Uso de Game Analytics no Jogo Pac-Man para Análise de Jogadores: Uma Revisão Sistemática da Literatura
Resumo
Este artigo apresenta uma revisão sistemática da literatura sobre o uso do jogo Pac-Man em pesquisas científicas voltadas à análise de dados. Foram selecionados 25 estudos a partir de 7 bases acadêmicas, com critérios de inclusão e de exclusão. A revisão identificou os métodos de coleta empregados (como coleta automática, captura de tela e sensores) e os diferentes tipos de dados extraídos, organizados entre personagens, ambiente do jogo e fontes externas. Os resultados revelam ampla diversidade no uso do Pac-Man como ambiente de estudo, mas também destacam a falta de padronização na descrição dos dados e a aplicação ainda limitada de análises sistemáticas entre variáveis internas do jogo.
Referências
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Banerjee, T., Khasnobish, A., Chowdhury, A., and Chatterjee, D. (2019). Reckoning respiratory signals to affectively decipher mental state. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 4654–4659. IEEE.
Blom, P. M., Bakkes, S., and Spronck, P. (2019). Modeling and adjusting in-game difficulty based on facial expression analysis. Entertainment Computing, 31:100307.
Buchinger, D., de Siqueira Cavalcanti, G. A., and da Silva Hounsell, M. (2014). Mecanismos de busca acadêmica: uma análise quantitativa. Revista brasileira de computaçaõ aplicada, 6(1):108–120.
Butt, N. A., Mahmood, Z., Rehman, G. U., Nasralla, M. M., Zubair, M., Farman, H., and Khattak, S. B. A. (2023). The development of intelligent agents: A case-based reasoning approach to achieve human-like peculiarities via playback of human traces. IEEE Access, 11:78693–78712.
Cai, J., Li, J., Zhang, M., and Tei, K. (2023). Value iteration networks with gated summarization module. IEEE Access, 11:60407–60420.
Cowley, B. and Charles, D. (2016). Behavlets: a method for practical player modelling using psychology-based player traits and domain specific features. User Modeling and User-Adapted Interaction, 26:257–306.
Costa, D. B., Lucca, G., and Adamatti, D. F. (2022). Aprendizado por reforço e jogos: uma proposta focada na análise de algoritmos. Revista Brasileira de Computaçaõ Aplicada, 14(2):26–34.
Deterding, S. (2015). The lens of intrinsic skill atoms: A method for gameful design. Human-Computer Interaction, 30:294–335.
Drachen, A., El-Nasr, M. S., and Canossa, A. (2013). Game analytics: maximizing the value of player data. Springer.
Fachantidis, A., Taylor, M. E., and Vlahavas, I. (2017). Learning to teach reinforcement learning agents. Machine Learning and Knowledge Extraction, 1(1):21–42.
Foderaro, G., Swingler, A., and Ferrari, S. (2016). A model-based approach to optimizing ms. pac-man game strategies in real time. IEEE Transactions on Computational Intelligence and AI in Games, 9(2):153–165.
Gee, J. P. (2003). What video games have to teach us about learning and literacy. Computers in entertainment (CIE), 1(1):20–20.
Green, C. S. and Bavelier, D. (2012). Learning, attentional control, and action video games. Current biology, 22(6):R197–R206.
Hatschka, C., Ciabattoni, A., and Eiter, T. (2023). Deontic paradoxes in asp with weak constraints. arXiv preprint arXiv:2308.15870.
Johnson, D., Gardner, J., and Wiles, J. (2004). Experience as a moderator of the media equation: the impact of flattery and praise. International Journal of HumanComputer Studies, 61(3):237–258.
Kitchenham, B., Charters, S., et al. (2007). Guidelines for performing systematic literature reviews in software engineering.
Maddipati, H., Kundurthi, A., Raaj, P., Srilatha, K., and Surapaneni, R. (2020). Artificial intelligence based pacman game. International Journal of Innovative Technology and Exploring Engineering, 9:140–144.
Mahlmann, T., Drachen, A., Togelius, J., Canossa, A., and Yannakakis, G. N. (2010). Predicting player behavior in tomb raider: Underworld. In Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games, pages 178–185. IEEE.
Miranda, M., Sánchez-Ruiz, A. A., and Peinado, F. (2021). Interactive explainable case-based reasoning for behavior modelling in videogames. In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), pages 1263–1270. IEEE.
Morosan, M. and Poli, R. (2017). Evolving a designer-balanced neural network for ms pacman. In 2017 9th Computer Science and Electronic Engineering (CEEC), pages 100–105. IEEE.
Neufeld, E. A., Bartocci, E., Ciabattoni, A., and Governatori, G. (2022). Enforcing ethical goals over reinforcement-learning policies. Ethics and Information Technology, 24(4):43.
Rohlfshagen, P., Liu, J., Perez-Liebana, D., and Lucas, S. M. (2017). Pac-man conquers academia: Two decades of research using a classic arcade game. IEEE Transactions on Games, 10(3):233–256.
Safak, A. B., Bostanci, E., and Soylucicek, A. E. (2016). Automated maze generation for ms. pac-man using genetic algorithms. International Journal of Machine Learning and Computing, 6(4):226–240.
Santos Junior, E. S., de Oliveira Machado, A., Macedo, M. C., and dos Santos Souza, A. C. (2019). Reinforcement learning para treino do pac-man em speedrun. Brazilian Journal of Development, 5(11):25927–25957.
Schrum, J. and Miikkulainen, R. (2015). Discovering multimodal behavior in ms. pacman through evolution of modular neural networks. IEEE transactions on computational intelligence and AI in games, 8(1):67–81.
Silva, G. A. and Rigo Júnior, L. O. (2020). Ensino de agentes inteligentes por meio de problemas em jogos. Research, Society and Development, 9(1):e129911793–e129911793.
Sledge, I. J. and Príncipe, J. C. (2018). Analysis of agent expertise in ms. pac-man using value-of-information-based policies. IEEE Transactions on Games, 11(2):142–158.
Tamassia, M., Zambetta, F., Raffe, W. L., Mueller, F., and Li, X. (2017). Learning options from demonstrations: A pac-man case study. IEEE Transactions on Games, 10(1):91–96.
Wang, B., Ma, R., Kuang, J., and Zhang, Y. (2020). How decisions are made in brains: Unpack “black box” of cnn with ms. pac-man video game. IEEE Access, 8:142446–142458.
Wolterink, M. and Bakkes, S. (2021). Towards explainable prediction of player frustration in video games. In Proceedings of the 16th International Conference on the Foundations of Digital Games, pages 1–10.
Yang, Q., Lin, Z., Zhang, W., Li, J., Chen, X., Zhang, J., and Yang, T. (2022). Monkey plays pac-man with compositional strategies and hierarchical decision-making. Elife, 11:e74500.
Yee, N. (2006). The demographics, motivations, and derived experiences of users of massively multi-user online graphical environments. Presence: Teleoperators and virtual environments, 15(3):309–329.
Zhang, Q., Yao, J., Yin, Q., and Zha, Y. (2018). Learning behavior trees for autonomous agents with hybrid constraints evolution. Applied Sciences, 8(7):1077.
Zikky, M. (2016). Review of a*(a star) navigation mesh pathfinding as the alternative of artificial intelligent for ghosts agent on the pacman game. EMITTER International journal of engineering technology, 4(1):141–149.
Banerjee, T., Khasnobish, A., Chowdhury, A., and Chatterjee, D. (2019). Reckoning respiratory signals to affectively decipher mental state. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 4654–4659. IEEE.
Blom, P. M., Bakkes, S., and Spronck, P. (2019). Modeling and adjusting in-game difficulty based on facial expression analysis. Entertainment Computing, 31:100307.
Buchinger, D., de Siqueira Cavalcanti, G. A., and da Silva Hounsell, M. (2014). Mecanismos de busca acadêmica: uma análise quantitativa. Revista brasileira de computaçaõ aplicada, 6(1):108–120.
Butt, N. A., Mahmood, Z., Rehman, G. U., Nasralla, M. M., Zubair, M., Farman, H., and Khattak, S. B. A. (2023). The development of intelligent agents: A case-based reasoning approach to achieve human-like peculiarities via playback of human traces. IEEE Access, 11:78693–78712.
Cai, J., Li, J., Zhang, M., and Tei, K. (2023). Value iteration networks with gated summarization module. IEEE Access, 11:60407–60420.
Cowley, B. and Charles, D. (2016). Behavlets: a method for practical player modelling using psychology-based player traits and domain specific features. User Modeling and User-Adapted Interaction, 26:257–306.
Costa, D. B., Lucca, G., and Adamatti, D. F. (2022). Aprendizado por reforço e jogos: uma proposta focada na análise de algoritmos. Revista Brasileira de Computaçaõ Aplicada, 14(2):26–34.
Deterding, S. (2015). The lens of intrinsic skill atoms: A method for gameful design. Human-Computer Interaction, 30:294–335.
Drachen, A., El-Nasr, M. S., and Canossa, A. (2013). Game analytics: maximizing the value of player data. Springer.
Fachantidis, A., Taylor, M. E., and Vlahavas, I. (2017). Learning to teach reinforcement learning agents. Machine Learning and Knowledge Extraction, 1(1):21–42.
Foderaro, G., Swingler, A., and Ferrari, S. (2016). A model-based approach to optimizing ms. pac-man game strategies in real time. IEEE Transactions on Computational Intelligence and AI in Games, 9(2):153–165.
Gee, J. P. (2003). What video games have to teach us about learning and literacy. Computers in entertainment (CIE), 1(1):20–20.
Green, C. S. and Bavelier, D. (2012). Learning, attentional control, and action video games. Current biology, 22(6):R197–R206.
Hatschka, C., Ciabattoni, A., and Eiter, T. (2023). Deontic paradoxes in asp with weak constraints. arXiv preprint arXiv:2308.15870.
Johnson, D., Gardner, J., and Wiles, J. (2004). Experience as a moderator of the media equation: the impact of flattery and praise. International Journal of HumanComputer Studies, 61(3):237–258.
Kitchenham, B., Charters, S., et al. (2007). Guidelines for performing systematic literature reviews in software engineering.
Maddipati, H., Kundurthi, A., Raaj, P., Srilatha, K., and Surapaneni, R. (2020). Artificial intelligence based pacman game. International Journal of Innovative Technology and Exploring Engineering, 9:140–144.
Mahlmann, T., Drachen, A., Togelius, J., Canossa, A., and Yannakakis, G. N. (2010). Predicting player behavior in tomb raider: Underworld. In Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games, pages 178–185. IEEE.
Miranda, M., Sánchez-Ruiz, A. A., and Peinado, F. (2021). Interactive explainable case-based reasoning for behavior modelling in videogames. In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), pages 1263–1270. IEEE.
Morosan, M. and Poli, R. (2017). Evolving a designer-balanced neural network for ms pacman. In 2017 9th Computer Science and Electronic Engineering (CEEC), pages 100–105. IEEE.
Neufeld, E. A., Bartocci, E., Ciabattoni, A., and Governatori, G. (2022). Enforcing ethical goals over reinforcement-learning policies. Ethics and Information Technology, 24(4):43.
Rohlfshagen, P., Liu, J., Perez-Liebana, D., and Lucas, S. M. (2017). Pac-man conquers academia: Two decades of research using a classic arcade game. IEEE Transactions on Games, 10(3):233–256.
Safak, A. B., Bostanci, E., and Soylucicek, A. E. (2016). Automated maze generation for ms. pac-man using genetic algorithms. International Journal of Machine Learning and Computing, 6(4):226–240.
Santos Junior, E. S., de Oliveira Machado, A., Macedo, M. C., and dos Santos Souza, A. C. (2019). Reinforcement learning para treino do pac-man em speedrun. Brazilian Journal of Development, 5(11):25927–25957.
Schrum, J. and Miikkulainen, R. (2015). Discovering multimodal behavior in ms. pacman through evolution of modular neural networks. IEEE transactions on computational intelligence and AI in games, 8(1):67–81.
Silva, G. A. and Rigo Júnior, L. O. (2020). Ensino de agentes inteligentes por meio de problemas em jogos. Research, Society and Development, 9(1):e129911793–e129911793.
Sledge, I. J. and Príncipe, J. C. (2018). Analysis of agent expertise in ms. pac-man using value-of-information-based policies. IEEE Transactions on Games, 11(2):142–158.
Tamassia, M., Zambetta, F., Raffe, W. L., Mueller, F., and Li, X. (2017). Learning options from demonstrations: A pac-man case study. IEEE Transactions on Games, 10(1):91–96.
Wang, B., Ma, R., Kuang, J., and Zhang, Y. (2020). How decisions are made in brains: Unpack “black box” of cnn with ms. pac-man video game. IEEE Access, 8:142446–142458.
Wolterink, M. and Bakkes, S. (2021). Towards explainable prediction of player frustration in video games. In Proceedings of the 16th International Conference on the Foundations of Digital Games, pages 1–10.
Yang, Q., Lin, Z., Zhang, W., Li, J., Chen, X., Zhang, J., and Yang, T. (2022). Monkey plays pac-man with compositional strategies and hierarchical decision-making. Elife, 11:e74500.
Yee, N. (2006). The demographics, motivations, and derived experiences of users of massively multi-user online graphical environments. Presence: Teleoperators and virtual environments, 15(3):309–329.
Zhang, Q., Yao, J., Yin, Q., and Zha, Y. (2018). Learning behavior trees for autonomous agents with hybrid constraints evolution. Applied Sciences, 8(7):1077.
Zikky, M. (2016). Review of a*(a star) navigation mesh pathfinding as the alternative of artificial intelligent for ghosts agent on the pacman game. EMITTER International journal of engineering technology, 4(1):141–149.
Publicado
24/11/2025
Como Citar
MENEZES, Guilherme Araujo Lira de; GUEDES, Luciana Rita; HOUNSELL, Marcelo da Silva.
Uso de Game Analytics no Jogo Pac-Man para Análise de Jogadores: Uma Revisão Sistemática da Literatura. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 36. , 2025, Curitiba/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 903-917.
DOI: https://doi.org/10.5753/sbie.2025.12713.
