Exploring Minecraft Settlement Generators with Generative Shift Analysis
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Abstract (arXiv)
With growing interest in Procedural Content Generation (PCG) it becomes increasingly important to develop methods and tools for evaluating and comparing alternative systems. There is a particular lack regarding the evaluation of generative pipelines, where a set of generative systems work in series to make iterative changes to an artifact. We introduce a novel method called Generative Shift for evaluating the impact of individual stages in a PCG pipeline by quantifying the impact that a generative process has when it is applied to a pre-existing artifact. We explore this technique by applying it to a very rich dataset of Minecraft game maps produced by a set of alternative settlement generators developed as part of the Generative Design in Minecraft Competition (GDMC), all of which are designed to produce appropriate settlements for a pre-existing map. While this is an early exploration of this technique we find it to be a promising lens to apply to PCG evaluation, and we are optimistic about the potential of Generative Shift to be a domain-agnostic method for evaluating generative pipelines.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 41 references that are in the map. First posted on arXiv 11 Sep 2023.
Builds on
9 of 41 references.
References · 9
- 2017Procedural Content Generation via Machine Learning (PCGML)cited by 137
- 2018Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generationcited by 81
- 2018Generative Design in Minecraft (GDMC), Settlement Generation Competitioncited by 22
- 2019General Video Game Rule Generationcited by 17
- 2023The Right Variety: Improving Expressive Range Analysis with Metric Selection Methodscited by 6
- 2021Comparing PCG metrics with Human Evaluation in Minecraft Settlement Generationcited by 5
- 2022Automated Isovist Computation for Minecraftcited by 2
- 2022Compressing and Comparing the Generative Spaces of Procedural Content Generatorscited by 1
- 2022Visualising Generative Spaces Using Convolutional Neural Network Embeddingscited by 1
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Citing papers per year: 2025: 2.
2 papers.