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arXiv · 2010.06627

Video Game Level Repair via Mixed Integer Linear Programming

Hejia Zhang·Matthew C. Fontaine·Amy K. Hoover·Julian Togelius·Bistra Dilkina·Stefanos Nikolaidis
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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.

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Hejia Zhang, Matthew C. Fontaine, Amy K. Hoover, Julian Togelius, Bistra Dilkina, Stefanos Nikolaidis. 2020-10-13. Video Game Level Repair via Mixed Integer Linear Programming. https://arxiv.org/abs/2010.06627

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