Please use this identifier to cite or link to this item: https://rd.uffs.edu.br/handle/prefix/9196
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dc.contributor.advisor1Grando, Felipe-
dc.creatorCamilotto, Andrei Carlesso-
dc.creatorBonelli, Djonatan Riquelme Clein-
dc.creatorFiorentin, Eduardo Vinicius Perissinotto-
dc.creatorSantos, João Luís Almeida-
dc.date2025-12-04-
dc.date.accessioned2026-04-02T12:34:34Z-
dc.date.available2028-
dc.date.available2026-04-02T12:34:34Z-
dc.date.issued2025-
dc.identifier.urihttps://rd.uffs.edu.br/handle/prefix/9196-
dc.description.resumoDecision-making in multiplayer, partially observable games poses significant challenges due to hidden information and large branching factors. This study introduces a persistent Monte Carlo Tree Search (MCTS) framework designed to operate efficiently in such environments, using the strategic board game Citadels as a case study. The key contribution is the decoupling of the tree construction phase from the application phase: decision trees are generated offline through environment simulations, and relevant statistics are stored in tabular form, enabling rapid querying during execution without the need for additional simulations. Experimental evaluation demonstrates that exposure to diverse opponents during training produces models with robust and generalizable strategies, capable of strong performance across a wide range of game scenarios. In particular, models trained in an environment where opponents were stochastically sampled consistently outperformed those trained against single opponent types and exhibited stable performance across training runs. These findings underscore the effectiveness of combining MCTS with persistent knowledge storage to produce fast, reli- able, and adaptable agents in complex domains.pt_BR
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dc.description.provenanceApproved for entry into archive by DIONE ROSSI FARIAS (dione@uffs.edu.br) on 2026-04-02T12:34:34Z (GMT) No. of bitstreams: 1 CAMILOTTO.pdf: 1656035 bytes, checksum: 16f145537c316c664d69cf08c97bc44a (MD5)en
dc.description.provenanceMade available in DSpace on 2026-04-02T12:34:34Z (GMT). No. of bitstreams: 1 CAMILOTTO.pdf: 1656035 bytes, checksum: 16f145537c316c664d69cf08c97bc44a (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 Embargadopt_BR
dc.subjectInteligência artificialpt_BR
dc.subjectAlgoritmospt_BR
dc.subjectMétodo de Monte Carlopt_BR
dc.subjectJogos de tabuleiropt_BR
dc.titlePersistent Monte Carlo Tree Search for agents operating in complex environments: a case study on Citadelspt_BR
dc.typeMonografiapt_BR
Appears in Collections:Ciência da Computação

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