Aprimorando a Experiência Ótima em Jogos da Indústria via Inteligência Artificial: Cenários e Proposições
Resumo
Introdução: Este trabalho investiga a convergência da Inteligência Artificial (IA) na experiência de fluxo em resposta à complexidade dos sistemas lúdicos modernos. Objetivo: Apresenta-se o desenvolvimento de estratégias de IA voltadas à adaptação da experiência, visando sustentação da experiência ótima. Metodologia: A pesquisa fundamenta-se no mapeamento entre as dimensões da experiência ótima e quatro pilares de IA, estruturando cenários que articulam modelagem e técnicas de intervenção. Resultados: Como resultado, apresenta-se um conjunto de estratégias de intervenção voltadas a mitigar a sobrecarga cognitiva e barreiras à imersão em títulos de grande porte (AAA) e produções independentes (indie). Estas estratégias servem como base para equipes de desenvolvimento e pesquisadores no estímulo ao estado de fluxo em jogos.
Palavras-chave:
IA, Teoria do Fluxo, Experiência de Fluxo, Experiência de Jogador
Referências
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Aggarwal, C.C. (2016). An introduction to recommender systems. In: Recommender systems: The textbook. Springer, pp. 1-28.
Azad, S., Xu, J., Yu, H., & Li, B. (2017). Scheduling live interactive narratives with mixed-integer linear programming. In: AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), 2-8.
Bakkes, S.C., Spronck, P.H., & Van Lankveld, G. (2012). Player behavioural modelling for video games. Entertainment Computing, 3(3): 71-79.
Barthet, M., Khalifa, A., Liapis, A., & Yannakakis, G. (2022). Generative personas that behave and experience like humans. In: 17th International Conference on the Foundations of Digital Games (FDG), 1-10.
Bohil, C.J. & Biocca, F.A. (2010). Cognitive modeling of video game player user experience. In: 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation, 229-233.
Chen, J. (2007). Flow in games (and everything else). Communications of the ACM, 50(4): 31-34.
Csikszentmihalyi, M. (1990). Flow – The psychology of optimal experience. Harper Perennial.
Csikszentmihalyi, M. (1997). Finding flow: The psychology of engagement with everyday life. Basic Books.
Csikszentmihalyi, M. (2013). Flow: The Psychology of Happiness, Random House.
Csikszentmihalyi, M. (2014). Flow and the foundations of positive psychology: The collected works of Mihaly Csikszentmihalyi. Springer.
D’Andrea, D.A.D., Classe, T.M., & Siqueira, S.W.M. (2025). Mapeamento sistemático da literatura sobre frameworks de game design baseados em motivação e emoção. In: XXIV Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames’25), 48-59.
Donskikh, A., Barabanov, V., Likhotin, M., & Mikhailusov, A. (2021). Architecture of utility-based AI and behavior tree control system. In: 3rd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA’21), 167-170.
Hooshyar, D., Yousefi, M., & Lim, H. (2018). Data-driven approaches to game player modeling: a systematic literature review. ACM Computing Surveys, 50(6): 1-19.
Kalyuga, S. & Plass, J.L. (2009). Evaluating and managing cognitive load in games.
In: Handbook of research on effective electronic gaming in education. IGI Global, pp. 719-737.
Kristen, K.Y., Guzdial, M., & Sturtevant, N.R. (2024). Evaluating the effects of AI directors for quest selection. In: AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), 245-252.
Marques, F.P.R. & Miranda, L.C. (2022a). Avaliação de jogos educacionais para aprendizado da língua japonesa: Uma proposta baseada em heurísticas. In: XXI Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames’22), 1009-1018.
Marques, F.P.R. & Miranda, L.C. (2022b). Design de jogo e experiência de fluxo: Reflexão e desafios na perspectiva da teoria do fluxo. In: XXI Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames’22), 41-50.
Marques, F.P.R. & Miranda, L.C. (2023). Heuristics to support the evaluation of optimal experience in educational games for learning japanese as a second language. Journal on Interactive Systems, 14(1): 494-517.
Marques, F.P.R. & Miranda, L.C. (2026a). Avaliação da experiência ótima de jogadores via escalas de fluxo: Revisões, diretrizes psicométricas, jogo digital e instrumentos psicométricos. In: XXV Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames’26), 1-7.
Marques, F.P.R. & Miranda, L.C. (2026b). Flowtris: Design de um jogo de quebracabeça de ação para indução multidimensional do fluxo. In: XXV Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames’26), 1-12.
Nakamura, J. & Csikszentmihalyi, M. (2014). The concept of flow. In: Flow and the foundations of positive psychology. Springer, pp. 239-263.
Pedersen, C., Togelius, J., & Yannakakis, G.N. (2010). Modeling player experience for content creation. IEEE Transactions on Computational Intelligence and AI in Games, 2(1): 54-67.
Robertson, J., Heiden, J., & Cardona-Rivera, R.E. (2023). Evolving interactive narrative worlds. In: 19th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE’23), 126-135.
Silva, V.E. & Farbiarz, A. (2025). Gamificação com Flow: resgatando o lúdico para uma aprendizagem engajadora no ensino fundamental. In: XXIV Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames’25), 1923-1933.
Taveekitworachai, P., Dewantoro, M.F., Xia, Y., Suntichaikul, P., & Thawonmas, R. (2025). Benching: A benchmark for evaluating large language models in following structured output format instruction in text-based narrative game tasks. IEEE Transactions on Games, 17(3): 665-675.
Tseng, Y.M., Huang, Y.C., Hsiao, T.Y., Chen, W.L., Huang, C.W., Meng, Y., & Chen, Y.N. (2024). Two tales of persona in LLMs: A survey of role-playing and personalization. In: Findings of the Association for Computational Linguistics / Conference on Empirical Methods in Natural Language Processing (EMNLP’24), 16612-16631.
Wender, S. & Watson, I. (2014). Combining case-based reasoning and reinforcement learning for unit navigation in real-time strategy game AI. In: International Conference on Case-Based Reasoning (ICCBR), 511-525.
Wolf, M.J. (2021). The experience and exploration of worlds in single-player video games. Images. The International Journal of European Film, Performing Arts and Audiovisual Communication, 29(38): 223-230.
Yannakakis, G.N. (2012). Game AI revisited. In: 9th Conference on Computing Frontiers (CF), 285-292.
Yannakakis, G.N. & Togelius, J. (2018). Artificial intelligence and games. Springer.
Zhang, Y., He, S., Wang, J., Gao, Y., Yang, J., Yu, X., & Sha, L. (2010). Optimizing player’s satisfaction through DDA of game AI by UCT for the game dead-end. In: 6th International Conference on Natural Computation (ICNC’10), 4161-4165.
Zhang, Y., Zhang, G., & Huang, X. (2022). A survey of procedural content generation for games. In: 3rd International Conference on Culture-Oriented Science and Technology (CoST’22), 186-190.
Zhao, Y.C., Wu, D., Song, S., & Yao, X. (2024). Exploring players’ in-game purchase intention in freemium open-world games: The role of cognitive absorption and motivational affordances. International Journal of Human-Computer Interaction, 40(3): 744-760.
Aggarwal, C.C. (2016). An introduction to recommender systems. In: Recommender systems: The textbook. Springer, pp. 1-28.
Azad, S., Xu, J., Yu, H., & Li, B. (2017). Scheduling live interactive narratives with mixed-integer linear programming. In: AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), 2-8.
Bakkes, S.C., Spronck, P.H., & Van Lankveld, G. (2012). Player behavioural modelling for video games. Entertainment Computing, 3(3): 71-79.
Barthet, M., Khalifa, A., Liapis, A., & Yannakakis, G. (2022). Generative personas that behave and experience like humans. In: 17th International Conference on the Foundations of Digital Games (FDG), 1-10.
Bohil, C.J. & Biocca, F.A. (2010). Cognitive modeling of video game player user experience. In: 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation, 229-233.
Chen, J. (2007). Flow in games (and everything else). Communications of the ACM, 50(4): 31-34.
Csikszentmihalyi, M. (1990). Flow – The psychology of optimal experience. Harper Perennial.
Csikszentmihalyi, M. (1997). Finding flow: The psychology of engagement with everyday life. Basic Books.
Csikszentmihalyi, M. (2013). Flow: The Psychology of Happiness, Random House.
Csikszentmihalyi, M. (2014). Flow and the foundations of positive psychology: The collected works of Mihaly Csikszentmihalyi. Springer.
D’Andrea, D.A.D., Classe, T.M., & Siqueira, S.W.M. (2025). Mapeamento sistemático da literatura sobre frameworks de game design baseados em motivação e emoção. In: XXIV Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames’25), 48-59.
Donskikh, A., Barabanov, V., Likhotin, M., & Mikhailusov, A. (2021). Architecture of utility-based AI and behavior tree control system. In: 3rd International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA’21), 167-170.
Hooshyar, D., Yousefi, M., & Lim, H. (2018). Data-driven approaches to game player modeling: a systematic literature review. ACM Computing Surveys, 50(6): 1-19.
Kalyuga, S. & Plass, J.L. (2009). Evaluating and managing cognitive load in games.
In: Handbook of research on effective electronic gaming in education. IGI Global, pp. 719-737.
Kristen, K.Y., Guzdial, M., & Sturtevant, N.R. (2024). Evaluating the effects of AI directors for quest selection. In: AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE), 245-252.
Marques, F.P.R. & Miranda, L.C. (2022a). Avaliação de jogos educacionais para aprendizado da língua japonesa: Uma proposta baseada em heurísticas. In: XXI Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames’22), 1009-1018.
Marques, F.P.R. & Miranda, L.C. (2022b). Design de jogo e experiência de fluxo: Reflexão e desafios na perspectiva da teoria do fluxo. In: XXI Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames’22), 41-50.
Marques, F.P.R. & Miranda, L.C. (2023). Heuristics to support the evaluation of optimal experience in educational games for learning japanese as a second language. Journal on Interactive Systems, 14(1): 494-517.
Marques, F.P.R. & Miranda, L.C. (2026a). Avaliação da experiência ótima de jogadores via escalas de fluxo: Revisões, diretrizes psicométricas, jogo digital e instrumentos psicométricos. In: XXV Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames’26), 1-7.
Marques, F.P.R. & Miranda, L.C. (2026b). Flowtris: Design de um jogo de quebracabeça de ação para indução multidimensional do fluxo. In: XXV Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames’26), 1-12.
Nakamura, J. & Csikszentmihalyi, M. (2014). The concept of flow. In: Flow and the foundations of positive psychology. Springer, pp. 239-263.
Pedersen, C., Togelius, J., & Yannakakis, G.N. (2010). Modeling player experience for content creation. IEEE Transactions on Computational Intelligence and AI in Games, 2(1): 54-67.
Robertson, J., Heiden, J., & Cardona-Rivera, R.E. (2023). Evolving interactive narrative worlds. In: 19th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE’23), 126-135.
Silva, V.E. & Farbiarz, A. (2025). Gamificação com Flow: resgatando o lúdico para uma aprendizagem engajadora no ensino fundamental. In: XXIV Simpósio Brasileiro de Jogos e Entretenimento Digital (SBGames’25), 1923-1933.
Taveekitworachai, P., Dewantoro, M.F., Xia, Y., Suntichaikul, P., & Thawonmas, R. (2025). Benching: A benchmark for evaluating large language models in following structured output format instruction in text-based narrative game tasks. IEEE Transactions on Games, 17(3): 665-675.
Tseng, Y.M., Huang, Y.C., Hsiao, T.Y., Chen, W.L., Huang, C.W., Meng, Y., & Chen, Y.N. (2024). Two tales of persona in LLMs: A survey of role-playing and personalization. In: Findings of the Association for Computational Linguistics / Conference on Empirical Methods in Natural Language Processing (EMNLP’24), 16612-16631.
Wender, S. & Watson, I. (2014). Combining case-based reasoning and reinforcement learning for unit navigation in real-time strategy game AI. In: International Conference on Case-Based Reasoning (ICCBR), 511-525.
Wolf, M.J. (2021). The experience and exploration of worlds in single-player video games. Images. The International Journal of European Film, Performing Arts and Audiovisual Communication, 29(38): 223-230.
Yannakakis, G.N. (2012). Game AI revisited. In: 9th Conference on Computing Frontiers (CF), 285-292.
Yannakakis, G.N. & Togelius, J. (2018). Artificial intelligence and games. Springer.
Zhang, Y., He, S., Wang, J., Gao, Y., Yang, J., Yu, X., & Sha, L. (2010). Optimizing player’s satisfaction through DDA of game AI by UCT for the game dead-end. In: 6th International Conference on Natural Computation (ICNC’10), 4161-4165.
Zhang, Y., Zhang, G., & Huang, X. (2022). A survey of procedural content generation for games. In: 3rd International Conference on Culture-Oriented Science and Technology (CoST’22), 186-190.
Zhao, Y.C., Wu, D., Song, S., & Yao, X. (2024). Exploring players’ in-game purchase intention in freemium open-world games: The role of cognitive absorption and motivational affordances. International Journal of Human-Computer Interaction, 40(3): 744-760.
Publicado
29/09/2026
Como Citar
MARQUES, Fábio Phillip Rocha; MIRANDA, Leonardo Cunha de; MIRANDA, Erica Esteves Cunha de.
Aprimorando a Experiência Ótima em Jogos da Indústria via Inteligência Artificial: Cenários e Proposições. In: SIMPÓSIO BRASILEIRO DE JOGOS E ENTRETENIMENTO DIGITAL (SBGAMES), 25. , 2026, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 2617-2629.
DOI: https://doi.org/10.5753/sbgames.2026.25640.
