# Exploring Minecraft Settlement Generators with Generative Shift Analysis

arXiv: https://arxiv.org/abs/2309.05371 · HTML: https://mlsanity.com/paper/2309.05371/

## 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 (most cited first, 9 of 9)

- [Procedural Content Generation via Machine Learning (PCGML)](https://mlsanity.com/paper/1702.00539/) (2017)
- [Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation](https://mlsanity.com/paper/1806.10729/) (2018)
- [Generative Design in Minecraft (GDMC), Settlement Generation Competition](https://mlsanity.com/paper/1803.09853/) (2018)
- [General Video Game Rule Generation](https://mlsanity.com/paper/1906.05160/) (2019)
- [The Right Variety: Improving Expressive Range Analysis with Metric Selection Methods](https://mlsanity.com/paper/2304.02366/) (2023)
- [Comparing PCG metrics with Human Evaluation in Minecraft Settlement Generation](https://mlsanity.com/paper/2107.02457/) (2021)
- [Automated Isovist Computation for Minecraft](https://mlsanity.com/paper/2204.03752/) (2022)
- [Compressing and Comparing the Generative Spaces of Procedural Content Generators](https://mlsanity.com/paper/2205.15133/) (2022)
- [Visualising Generative Spaces Using Convolutional Neural Network Embeddings](https://mlsanity.com/paper/2210.17464/) (2022)

## Built on by (most cited first, 2 of 2)

- [Level Generation with Constrained Expressive Range](https://mlsanity.com/paper/2504.05334/) (2025)
- [Landmarks, Monuments, and Beacons: Understanding Generative Calls to Action](https://mlsanity.com/paper/2509.19030/) (2025)

## 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.