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

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

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

EvoCraft: A New Challenge for Open-Endedness

This paper introduces EvoCraft, a framework for Minecraft designed to study open-ended algorithms. We introduce an API that provides an open-source Python interface for communicating with Minecraft to place and track blocks. In contrast to previous work in Minecraft that focused on learning to play the game, the grand challenge we pose here is to automatically search for increasingly complex artifacts in an open-ended fashion. Compared to other environments used to study open-endedness, Minecraft allows the construction of almost any kind of structure, including actuated machines with circuits and mechanical components. We present initial baseline results in evolving simple Minecraft creations through both interactive and automated evolution. While evolution succeeds when tasked to grow a structure towards a specific target, it is unable to find a solution when rewarded for creating a simple machine that moves. Thus, EvoCraft offers a challenging new environment for automated search methods (such as evolution) to find complex artifacts that we hope will spur the development of more open-ended algorithms. A Python implementation of the EvoCraft framework is available at: https://github.com/real-itu/Evocraft-py

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)