Comparing PCG metrics with Human Evaluation in Minecraft Settlement Generation
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Abstract (arXiv)
There are a range of metrics that can be applied to the artifacts produced by procedural content generation, and several of them come with qualitative claims. In this paper, we adapt a range of existing PCG metrics to generated Minecraft settlements, develop a few new metrics inspired by PCG literature, and compare the resulting measurements to existing human evaluations. The aim is to analyze how those metrics capture human evaluation scores in different categories, how the metrics generalize to another game domain, and how metrics deal with more complex artifacts. We provide an exploratory look at a variety of metrics and provide an information gain and several correlation analyses. We found some relationships between human scores and metrics counting specific elements, measuring the diversity of blocks and measuring the presence of crafting materials for the present complex blocks.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 9 of its 43 references that are in the map. First posted on arXiv 6 Jul 2021.
Builds on
9 of 43 references.
References · 9
- 2017Procedural Content Generation via Machine Learning (PCGML)cited by 137
- 2021The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priorscited by 41
- 2018Generative Design in Minecraft (GDMC), Settlement Generation Competitioncited by 22
- 2019General Video Game Rule Generationcited by 17
- 2021The AI Settlement Generation Challenge in Minecraft: First Year Reportcited by 9
- 2019Generative Design in Minecraft: Chronicle Challengecited by 8
- 2018A Continuous Information Gain Measure to Find the Most Discriminatory Problems for AI Benchmarkingcited by 6
- 2016Demonstrating the Feasibility of Automatic Game Balancingcited by 5
- 2018Exploring Gameplay With AI Agentscited by 3
Built on by
Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 1.
5 papers.
- 2024Measuring Diversity of Game Scenarioscited by 5
- 2022Automated Isovist Computation for Minecraftcited by 2
- 2023Exploring Minecraft Settlement Generators with Generative Shift Analysiscited by 2
- 2023Believable Minecraft Settlements by Means of Decentralised Iterative Planningcited by 0
- 2025Landmarks, Monuments, and Beacons: Understanding Generative Calls to Actioncited by 0
- 2025Landmarks, Monuments, and Beacons: Understanding Generative Calls to Action23 Sep 2025
- 2024Measuring Diversity of Game Scenarios15 Apr 2024
- 2023Believable Minecraft Settlements by Means of Decentralised Iterative Planning19 Sep 2023
- 2023Exploring Minecraft Settlement Generators with Generative Shift Analysis11 Sep 2023
- 2022Automated Isovist Computation for Minecraft7 Apr 2022