2020/06/17 by Sam Snodgrass, Snodgrass, Sam, Anurag Sarkar +1 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #Computer Graphics and Visualization Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Video Analysis and Summarization #cs.AI #cs.LG
paper · pdf · doi:10.48550/arxiv.2006.09807
To appear in FDG 2020 Cite as: @inproceedings{snodgrass2020blending, title={Multi-Domain Level Generation and Blending with Sketches via Example-Driven BSP and Variational Autoencoders}, author={Snodgrass, Sam and Sarkar, Anurag}, booktitle={Proceedings of the 15th International Conference on the Foundations of Digital Games}, year={2020} }
arxiv created 2020/06/17 · openalex publication_date 2020/06/17 · arxiv updated 2020/06/18 · openalex created_date 2020/06/25 · openalex updated_date 2026/07/28
Procedural content generation via machine learning (PCGML) has demonstrated its usefulness as a content and game creation approach, and has been shown to be able to support human creativity. An important facet of creativity is combinational creativity or the recombination, adaptation, and reuse of ideas and concepts between and across domains. In this paper, we present a PCGML approach for level generation that is able to recombine, adapt, and reuse structural patterns from several domains to approximate unseen domains. We extend prior work involving example-driven Binary Space Partitioning for recombining and reusing patterns in multiple domains, and incorporate Variational Autoencoders (VAEs) for generating unseen structures. We evaluate our approach by blending across 7 domains and subsets of those domains. We show that our approach is able to blend domains together while retaining structural components. Additionally, by using different groups of training domains our approach is able to generate both 1) levels that reproduce and capture features of a target domain, and 2) levels that have vastly different properties from the input domain.