Please use this identifier to cite or link to this item: https://rd.uffs.edu.br/handle/prefix/9176
Full metadata record
DC FieldValueLanguage
dc.contributor.advisor1Grando, Felipe-
dc.creatorBonelli, Djonatan Riquelme Clein-
dc.date2025-11-25-
dc.date.accessioned2026-03-30T17:43:39Z-
dc.date.available2026-
dc.date.available2026-03-30T17:43:39Z-
dc.date.issued2025-
dc.identifier.urihttps://rd.uffs.edu.br/handle/prefix/9176-
dc.description.resumoPredicting outcomes in complex multi-agent games is challenging due to imper- fect information, stochastic events, and strategic interactions. We investigate interpretable tree-based models for outcome prediction in Citadels, a strategic board game that serves as a testbed for multi-agent dynamics. Using Optuna for hyperparameter optimization, we configure Decision Trees and Random Forests, with the latter consistently outperforming single trees. Prediction accu- racy exceeds 60% in early game rounds and surpasses 90% in later rounds, while metric-specific optimization highlights trade-offs among precision, recall, and F1 score. showing that combinations of a few features can yield strong predictive signals when interaction effects are considered. Our results demonstrate that interpretable tree-based models can combine robust predictive performance with transparent explanations, offering insights into strategic behavior and informing the broader study of decision-making in complex multi-agent environments.pt_BR
dc.description.provenanceSubmitted by Biblioteca Chapeco (biblio.ch@uffs.edu.br) on 2026-03-30T15:17:40Z No. of bitstreams: 1 BONELLI.pdf: 2854682 bytes, checksum: 91bb284a8c432e77017c6c10718e9f98 (MD5)en
dc.description.provenanceApproved for entry into archive by DIONE ROSSI FARIAS (dione@uffs.edu.br) on 2026-03-30T17:43:39Z (GMT) No. of bitstreams: 1 BONELLI.pdf: 2854682 bytes, checksum: 91bb284a8c432e77017c6c10718e9f98 (MD5)en
dc.description.provenanceMade available in DSpace on 2026-03-30T17:43:39Z (GMT). No. of bitstreams: 1 BONELLI.pdf: 2854682 bytes, checksum: 91bb284a8c432e77017c6c10718e9f98 (MD5) Previous issue date: 2025en
dc.languageporpt_BR
dc.publisherUniversidade Federal da Fronteira Sulpt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentCampus Chapecópt_BR
dc.publisher.initialsUFFSpt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectInteligência artificialpt_BR
dc.subjectPrediçãopt_BR
dc.subjectJogos de tabuleiropt_BR
dc.subjectSistemas multiagentespt_BR
dc.subjectAlgoritmospt_BR
dc.titleTree-based learning for game outcome predictionpt_BR
dc.typeMonografiapt_BR
Appears in Collections:Ciência da Computação

Files in This Item:
File Description SizeFormat 
BONELLI.pdf2.79 MBAdobe PDFView/Open
Show simple item record Recommend this item


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.