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dc.contributor.authorBrown, Joseph Alexander
dc.contributor.authorAshlock, Daniel
dc.contributor.authorHoughten, Sheridan
dc.contributor.authorRomualdo, Angelo
dc.date.accessioned2022-11-11T14:17:01Z
dc.date.available2022-11-11T14:17:01Z
dc.date.issued2020-07
dc.identifier.citationJ. A. Brown, D. Ashlock, S. Houghten and A. Romualdo, "Evolutionary Graph Compression and Diffusion Methods for City Discovery in Role Playing Games," 2020 IEEE Congress on Evolutionary Computation (CEC), 2020, pp. 1-8, doi: 10.1109/CEC48606.2020.9185601.en_US
dc.identifier.urihttp://hdl.handle.net/10464/16909
dc.description.abstractCities, while exciting in their visualization and permitting several layouts, do not take into account the placement of crucial characters which might be part of the narrative. Narrative graphs, a connected graph of all potential and existing relations within a game, can enable an ability to find a Nonplayer Character (NPC) who is likely to live nearby, under the assumption that those who interact most frequently are also close in distance. We examine the use of an evolutionary graph compression method and a method using simulated diffusion to cluster features based on relational information about players to generate relationally intimate groups. This clustering can be used to generate information about the game world and cities to inform PCG as to how the connectivity of these areas is, and should be, arranged. The algorithms are validated as being human competitive.en_US
dc.publisherIEEEen_US
dc.rights.urihttps://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
dc.source2020 IEEE Congress on Evolutionary Computation (CEC)
dc.subjectGraph Compressionen_US
dc.subjectClusteringen_US
dc.titleEvolutionary Graph Compression and Diffusion Methods for City Discovery in Role Playing Gamesen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/cec48606.2020.9185601
refterms.dateFOA2022-11-11T14:17:02Z


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