Procedural content generation in gaming via evolutionary algorithms

Detalhes bibliográficos
Autor(a) principal: Çetiner, Mehmet Serdar
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/152100
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
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spelling Procedural content generation in gaming via evolutionary algorithmsGenetic OperatorsOptimizationGenetic AlgorithmsProcedural GenerationMap GenerationQuest GenerationCharacter GenerationNoise MapsDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThe aim of this thesis is to investigate the possibility of creating content using the Genetic Algorithms. To this end a simple system of interconnected algorithms were developed using concepts from Role Playing Games, specifically Dungeons and Dragons to create game content as characters, quests, and encounters. To be able to produce context, subsystems of map, character, quest, and encounter generators were created. These systems or engines not only define the game space to be populated, but they also provide each other input to create maps, quests, locations, animals, and events that are sensible and coherent. Randomness of the generation was essential as such a variety of noise maps and random number generation were added to every engine in the system. Layered or singular noise maps allowed for logical assumptions to be made, like seeing camels in a location with no rain and high temperatures. With the base truth coming from a random noise map such as danger, civilisation, faction etc., each system built on top of each other can get more complex. There are several Genetic Algorithms with custom operators within the system. These algorithms take their inputs and individuals from the respective engines and tie them all to each other through their physical coordinates in the gaming space. The most impactful part of these algorithms is the Fitness Functions defined with concepts from literature or CGI. The proposed system can populate a game space with elements of desired attributes given the constraints. The output produced consists of coherently tied story beats with some attributes already set. Even in this simple level, this can allow not only game designers but anyone who wants to build any kind of fictional work.Castelli, MauroRUNÇetiner, Mehmet Serdar2023-04-24T15:48:58Z2023-04-102023-04-10T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/152100TID:203268563enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:34:28Zoai:run.unl.pt:10362/152100Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:54:47.907012Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Procedural content generation in gaming via evolutionary algorithms
title Procedural content generation in gaming via evolutionary algorithms
spellingShingle Procedural content generation in gaming via evolutionary algorithms
Çetiner, Mehmet Serdar
Genetic Operators
Optimization
Genetic Algorithms
Procedural Generation
Map Generation
Quest Generation
Character Generation
Noise Maps
title_short Procedural content generation in gaming via evolutionary algorithms
title_full Procedural content generation in gaming via evolutionary algorithms
title_fullStr Procedural content generation in gaming via evolutionary algorithms
title_full_unstemmed Procedural content generation in gaming via evolutionary algorithms
title_sort Procedural content generation in gaming via evolutionary algorithms
author Çetiner, Mehmet Serdar
author_facet Çetiner, Mehmet Serdar
author_role author
dc.contributor.none.fl_str_mv Castelli, Mauro
RUN
dc.contributor.author.fl_str_mv Çetiner, Mehmet Serdar
dc.subject.por.fl_str_mv Genetic Operators
Optimization
Genetic Algorithms
Procedural Generation
Map Generation
Quest Generation
Character Generation
Noise Maps
topic Genetic Operators
Optimization
Genetic Algorithms
Procedural Generation
Map Generation
Quest Generation
Character Generation
Noise Maps
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science
publishDate 2023
dc.date.none.fl_str_mv 2023-04-24T15:48:58Z
2023-04-10
2023-04-10T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/152100
TID:203268563
url http://hdl.handle.net/10362/152100
identifier_str_mv TID:203268563
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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