Exploring Minecraft Settlement Generators with Generative Shift Analysis

• URL: http://arxiv.org/abs/2309.05371v1
• Date: Mon, 11 Sep 2023 10:48:42 GMT
• Title: Exploring Minecraft Settlement Generators with Generative Shift Analysis
• Authors: Jean-Baptiste Herv\'e, Oliver Withington, Marion Herv\'e, Laurissa Tokarchuk, Christoph Salge
• Abstract summary: We introduce a novel method 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) 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
• Score: 1.591012510488751
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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