Using genetic algorithms for real-time dynamic difficulty adjustment in games
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/20243 |
Resumo: | Dynamic Difficulty Adjustment is the area of research that seeks ways to balance game difficulty with challenge, making it an engaging experience for all types of players, from novice to veteran, without making it frustrating or boring. In this dissertation we propose an approach that aims to evolve agents, in this case predators, as a group and in real time, in a way that they adapt to a changing environment. We showcase our approach after using a generic genetic algorithm in two scenarios, pitting the predators vs passive prey in one scenario and pitting the predators vs aggressive prey in another, this is done to create a basis for our approach and then test our algorithm in four different scenarios, the first two are the same as the generic genetic algorithm and in the next two we switch prey in the middle of the experience progressively from passive to aggressive or vice versa. |
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Using genetic algorithms for real-time dynamic difficulty adjustment in gamesGame developmentDynamic difficulty adjustmentGenetic algorithmsNEATDesenvolvimento de jogosAdaptação dinâmica de dificuldadeAlgoritmo genéticoDynamic Difficulty Adjustment is the area of research that seeks ways to balance game difficulty with challenge, making it an engaging experience for all types of players, from novice to veteran, without making it frustrating or boring. In this dissertation we propose an approach that aims to evolve agents, in this case predators, as a group and in real time, in a way that they adapt to a changing environment. We showcase our approach after using a generic genetic algorithm in two scenarios, pitting the predators vs passive prey in one scenario and pitting the predators vs aggressive prey in another, this is done to create a basis for our approach and then test our algorithm in four different scenarios, the first two are the same as the generic genetic algorithm and in the next two we switch prey in the middle of the experience progressively from passive to aggressive or vice versa.Adaptação Dinâmica de Dificuldade é a área de pesquisa que procura formas de equilibrar a dificuldade do jogo com o desafio, tornando-o uma experiência envolvente para todos os tipos de jogadores, desde principiantes a veteranos, sem o tornar frustrante ou aborrecido. Nesta dissertação propomos uma abordagem que visa evoluir os agentes, neste caso predadores, como um grupo e em tempo real, de forma a que estes se adaptem a um ambiente em mudança. Nós mostramos a nossa abordagem depois de usar um algoritmo genético genérico em dois cenários, colocando os predadores versus presas passivas num cenário e colocando os predadores versus presas agressivas noutro, isto é feito para criar uma base para a nossa abordagem e depois testamos o nosso algoritmo em quatro cenários diferentes, os dois primeiros são os mesmos que o algoritmo genético genérico e nos dois seguintes trocamos as presas a meio da experiência progressivamente de passivas para agressivas ou vice-versa.2020-12-11T00:00:00Z2019-12-12T00:00:00Z2019-12-122019-10info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10071/20243TID:202459802engPereira, João David Oliveirainfo: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:RCAAP2023-11-09T17:47:47Zoai:repositorio.iscte-iul.pt:10071/20243Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:23:13.035723Repositório 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 |
Using genetic algorithms for real-time dynamic difficulty adjustment in games |
title |
Using genetic algorithms for real-time dynamic difficulty adjustment in games |
spellingShingle |
Using genetic algorithms for real-time dynamic difficulty adjustment in games Pereira, João David Oliveira Game development Dynamic difficulty adjustment Genetic algorithms NEAT Desenvolvimento de jogos Adaptação dinâmica de dificuldade Algoritmo genético |
title_short |
Using genetic algorithms for real-time dynamic difficulty adjustment in games |
title_full |
Using genetic algorithms for real-time dynamic difficulty adjustment in games |
title_fullStr |
Using genetic algorithms for real-time dynamic difficulty adjustment in games |
title_full_unstemmed |
Using genetic algorithms for real-time dynamic difficulty adjustment in games |
title_sort |
Using genetic algorithms for real-time dynamic difficulty adjustment in games |
author |
Pereira, João David Oliveira |
author_facet |
Pereira, João David Oliveira |
author_role |
author |
dc.contributor.author.fl_str_mv |
Pereira, João David Oliveira |
dc.subject.por.fl_str_mv |
Game development Dynamic difficulty adjustment Genetic algorithms NEAT Desenvolvimento de jogos Adaptação dinâmica de dificuldade Algoritmo genético |
topic |
Game development Dynamic difficulty adjustment Genetic algorithms NEAT Desenvolvimento de jogos Adaptação dinâmica de dificuldade Algoritmo genético |
description |
Dynamic Difficulty Adjustment is the area of research that seeks ways to balance game difficulty with challenge, making it an engaging experience for all types of players, from novice to veteran, without making it frustrating or boring. In this dissertation we propose an approach that aims to evolve agents, in this case predators, as a group and in real time, in a way that they adapt to a changing environment. We showcase our approach after using a generic genetic algorithm in two scenarios, pitting the predators vs passive prey in one scenario and pitting the predators vs aggressive prey in another, this is done to create a basis for our approach and then test our algorithm in four different scenarios, the first two are the same as the generic genetic algorithm and in the next two we switch prey in the middle of the experience progressively from passive to aggressive or vice versa. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-12-12T00:00:00Z 2019-12-12 2019-10 2020-12-11T00:00:00Z |
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/10071/20243 TID:202459802 |
url |
http://hdl.handle.net/10071/20243 |
identifier_str_mv |
TID:202459802 |
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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799134793697329152 |