Using model-based collaborative filtering techniques to recommend the expected best strategy to defeat a simulated soccer opponent
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://repositorio.inesctec.pt/handle/123456789/3620 http://dx.doi.org/10.3233/ida-140678 |
Resumo: | How to improve the performance of a simulated soccer team using final game statistics? This is the question this research aims to answer using model-based collaborative techniques and a robotic team - FC Portugal - as a case study. After developing a framework capable of automatically calculating the final game statistics through the RoboCup log files, a feature selection algorithm was used to select the variables that most influence the final game result. In the next stage, given the statistics of the current game, we rank the strategies that obtained the maximum average of goal difference in similar past games. This is done by splitting offline past games into different k-clusters. Then, for each cluster, the expected best strategy was assigned. The online phase consists in the selection of the expected best strategy for the cluster in which the current game best fits. Regarding the final results, our approach proved that it is possible to improve the performance of a robotic team by more than 35%, even in a competitive environment such as the RoboCup 2D simulation league. |
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Using model-based collaborative filtering techniques to recommend the expected best strategy to defeat a simulated soccer opponentHow to improve the performance of a simulated soccer team using final game statistics? This is the question this research aims to answer using model-based collaborative techniques and a robotic team - FC Portugal - as a case study. After developing a framework capable of automatically calculating the final game statistics through the RoboCup log files, a feature selection algorithm was used to select the variables that most influence the final game result. In the next stage, given the statistics of the current game, we rank the strategies that obtained the maximum average of goal difference in similar past games. This is done by splitting offline past games into different k-clusters. Then, for each cluster, the expected best strategy was assigned. The online phase consists in the selection of the expected best strategy for the cluster in which the current game best fits. Regarding the final results, our approach proved that it is possible to improve the performance of a robotic team by more than 35%, even in a competitive environment such as the RoboCup 2D simulation league.2017-11-20T10:48:34Z2014-01-01T00:00:00Z2014info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/3620http://dx.doi.org/10.3233/ida-140678engAbreu,PHSilva,DCPortela,JJoão Mendes MoreiraLuís Paulo Reisinfo: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:RCAAP2023-05-15T10:19:41Zoai:repositorio.inesctec.pt:123456789/3620Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:52:06.210452Repositório 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 model-based collaborative filtering techniques to recommend the expected best strategy to defeat a simulated soccer opponent |
title |
Using model-based collaborative filtering techniques to recommend the expected best strategy to defeat a simulated soccer opponent |
spellingShingle |
Using model-based collaborative filtering techniques to recommend the expected best strategy to defeat a simulated soccer opponent Abreu,PH |
title_short |
Using model-based collaborative filtering techniques to recommend the expected best strategy to defeat a simulated soccer opponent |
title_full |
Using model-based collaborative filtering techniques to recommend the expected best strategy to defeat a simulated soccer opponent |
title_fullStr |
Using model-based collaborative filtering techniques to recommend the expected best strategy to defeat a simulated soccer opponent |
title_full_unstemmed |
Using model-based collaborative filtering techniques to recommend the expected best strategy to defeat a simulated soccer opponent |
title_sort |
Using model-based collaborative filtering techniques to recommend the expected best strategy to defeat a simulated soccer opponent |
author |
Abreu,PH |
author_facet |
Abreu,PH Silva,DC Portela,J João Mendes Moreira Luís Paulo Reis |
author_role |
author |
author2 |
Silva,DC Portela,J João Mendes Moreira Luís Paulo Reis |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Abreu,PH Silva,DC Portela,J João Mendes Moreira Luís Paulo Reis |
description |
How to improve the performance of a simulated soccer team using final game statistics? This is the question this research aims to answer using model-based collaborative techniques and a robotic team - FC Portugal - as a case study. After developing a framework capable of automatically calculating the final game statistics through the RoboCup log files, a feature selection algorithm was used to select the variables that most influence the final game result. In the next stage, given the statistics of the current game, we rank the strategies that obtained the maximum average of goal difference in similar past games. This is done by splitting offline past games into different k-clusters. Then, for each cluster, the expected best strategy was assigned. The online phase consists in the selection of the expected best strategy for the cluster in which the current game best fits. Regarding the final results, our approach proved that it is possible to improve the performance of a robotic team by more than 35%, even in a competitive environment such as the RoboCup 2D simulation league. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-01-01T00:00:00Z 2014 2017-11-20T10:48:34Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://repositorio.inesctec.pt/handle/123456789/3620 http://dx.doi.org/10.3233/ida-140678 |
url |
http://repositorio.inesctec.pt/handle/123456789/3620 http://dx.doi.org/10.3233/ida-140678 |
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 |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799131597873610752 |