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dc.contributor.authorSalge, Christoph
dc.contributor.authorGuckelsberger, Christian
dc.contributor.authorGreen, Michael Cerny
dc.contributor.authorCanaan, Rodrigo
dc.contributor.authorTogelius, Julian
dc.date.accessioned2019-06-21T00:07:49Z
dc.date.available2019-06-21T00:07:49Z
dc.date.issued2019-06-21
dc.identifier.citationSalge , C , Guckelsberger , C , Green , M C , Canaan , R & Togelius , J 2019 , ' Generative Design in Minecraft: Chronicle Challenge ' Paper presented at 10th International Conference on Computational Creativity , Charlotte , United States , 17/06/19 - 21/06/19 , pp. 5 .
dc.identifier.citationconference
dc.identifier.otherPURE: 16756367
dc.identifier.otherPURE UUID: 7f0b5121-a1c1-4eac-b103-f9229e53fc0e
dc.identifier.urihttp://hdl.handle.net/2299/21386
dc.description© 2016 ACC 2019
dc.description.abstractWe 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.en
dc.language.isoeng
dc.relation.ispartof
dc.titleGenerative Design in Minecraft: Chronicle Challengeen
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionAdaptive Systems
dc.contributor.institutionSchool of Computer Science
dc.description.statusPeer reviewed
dc.date.embargoedUntil2019-06-21
dc.identifier.urlhttps://arxiv.org/abs/1905.05888
rioxxterms.versionAM
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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