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dc.contributor.authorHervé, Jean-Baptiste
dc.contributor.authorWithington, Oliver
dc.contributor.authorHervé, Marion
dc.contributor.authorTokarchuk, Laurissa
dc.contributor.authorSalge, Christoph
dc.date.accessioned2024-03-25T13:07:18Z
dc.date.available2024-03-25T13:07:18Z
dc.date.issued2023-08-28
dc.identifier.citationHervé , J-B , Withington , O , Hervé , M , Tokarchuk , L & Salge , C 2023 , Exploring Minecraft Settlement Generators with Generative Shift Analysis . in CEUR Workshop Proceedings . vol. 3626 , AIIDEWorkshoponExperimentalArtificial Intelligence inGames, vol. 3626 , Salt Lake City , 10th Experimental Artificial Intelligence in Games Workshop at the Nineteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-23) , Salt Lake City , United States , 8/10/23 . < https://ceur-ws.org/Vol-3626/ >
dc.identifier.citationconference
dc.identifier.issn1613-0073
dc.identifier.otherArXiv: http://arxiv.org/abs/2309.05371v1
dc.identifier.urihttp://hdl.handle.net/2299/27501
dc.description© 2023 The Author(s). This is an open access conference paper distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/
dc.description.abstractWith 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.en
dc.format.extent11
dc.format.extent22672200
dc.language.isoeng
dc.relation.ispartofCEUR Workshop Proceedings
dc.relation.ispartofseriesAIIDEWorkshoponExperimentalArtificial Intelligence inGames,
dc.subjectcs.AI
dc.titleExploring Minecraft Settlement Generators with Generative Shift Analysisen
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionDepartment of Computer Science
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionCentre for Future Societies Research
dc.contributor.institutionAdaptive Systems
dc.identifier.urlhttps://ceur-ws.org/Vol-3626/
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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