Técnicas de Autobalanceamento em Jogos Digitais - Mapeamento Sistemático da Literatura
Resumo
Introdução: O autobalanceamento é uma estratégia importante para ajustar dinamicamente a dificuldade em jogos digitais, mas ainda carece de sistematização quanto às técnicas, dados e validações utilizadas. Objetivo: Este artigo apresenta um Mapeamento Sistemático da Literatura (MSL) sobre autobalanceamento em jogos, focando em técnicas aplicadas, tipos de dados e estratégias de validação. Metodologia: A pesquisa foi baseada na estrutura proposta Kitchenham e Charters e realizada em seis bases de dados, resultando em 1532 estudos. Após triagem, 25 estudos foram analisados com base em cinco questões de pesquisa. Resultados: Destacam-se técnicas baseadas em IA/ML e frameworks estruturados. A maioria dos estudos usa dados de desempenho e validação quantitativa. O ajuste de dificuldade é a principal mecânica abordada. A maioria dos jogos é voltada ao entretenimento com cenários 2D e 3D equilibrados. O estudo contribui para práticas mais robustas e personalizadas de autobalanceamento.
Palavras-chave:
Jogos Digitais, Autobalanceamento, Mapeamento Sistemático
Referências
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Atorf, D., Dyck, S., e Steinbach, J. (2021). Towards a concept for a hidden object game with dynamic difficulty adjustment. COGNITION AND EXPLORATORY LEARNING IN THE DIGITAL AGE (CELDA 2021), page 311.
Basili, V. R. (1992). Software modeling and measurement: the Goal/Question/Metric paradigm. University of Maryland at College Park.
Caldas, O. I., Mauledoux, M., Aviles, O. F., e Rodriguez-Guerrero, C. (2023). Behavioral and psychophysiological measures of engagement during dynamic difficulty adjustment in immersive virtual reality. Journal of Universal Computer Science, 29(1):16–33.
Camillo, C. M. e Medeiros, L. M. (2018). A importância dos jogos digitais no contexto escolar. Revista Competência.
Cardouzo, H. H. e de Classe, T. M. (2023). Balanceamento em estéticas e dinâmicas para imersao em certos gêneros de jogos-revisao rápida da literatura. Anais Estendidos do XXII Simpósio Brasileiro de Jogos e Entretenimento Digital, pages 200–212.
Chanel, G. e Lopes, P. (2020). User evaluation of affective dynamic difficulty adjustment based on physiological deep learning. In International Conference on Human- Computer Interaction, pages 3–23. Springer.
Chrysafiadi, K., Kamitsios, M., e Virvou, M. (2023). Fuzzy-based dynamic difficulty adjustment of an educational 3d-game. Multimedia Tools and Applications, 82(18):27525–27549.
Denisova, A. e Cairns, P. (2015). Adaptation in digital games: the effect of challenge adjustment on player performance and experience. In Proceedings of the 2015 Annual Symposium on Computer-Human Interaction in Play, pages 97–101.
Frommel, J., Fischbach, F., Rogers, K., e Weber, M. (2018). Emotion-based dynamic difficulty adjustment using parameterized difficulty and self-reports of emotion. In Proceedings of the 2018 annual symposium on computer-human interaction in play, pages 163–171.
Fujita, K. (2022). Alphadda: strategies for adjusting the playing strength of a fully trained alphazero system to a suitable human training partner. PeerJ Computer Science, 8:e1123.
Keele, S. et al. (2007). Guidelines for performing systematic literature reviews in software engineering.
Kickmeier-Rust, M. D. e Albert, D. (2010). Micro-adaptivity: Protecting immersion in didactically adaptive digital educational games. Journal of Computer Assisted Learning, 26(2):95–105.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004):1–26.
Leitão, T., Navarro, L. L. L., Cameira, R. F., e Silva, É. R. (2021). Serious games in business process management: a systematic literature review. Bus. Process. Manag. J., 27:685–721.
Mainieri, B. O., Azevedo, V. L., Braga, P. H. C., e Omar, N. (2018). Development and assessment of an adaptive difficulty arithmetic game based learning object. In 2018 XIII Latin American Conference on Learning Technologies (LACLO), pages 232–239. IEEE.
Masanobu, E., Mikami, K., et al. (2017). Dynamic pressure cycle control: Dynamic diffculty adjustment beyond the flow zone. In 2017 Nicograph International (NicoInt), pages 9–14. IEEE.
Mi, Q. e Gao, T. (2022). Improved belgian ai algorithm for dynamic management in action role-playing games. Applied Sciences, 12(22):11860.
Moon, H.-S. e Seo, J. (2020). Dynamic difficulty adjustment via fast user adaptation. In Adjunct Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology, pages 13–15.
Moschovitis, P. e Denisova, A. (2022). Keep calm and aim for the head: Biofeedback- controlled dynamic difficulty adjustment in a horror game. IEEE Transactions on Games, 15(3):368–377.
Ninaus, M., Tsarava, K., e Moeller, K. (2019). A pilot study on the feasibility of dynamic difficulty adjustment in game-based learning using heart-rate. In Games and Learning Alliance, pages 117–128. Springer.
Orozco-Mora, C. E., Fuentes-Aguilar, R. Q., e Hernández-Melgarejo, G. (2024). Dynamic difficulty adaptation based on stress detection for a virtual reality video game: A pilot study. Electronics, 13(12):2324.
Pfau, J., Smeddinck, J. D., e Malaka, R. (2020). Enemy within: Long-term motivation effects of deep player behavior models for dynamic difficulty adjustment. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems.
Rao Fernandes, W. e Levieux, G. (2019). -logit: Dynamic difficulty adjustment using few data points. In Joint International Conference on Entertainment Computing and Serious Games, pages 158–171. Springer.
Santos, C. M. d. C., Pimenta, C. A. d. M., e Nobre, M. R. C. (2007). A estratégia pico para a construção da pergunta de pesquisa e busca de evidências. Revista latino-americana de enfermagem, 15:508–511.
Shi, P. e Chen, K. (2017). Learning constructive primitives for real-time dynamic difficulty adjustment in super mario bros. IEEE Transactions on Games, 10(2):155– 169.
Silva, M. P., Silva, V. d. N., e Chaimowicz, L. (2016). Dynamic difficulty adjustment on moba games. Entertainment Computing, 17:41–49.
Smerdov, A., Somov, A., Burnaev, E., e Stepanov, A. (2023). Ai-enabled prediction of video game player performance using the data from heterogeneous sensors. Multimedia Tools and Applications, 82(7):11021–11046.
Soancatl, V., van de Gronde, J. J., Lamoth, C., Maurits, N. M., e Roerdink, J. B. (2016). Visual data exploration for balance quantification during exergaming. In EuroVis (Posters), pages 25–27.
Souza, C. H. R., de Oliveira, S. S., Berretta, L. O., e Carvalho, S. T. (2025). Extending a mape-k loop-based framework for dynamic difficulty adjustment in single-player games. Entertainment Computing, 52:100842.
Spronck, P., Sprinkhuizen-Kuyper, I., e Postma, E. O. (2006). Adaptive game ai with dynamic scripting. Machine Learning, 63(3):217–248.
Suaza, J., Gamboa, E., e Trujillo, M. (2019). A health point-based dynamic difficulty adjustment strategy for video games. In Joint International Conference on Entertainment Computing and Serious Games, pages 436–440. Springer.
Weber, M. e Notargiacomo, P. (2020). Dynamic difficulty adjustment in digital games using genetic algorithms. In 2020 19th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames), pages 62–70. IEEE.
Xia, W. e Anand, B. (2016). Game balancing with ecosystem mechanism. In 2016 international conference on data mining and advanced computing (SAPIENCE), pages 317–324. IEEE.
Xue, S., Wu, M., Kolen, J., Aghdaie, N., e Zaman, K. A. (2017). Dynamic difficulty adjustment for maximized engagement in digital games. In Proceedings of the 26th international conference on world wide web companion, pages 465–471.
Yin, H., Luo, L., Cai, W., Ong, Y.-S., e Zhong, J. (2015). A data-driven approach for online adaptation of game difficulty. In 2015 IEEE conference on computational intelligence and games (CIG), pages 146–153. IEEE.
Atorf, D., Dyck, S., e Steinbach, J. (2021). Towards a concept for a hidden object game with dynamic difficulty adjustment. COGNITION AND EXPLORATORY LEARNING IN THE DIGITAL AGE (CELDA 2021), page 311.
Basili, V. R. (1992). Software modeling and measurement: the Goal/Question/Metric paradigm. University of Maryland at College Park.
Caldas, O. I., Mauledoux, M., Aviles, O. F., e Rodriguez-Guerrero, C. (2023). Behavioral and psychophysiological measures of engagement during dynamic difficulty adjustment in immersive virtual reality. Journal of Universal Computer Science, 29(1):16–33.
Camillo, C. M. e Medeiros, L. M. (2018). A importância dos jogos digitais no contexto escolar. Revista Competência.
Cardouzo, H. H. e de Classe, T. M. (2023). Balanceamento em estéticas e dinâmicas para imersao em certos gêneros de jogos-revisao rápida da literatura. Anais Estendidos do XXII Simpósio Brasileiro de Jogos e Entretenimento Digital, pages 200–212.
Chanel, G. e Lopes, P. (2020). User evaluation of affective dynamic difficulty adjustment based on physiological deep learning. In International Conference on Human- Computer Interaction, pages 3–23. Springer.
Chrysafiadi, K., Kamitsios, M., e Virvou, M. (2023). Fuzzy-based dynamic difficulty adjustment of an educational 3d-game. Multimedia Tools and Applications, 82(18):27525–27549.
Denisova, A. e Cairns, P. (2015). Adaptation in digital games: the effect of challenge adjustment on player performance and experience. In Proceedings of the 2015 Annual Symposium on Computer-Human Interaction in Play, pages 97–101.
Frommel, J., Fischbach, F., Rogers, K., e Weber, M. (2018). Emotion-based dynamic difficulty adjustment using parameterized difficulty and self-reports of emotion. In Proceedings of the 2018 annual symposium on computer-human interaction in play, pages 163–171.
Fujita, K. (2022). Alphadda: strategies for adjusting the playing strength of a fully trained alphazero system to a suitable human training partner. PeerJ Computer Science, 8:e1123.
Keele, S. et al. (2007). Guidelines for performing systematic literature reviews in software engineering.
Kickmeier-Rust, M. D. e Albert, D. (2010). Micro-adaptivity: Protecting immersion in didactically adaptive digital educational games. Journal of Computer Assisted Learning, 26(2):95–105.
Kitchenham, B. (2004). Procedures for performing systematic reviews. Keele, UK, Keele University, 33(2004):1–26.
Leitão, T., Navarro, L. L. L., Cameira, R. F., e Silva, É. R. (2021). Serious games in business process management: a systematic literature review. Bus. Process. Manag. J., 27:685–721.
Mainieri, B. O., Azevedo, V. L., Braga, P. H. C., e Omar, N. (2018). Development and assessment of an adaptive difficulty arithmetic game based learning object. In 2018 XIII Latin American Conference on Learning Technologies (LACLO), pages 232–239. IEEE.
Masanobu, E., Mikami, K., et al. (2017). Dynamic pressure cycle control: Dynamic diffculty adjustment beyond the flow zone. In 2017 Nicograph International (NicoInt), pages 9–14. IEEE.
Mi, Q. e Gao, T. (2022). Improved belgian ai algorithm for dynamic management in action role-playing games. Applied Sciences, 12(22):11860.
Moon, H.-S. e Seo, J. (2020). Dynamic difficulty adjustment via fast user adaptation. In Adjunct Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology, pages 13–15.
Moschovitis, P. e Denisova, A. (2022). Keep calm and aim for the head: Biofeedback- controlled dynamic difficulty adjustment in a horror game. IEEE Transactions on Games, 15(3):368–377.
Ninaus, M., Tsarava, K., e Moeller, K. (2019). A pilot study on the feasibility of dynamic difficulty adjustment in game-based learning using heart-rate. In Games and Learning Alliance, pages 117–128. Springer.
Orozco-Mora, C. E., Fuentes-Aguilar, R. Q., e Hernández-Melgarejo, G. (2024). Dynamic difficulty adaptation based on stress detection for a virtual reality video game: A pilot study. Electronics, 13(12):2324.
Pfau, J., Smeddinck, J. D., e Malaka, R. (2020). Enemy within: Long-term motivation effects of deep player behavior models for dynamic difficulty adjustment. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems.
Rao Fernandes, W. e Levieux, G. (2019). -logit: Dynamic difficulty adjustment using few data points. In Joint International Conference on Entertainment Computing and Serious Games, pages 158–171. Springer.
Santos, C. M. d. C., Pimenta, C. A. d. M., e Nobre, M. R. C. (2007). A estratégia pico para a construção da pergunta de pesquisa e busca de evidências. Revista latino-americana de enfermagem, 15:508–511.
Shi, P. e Chen, K. (2017). Learning constructive primitives for real-time dynamic difficulty adjustment in super mario bros. IEEE Transactions on Games, 10(2):155– 169.
Silva, M. P., Silva, V. d. N., e Chaimowicz, L. (2016). Dynamic difficulty adjustment on moba games. Entertainment Computing, 17:41–49.
Smerdov, A., Somov, A., Burnaev, E., e Stepanov, A. (2023). Ai-enabled prediction of video game player performance using the data from heterogeneous sensors. Multimedia Tools and Applications, 82(7):11021–11046.
Soancatl, V., van de Gronde, J. J., Lamoth, C., Maurits, N. M., e Roerdink, J. B. (2016). Visual data exploration for balance quantification during exergaming. In EuroVis (Posters), pages 25–27.
Souza, C. H. R., de Oliveira, S. S., Berretta, L. O., e Carvalho, S. T. (2025). Extending a mape-k loop-based framework for dynamic difficulty adjustment in single-player games. Entertainment Computing, 52:100842.
Spronck, P., Sprinkhuizen-Kuyper, I., e Postma, E. O. (2006). Adaptive game ai with dynamic scripting. Machine Learning, 63(3):217–248.
Suaza, J., Gamboa, E., e Trujillo, M. (2019). A health point-based dynamic difficulty adjustment strategy for video games. In Joint International Conference on Entertainment Computing and Serious Games, pages 436–440. Springer.
Weber, M. e Notargiacomo, P. (2020). Dynamic difficulty adjustment in digital games using genetic algorithms. In 2020 19th Brazilian Symposium on Computer Games and Digital Entertainment (SBGames), pages 62–70. IEEE.
Xia, W. e Anand, B. (2016). Game balancing with ecosystem mechanism. In 2016 international conference on data mining and advanced computing (SAPIENCE), pages 317–324. IEEE.
Xue, S., Wu, M., Kolen, J., Aghdaie, N., e Zaman, K. A. (2017). Dynamic difficulty adjustment for maximized engagement in digital games. In Proceedings of the 26th international conference on world wide web companion, pages 465–471.
Yin, H., Luo, L., Cai, W., Ong, Y.-S., e Zhong, J. (2015). A data-driven approach for online adaptation of game difficulty. In 2015 IEEE conference on computational intelligence and games (CIG), pages 146–153. IEEE.
Publicado
30/09/2025
Como Citar
SERRA, Cristiano Barroso; CLASSE, Tadeu Moreira de; SIQUEIRA, Sean Wolfgand Matsui.
Técnicas de Autobalanceamento em Jogos Digitais - Mapeamento Sistemático da Literatura. In: SIMPÓSIO BRASILEIRO DE JOGOS E ENTRETENIMENTO DIGITAL (SBGAMES), 24. , 2025, Salvador/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 254-266.
DOI: https://doi.org/10.5753/sbgames.2025.10134.
