Tipo:	Monografia
Título:	Tree-based learning for game outcome prediction
Autor(es):	Bonelli, Djonatan Riquelme Clein
Primeiro Orientador:	Grando, Felipe
Resumo:	Predicting 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.
Palavras-chave:	Inteligência artificial Predição Jogos de tabuleiro Sistemas multiagentes Algoritmos
Idioma:	por
País:	Brasil
Instituição:	Universidade Federal da Fronteira Sul
Sigla da Instituição:	UFFS
Faculdade, Instituto ou Departamento:	Campus Chapecó
Tipo de Acesso:	Acesso Aberto
URI:	https://rd.uffs.edu.br/handle/prefix/9176
Data do documento:	2025
Aparece nas coleções:	Ciência da Computação

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