Video game level repair via mixed integer linear programming

Document Type

Conference Proceeding

Publication Date

1-1-2020

Abstract

Recent advancements in procedural content generation via machine learning enable the generation of video-game levels that are aesthetically similar to human-authored examples. However, the generated levels are often unplayable without additional editing. We propose a “generate-then-repair” framework for automatic generation of playable levels adhering to specific styles. The framework constructs levels using a generative adversarial network (GAN) trained with human-authored examples and repairs them using a mixed-integer linear program (MIP) with playability constraints. A key component of the framework is computing minimum cost edits between the GAN generated level and the solution of the MIP solver, which we cast as a minimum cost network flow problem. Results show that the proposed framework generates a diverse range of playable levels, that capture the spatial relationships between objects exhibited in the human-authored levels.*

Identifier

85102272091 (Scopus)

ISBN

[9781577358497]

Publication Title

Proceedings of the 16th Aaai Conference on Artificial Intelligence and Interactive Digital Entertainment Aiide 2020

First Page

151

Last Page

158

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