Generative Design in Minecraft (GDMC), Settlement Generation Competition

This paper introduces the settlement generation competition for Minecraft, the first part of the Generative Design in Minecraft challenge. The settlement generation competition is about creating Artificial Intelligence (AI) agents that can produce functional, aesthetically appealing and believable settlements adapted to a given Minecraft map - ideally at a level that can compete with human created designs. The aim of the competition is to advance procedural content generation for games, especially in overcoming the challenges of adaptive and holistic PCG. The paper introduces the technical details of the challenge, but mostly focuses on what challenges this competition provides and why they are scientifically relevant.Comment: 10 pages, 5 figures, Part of the Foundations of Digital Games 2018 proceedings, as part of the workshop on Procedural Content Generatio

Generative Design in Minecraft: Chronicle Challenge

© 2016 ACC 2019We introduce the Chronicle Challenge as an optional addition to the Settlement Generation Challenge in Minecraft. One of the foci of the overall competition is adaptive procedural content generation (PCG), an arguably under-explored problem in computational creativity. In the base challenge, participants must generate new settlements that respond to and ideally interact with existing content in the world, such as the landscape or climate. The goal is to understand the underlying creative process, and to design better PCG systems. The Chronicle Challenge in particular focuses on the generation of a narrative based on the history of a generated settlement, expressed in natural language. We discuss the unique features of the Chronicle Challenge in comparison to other competitions, clarify the characteristics of a chronicle eligible for submission and describe the evaluation criteria. We furthermore draw on simulation-based approaches in computational storytelling as examples to how this challenge could be approached.Peer reviewe

Believable Minecraft Settlements by Means of Decentralised Iterative Planning

Procedural city generation that focuses on believability and adaptability to random terrain is a difficult challenge in the field of Procedural Content Generation (PCG). Dozens of researchers compete for a realistic approach in challenges such as the Generative Settlement Design in Minecraft (GDMC), in which our method has won the 2022 competition. This was achieved through a decentralised, iterative planning process that is transferable to similar generation processes that aims to produce "organic" content procedurally.Comment: 8 pages, 8 figures, to be published in "2023 IEEE Conference on Games (CoG)

An Examination of the Hidden Judging Criteria in the Generative Design in Minecraft Competition

© 2023 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TG.2023.3329763Game content has long been created using procedural generation. However, many of these systems are currently designed in an ad-hoc manner, and there is a lack of knowledge around the design criteria that lead to generators producing the most successful results. In this study, we conduct a qualitative examination of the comments left by judges for the 2018--2020 \textit{Generative Design in Minecraft} competition. Using abductive thematic analysis, we identify the core design criteria that contribute to a generator that creates ``good'' content -- here defined as interesting or engaging. By performing this study, we have identified that the core design criteria that create and interesting settlement are usability of the settlement environment, the thematic coherence within the settlement, and an anchoring in real-world simulacra.Peer reviewe

The AI Settlement Generation Challenge in Minecraft : First Year Report

© 2020 Springer-Verlag. This is a post-peer-review, pre-copyedit version of an article published in KI - Künstliche Intelligenz. The final authenticated version is available online at: https://doi.org/10.1007/s13218-020-00635-0.This article outlines what we learned from the first year of the AI Settlement Generation Competition in Minecraft, a competition about producing AI programs that can generate interesting settlements in Minecraft for an unseen map. This challenge seeks to focus research into adaptive and holistic procedural content generation. Generating Minecraft towns and villages given existing maps is a suitable task for this, as it requires the generated content to be adaptive, functional, evocative and aesthetic at the same time. Here, we present the results from the first iteration of the competition. We discuss the evaluation methodology, present the different technical approaches by the competitors, and outline the open problems.Peer reviewedFinal Accepted Versio

Exploring Minecraft Settlement Generators with Generative Shift Analysis

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

Procedural Content Generation in 3 Dimensions using Wave Function Collapse in Minecraft

This investigation into the effectiveness of Wave Function Collapse as a Procedural Content Generation Technique (PCG) in Minecraft sets out to determine whether this method can be used easily by players and game designers to generate content that mimics the original content. We also set out to determine whether this technique can be implemented by game designers or community modders easily enough to improve the default generation of settlements in Minecraft. We grade the effectiveness of our output using metrics provided by the Generative Design in Minecraft Competition in order to test whether generated content is effective. Tests were conducted on terrain that was taken from an existing Minecraft world, and featured a mixture of structures ranging from simple to complex in design meant to simulate structures that players would build near the beginning of the game. Unfortunately, our conclusion is that in it’s most basic form, Wave Function Collapse is unsuited as a PCG tool for Minecraft. During the course of our testing, we found that the run times for simple algorithms were too long to be effective, and the algorithm fails to generate content for many of the test cases regularly. In order to make it more suitable, a number of improvements are suggested including global constraints, weight balancing, and layering PCG methods. Overall, this approach has potential, but requires more work before it is a suitable replacement to current PCG methods for Minecraft settlement generation