631-1639)Online publication date: 14-Jul-2024
        • (2023)Multiobjective evolutionary search of the latent space of Generative Adversarial Networks for human face generationProceedings of the Companion Conference on Genetic and Evolutionary Computation10.1145/3583133.3596391(1768-1776)Online publication date: 15-Jul-2023
        • (2023)ChatGPT and Other Large Language Models as Evolutionary Engines for Online Interactive Collaborative Game DesignProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590351(1383-1390)Online publication date: 15-Jul-2023
        • (2022)A Dataset to Investigate First-Person Shooter PlayersExtended Abstracts of the 2022 Annual Symposium on Computer-Human Interaction in Play10.1145/3505270.3558331(51-56)Online publication date: 2-Nov-2022
        • (2021)Using multiple generative adversarial networks to build better-connected levels for mega manProceedings of the Genetic and Evolutionary Computation Conference10.1145/3449639.3459323(66-74)Online publication date: 26-Jun-2021
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        • (2021)Method for Exploring Generative Adversarial Networks (GANs) via Automatically Generated Image GalleriesProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445714(1-15)Online publication date: 6-May-2021

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