Detection of behavior in electronic games - a systematic mapping
Resumo
Introduction: Behavior detection in electronic games enables adaptive experiences, yet lacks comprehensive analysis of techniques for constructivist serious games. Objective: To systematically map behavior detection technologies, analyzing their purposes, efficiency, and adaptability for educational contexts. Methodology: Systematic mapping using Parsifal, evaluating technologies, implementation challenges, and effectiveness metrics across 39 selected studies. Results: Machine learning, statistical methods, and ensemble approaches dominate, applied for dynamic difficulty and emotion detection. Key challenges include data requirements and visualization tool limitations.
Palavras-chave:
Behavior detection, Player modeling, Serious games, Constructivist learning, Systematic mapping, Educational Technologies
Referências
Alonso-Fernández, C., Calvo-Morata, A., Freire, M., Martínez-Ortiz, I., e FernándezManjón, B. (2019). Applications of data science to game learning analytics data: A systematic literature review. Computers & Education, 141:103612.
Aydin, M., Karal, H., e Nabiyev, V. (2023). Examination of adaptation components in serious games: a systematic review study. Education and Information Technologies, 28(6):6541–6562.
Bahrololloomi, F., Klonowski, F., Sauer, S., Horst, R., e Dörner, R. (2023). E-sports player performance metrics for predicting the outcome of league of legends matches considering player roles. SN Computer Science, 4(3):238.
Bindewald, J. M., Peterson, G. L., e Miller, M. E. (2017). Clustering-based online player modeling. In Computer Games: 5th Workshop on Computer Games, CGW 2016, and 5th Workshop on General Intelligence in Game-Playing Agents, GIGA 2016, Held in Conjunction with the 25th International Conference on Artificial Intelligence, IJCAI 2016, New York, USA, July 9-10, 2016, Revised Selected Papers 5, pages 86–100. Springer.
Butler, E. (2013). Player knowledge modeling in game design feedback and automation. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, volume 9, pages 2–5.
Carneiro, E. M., da Cunha, A. M., e Dias, L. A. V. (2014). Adaptive game ai architecture with player modeling. In 2014 11th International Conference on Information Technology: New Generations, pages 40–45. IEEE.
Catarino, J. e Martinho, C. (2019). Procedural progression model for smash time. In 2019 IEEE Conference on Games (CoG), pages 1–8. IEEE.
Charles, D., McNeill, M., McAlister, M., Black, M., Moore, A., Stringer, K., Kücklich, J., e Kerr, A. (2005). Player-centred game design: Player modelling and adaptive digital games.
Chittaro, L., Ranon, R., e Ieronutti, L. (2006). Vu-flow: A visualization tool for analyzing navigation in virtual environments. IEEE Transactions on Visualization and Computer Graphics, 12(6):1475–1485.
Chrysafiadi, K. e Virvou, M. (2013). Student modeling approaches: A literature review for the last decade. Expert Systems with Applications, 40(11):4715–4729.
Conroy, D., Wyeth, P., e Johnson, D. (2011). Modeling player-like behavior for game ai design. In Proceedings of the 8th International Conference on Advances in Computer Entertainment Technology, pages 1–8.
Cowley, B. U. (2020). Generalised player modelling: why artificial intelligence in games should incorporate meaning, with a formalism for so doing. In HCI in Games: Second International Conference, HCI-Games 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings 22, pages 3–22. Springer.
Croissant, M., Schofield, G., e McCall, C. (2023). Theories, methodologies, and effects of affect-adaptive games: A systematic review. Entertainment Computing, 47:100591.
Desurvire, H. e El-Nasr, M. S. (2013). Methods for game user research: studying player behavior to enhance game design. IEEE computer graphics and applications, 33(4):82–87.
Gambs, S., Killijian, M.-O., e del Prado Cortez, M. N. (2012). Next place prediction using mobility markov chains. In Proceedings of the first workshop on measurement, privacy, and mobility, pages 1–6.
Gray, R. C., Zhu, J., Arigo, D., Forman, E., e Ontañón, S. (2020). Player modeling via multi-armed bandits. In Proceedings of the 15th international conference on the foundations of digital games, pages 1–8.
Hare, R. e Tang, Y. (2022a). Petri nets and hierarchical reinforcement learning for personalized student assistance in serious games. In 2022 International Conference on Cyber-Physical Social Intelligence (ICCSI), pages 733–737. IEEE.
Hare, R. e Tang, Y. (2022b). Player modeling and adaptation methods within adaptive serious games. IEEE Transactions on Computational Social Systems, 10(4):1939– 1950.
Harpstead, E., Myers, B. A., e Aleven, V. (2013). In search of learning: facilitating data analysis in educational games. In Proceedings of the SIGCHI conference on human factors in computing systems, pages 79–88.
Harrison, B. e Roberts, D. (2012). A review of student modeling techniques in intelligent tutoring systems. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, volume 8, pages 61–66.
Hooshyar, D., Bardone, E., Mawas, N. E., e Yang, Y. (2020). Transparent player model: Adaptive visualization of learner model in educational games. In Innovative Technologies and Learning: Third International Conference, ICITL 2020, Porto, Portugal, November 23–25, 2020, Proceedings 3, pages 349–357. Springer.
Hooshyar, D., El Mawas, N., Milrad, M., e Yang, Y. (2023). Modeling learners to early predict their performance in educational computer games. IEEE Access, 11:20399– 20417.
Hooshyar, D., Huang, Y.-M., e Yang, Y. (2022). Gamedkt: Deep knowledge tracing in educational games. Expert Systems with Applications, 196:116670.
Hooshyar, D., Lee, C., e Lim, H. (2016). A survey on data-driven approaches in educational games. In 2016 2nd International Conference on Science in Information Technology (ICSITech), pages 291–295. IEEE.
Huang, T. T. (2002). Towards incorporating intent inference into the game of go.
Ingram, B., van Alten, C., Klein, R., e Rosman, B. (2023). Generating interpretable play-style descriptions through deep unsupervised clustering of trajectories. IEEE Transactions on Games.
Kang, S. e Kim, S. K. (2022). Game outlier behavior detection system based on dynamic timewarp algorithm. CMES-Computer Modeling in Engineering & Sciences, 131(1).
Kantharaju, P., Alderfer, K., Zhu, J., Char, B., Smith, B., e Ontanón, S. (2018). Tracing player knowledge in a parallel programming educational game. In Proceedings of Computing the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, volume 14, pages 173–179.
Khoshkangini, R., Ontanón, S., Marconi, A., e Zhu, J. (2018). Dynamically extracting play style in educational games. EUROSIS proceedings, GameOn.
Kitchenham, B. A. e Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical Report EBSE 2007-001, Keele University and Durham University Joint Report.
Kleinman, E., Ahmad, S., Teng, Z., Bryant, A., Nguyen, T.-H. D., Harteveld, C., e Seif El-Nasr, M. (2020). ” and then they died”: Using action sequences for data driven, context aware gameplay analysis. In Proceedings of the 15th International Conference on the Foundations of Digital Games, pages 1–12.
Liao, N., Guzdial, M., e Riedl, M. (2017). Deep convolutional player modeling on log and level data. In Proceedings of the 12th International Conference on the Foundations of Digital Games, pages 1–4.
Liu, Y.-E., Mandel, T., Butler, E., Andersen, E., O’Rourke, E., Brunskill, E., e Popovic, Z. (2013). Predicting player moves in an educational game: A hybrid approach. In EDM, pages 106–113.
Loh, C. S., Li, I.-H., e Sheng, Y. (2016). Comparison of similarity measures to differentiate players’ actions and decision-making profiles in serious games analytics. Computers in Human Behavior, 64:562–574.
Machado, M. C., Fantini, E. P., e Chaimowicz, L. (2011). Player modeling: Towards a common taxonomy. In 2011 16th international conference on computer games (CGAMES), pages 50–57. IEEE.
Min, W., Baikadi, A., Mott, B., Rowe, J., Liu, B., Ha, E. Y., e Lester, J. (2016). A generalized multidimensional evaluation framework for player goal recognition. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, volume 12, pages 197–203.
Missura, O. e Gärtner, T. (2009). Player modeling for intelligent difficulty adjustment (resubmission). KDML 2009, page 76.
Moon, J., Choi, Y., Park, T., Choi, J., Hong, J.-H., e Kim, K.-J. (2022). Diversifying dynamic difficulty adjustment agent by integrating player state models into monte-carlo tree search. Expert Systems with Applications, 205:117677.
Moura, D., El-Nasr, M. S., e Shaw, C. D. (2011). Visualizing and understanding players’ behavior in video games: discovering patterns and supporting aggregation and comparison. In ACM SIGGRAPH 2011 game papers, pages 1–6.
Petersen, K., Vakkalanka, S., e Kuzniarz, L. (2015). Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology, 64:1–18.
Said, B., Cheniti-Belcadhi, L., e El Khayat, G. (2019). An ontology for personalization in serious games for assessment. In 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), pages 148–154. IEEE.
Scannavino, K. R. F., Nakagawa, E. Y., Fabbri, S. C. P. F., e Ferrari, F. C. (2017). Revisão sistemática da literatura em engenharia de software: teoria e prática.
Shaker, N., Shaker, M., e Abou-Zleikha, M. (2015). Towards generic models of player experience. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, volume 11, pages 191–197.
Snodgrass, S., Mohaddesi, O., e Harteveld, C. (2019). Towards a generalized player model through the peas framework. In Proceedings of the 14th International Conference on the Foundations of Digital Games, pages 1–7.
Streicher, A., Busch, J., e Roller, W. (2021). Dynamic cognitive modeling for adaptive serious games. In International Conference on Human-Computer Interaction, pages 167–184. Springer.
Sun, Y., Liang, C., Sutherland, S., Harteveld, C., e Kaeli, D. (2016). Modeling player decisions in a supply chain game. In 2016 IEEE Conference on Computational Intelligence and Games (CIG), pages 1–8. IEEE.
Thue, D. e Bulitko, V. (2006). Modelling goal-directed players in digital games. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, volume 2, pages 86–91.
Tychsen, A. e Canossa, A. (2008). Defining personas in games using metrics. In Proceedings of the 2008 conference on future play: Research, play, share, pages 73–80.
Vallim, R. M., Andrade Filho, J. A., De Mello, R. F., e De Carvalho, A. C. (2013). Online behavior change detection in computer games. Expert systems with applications, 40(16):6258–6265.
Vaz de Carvalho, C. (2016). Dynamic serious games balancing. In Serious Games, Interaction, and Simulation: 5th International Conference, SGAMES 2015, Novedrate, Italy, September 16–18, 2015, Revised Selected Papers 5, pages 21–27. Springer.
Yannakakis, G. N. e Togelius, J. (2018). Artificial intelligence and games, volume 2. Springer.
Yoon, T. B., Kim, D. M., Park, K. H., Lee, J. H., e You, K.-H. (2007). Game player modeling using d-fsms. In Human Interface and the Management of Information. Interacting in Information Environments: Symposium on Human Interface 2007, Held as Part of HCI International 2007, Beijing, China, July 22-27, 2007, Proceedings, Part II, pages 490–499. Springer.
Zhao, Y., Mokhtar Muhamad, M., Salina Mustakim, S., Li, W., Wu, X., e Wang, A. (2023). Adaptive mobile-assisted language learning: A bayesian framework study for optimal learning content selection. In 2023 3rd International Conference on Mobile Networks and Wireless Communications (ICMNWC), pages 1–6.
Zhu, J. e Ontañón, S. (2020). Player-centered ai for automatic game personalization: Open problems. In Proceedings of the 15th International Conference on the Foundations of Digital Games, pages 1–8.
Zook, A., Lee-Urban, S., Drinkwater, M. R., e Riedl, M. O. (2012). Skill-based mission generation: A data-driven temporal player modeling approach. In Proceedings of the The third workshop on Procedural Content Generation in Games, pages 1–8.
Zook, A. e Riedl, M. (2012). A temporal data-driven player model for dynamic difficulty adjustment. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, volume 8, pages 93–98.
Aydin, M., Karal, H., e Nabiyev, V. (2023). Examination of adaptation components in serious games: a systematic review study. Education and Information Technologies, 28(6):6541–6562.
Bahrololloomi, F., Klonowski, F., Sauer, S., Horst, R., e Dörner, R. (2023). E-sports player performance metrics for predicting the outcome of league of legends matches considering player roles. SN Computer Science, 4(3):238.
Bindewald, J. M., Peterson, G. L., e Miller, M. E. (2017). Clustering-based online player modeling. In Computer Games: 5th Workshop on Computer Games, CGW 2016, and 5th Workshop on General Intelligence in Game-Playing Agents, GIGA 2016, Held in Conjunction with the 25th International Conference on Artificial Intelligence, IJCAI 2016, New York, USA, July 9-10, 2016, Revised Selected Papers 5, pages 86–100. Springer.
Butler, E. (2013). Player knowledge modeling in game design feedback and automation. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, volume 9, pages 2–5.
Carneiro, E. M., da Cunha, A. M., e Dias, L. A. V. (2014). Adaptive game ai architecture with player modeling. In 2014 11th International Conference on Information Technology: New Generations, pages 40–45. IEEE.
Catarino, J. e Martinho, C. (2019). Procedural progression model for smash time. In 2019 IEEE Conference on Games (CoG), pages 1–8. IEEE.
Charles, D., McNeill, M., McAlister, M., Black, M., Moore, A., Stringer, K., Kücklich, J., e Kerr, A. (2005). Player-centred game design: Player modelling and adaptive digital games.
Chittaro, L., Ranon, R., e Ieronutti, L. (2006). Vu-flow: A visualization tool for analyzing navigation in virtual environments. IEEE Transactions on Visualization and Computer Graphics, 12(6):1475–1485.
Chrysafiadi, K. e Virvou, M. (2013). Student modeling approaches: A literature review for the last decade. Expert Systems with Applications, 40(11):4715–4729.
Conroy, D., Wyeth, P., e Johnson, D. (2011). Modeling player-like behavior for game ai design. In Proceedings of the 8th International Conference on Advances in Computer Entertainment Technology, pages 1–8.
Cowley, B. U. (2020). Generalised player modelling: why artificial intelligence in games should incorporate meaning, with a formalism for so doing. In HCI in Games: Second International Conference, HCI-Games 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings 22, pages 3–22. Springer.
Croissant, M., Schofield, G., e McCall, C. (2023). Theories, methodologies, and effects of affect-adaptive games: A systematic review. Entertainment Computing, 47:100591.
Desurvire, H. e El-Nasr, M. S. (2013). Methods for game user research: studying player behavior to enhance game design. IEEE computer graphics and applications, 33(4):82–87.
Gambs, S., Killijian, M.-O., e del Prado Cortez, M. N. (2012). Next place prediction using mobility markov chains. In Proceedings of the first workshop on measurement, privacy, and mobility, pages 1–6.
Gray, R. C., Zhu, J., Arigo, D., Forman, E., e Ontañón, S. (2020). Player modeling via multi-armed bandits. In Proceedings of the 15th international conference on the foundations of digital games, pages 1–8.
Hare, R. e Tang, Y. (2022a). Petri nets and hierarchical reinforcement learning for personalized student assistance in serious games. In 2022 International Conference on Cyber-Physical Social Intelligence (ICCSI), pages 733–737. IEEE.
Hare, R. e Tang, Y. (2022b). Player modeling and adaptation methods within adaptive serious games. IEEE Transactions on Computational Social Systems, 10(4):1939– 1950.
Harpstead, E., Myers, B. A., e Aleven, V. (2013). In search of learning: facilitating data analysis in educational games. In Proceedings of the SIGCHI conference on human factors in computing systems, pages 79–88.
Harrison, B. e Roberts, D. (2012). A review of student modeling techniques in intelligent tutoring systems. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, volume 8, pages 61–66.
Hooshyar, D., Bardone, E., Mawas, N. E., e Yang, Y. (2020). Transparent player model: Adaptive visualization of learner model in educational games. In Innovative Technologies and Learning: Third International Conference, ICITL 2020, Porto, Portugal, November 23–25, 2020, Proceedings 3, pages 349–357. Springer.
Hooshyar, D., El Mawas, N., Milrad, M., e Yang, Y. (2023). Modeling learners to early predict their performance in educational computer games. IEEE Access, 11:20399– 20417.
Hooshyar, D., Huang, Y.-M., e Yang, Y. (2022). Gamedkt: Deep knowledge tracing in educational games. Expert Systems with Applications, 196:116670.
Hooshyar, D., Lee, C., e Lim, H. (2016). A survey on data-driven approaches in educational games. In 2016 2nd International Conference on Science in Information Technology (ICSITech), pages 291–295. IEEE.
Huang, T. T. (2002). Towards incorporating intent inference into the game of go.
Ingram, B., van Alten, C., Klein, R., e Rosman, B. (2023). Generating interpretable play-style descriptions through deep unsupervised clustering of trajectories. IEEE Transactions on Games.
Kang, S. e Kim, S. K. (2022). Game outlier behavior detection system based on dynamic timewarp algorithm. CMES-Computer Modeling in Engineering & Sciences, 131(1).
Kantharaju, P., Alderfer, K., Zhu, J., Char, B., Smith, B., e Ontanón, S. (2018). Tracing player knowledge in a parallel programming educational game. In Proceedings of Computing the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, volume 14, pages 173–179.
Khoshkangini, R., Ontanón, S., Marconi, A., e Zhu, J. (2018). Dynamically extracting play style in educational games. EUROSIS proceedings, GameOn.
Kitchenham, B. A. e Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical Report EBSE 2007-001, Keele University and Durham University Joint Report.
Kleinman, E., Ahmad, S., Teng, Z., Bryant, A., Nguyen, T.-H. D., Harteveld, C., e Seif El-Nasr, M. (2020). ” and then they died”: Using action sequences for data driven, context aware gameplay analysis. In Proceedings of the 15th International Conference on the Foundations of Digital Games, pages 1–12.
Liao, N., Guzdial, M., e Riedl, M. (2017). Deep convolutional player modeling on log and level data. In Proceedings of the 12th International Conference on the Foundations of Digital Games, pages 1–4.
Liu, Y.-E., Mandel, T., Butler, E., Andersen, E., O’Rourke, E., Brunskill, E., e Popovic, Z. (2013). Predicting player moves in an educational game: A hybrid approach. In EDM, pages 106–113.
Loh, C. S., Li, I.-H., e Sheng, Y. (2016). Comparison of similarity measures to differentiate players’ actions and decision-making profiles in serious games analytics. Computers in Human Behavior, 64:562–574.
Machado, M. C., Fantini, E. P., e Chaimowicz, L. (2011). Player modeling: Towards a common taxonomy. In 2011 16th international conference on computer games (CGAMES), pages 50–57. IEEE.
Min, W., Baikadi, A., Mott, B., Rowe, J., Liu, B., Ha, E. Y., e Lester, J. (2016). A generalized multidimensional evaluation framework for player goal recognition. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, volume 12, pages 197–203.
Missura, O. e Gärtner, T. (2009). Player modeling for intelligent difficulty adjustment (resubmission). KDML 2009, page 76.
Moon, J., Choi, Y., Park, T., Choi, J., Hong, J.-H., e Kim, K.-J. (2022). Diversifying dynamic difficulty adjustment agent by integrating player state models into monte-carlo tree search. Expert Systems with Applications, 205:117677.
Moura, D., El-Nasr, M. S., e Shaw, C. D. (2011). Visualizing and understanding players’ behavior in video games: discovering patterns and supporting aggregation and comparison. In ACM SIGGRAPH 2011 game papers, pages 1–6.
Petersen, K., Vakkalanka, S., e Kuzniarz, L. (2015). Guidelines for conducting systematic mapping studies in software engineering: An update. Information and Software Technology, 64:1–18.
Said, B., Cheniti-Belcadhi, L., e El Khayat, G. (2019). An ontology for personalization in serious games for assessment. In 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), pages 148–154. IEEE.
Scannavino, K. R. F., Nakagawa, E. Y., Fabbri, S. C. P. F., e Ferrari, F. C. (2017). Revisão sistemática da literatura em engenharia de software: teoria e prática.
Shaker, N., Shaker, M., e Abou-Zleikha, M. (2015). Towards generic models of player experience. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, volume 11, pages 191–197.
Snodgrass, S., Mohaddesi, O., e Harteveld, C. (2019). Towards a generalized player model through the peas framework. In Proceedings of the 14th International Conference on the Foundations of Digital Games, pages 1–7.
Streicher, A., Busch, J., e Roller, W. (2021). Dynamic cognitive modeling for adaptive serious games. In International Conference on Human-Computer Interaction, pages 167–184. Springer.
Sun, Y., Liang, C., Sutherland, S., Harteveld, C., e Kaeli, D. (2016). Modeling player decisions in a supply chain game. In 2016 IEEE Conference on Computational Intelligence and Games (CIG), pages 1–8. IEEE.
Thue, D. e Bulitko, V. (2006). Modelling goal-directed players in digital games. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, volume 2, pages 86–91.
Tychsen, A. e Canossa, A. (2008). Defining personas in games using metrics. In Proceedings of the 2008 conference on future play: Research, play, share, pages 73–80.
Vallim, R. M., Andrade Filho, J. A., De Mello, R. F., e De Carvalho, A. C. (2013). Online behavior change detection in computer games. Expert systems with applications, 40(16):6258–6265.
Vaz de Carvalho, C. (2016). Dynamic serious games balancing. In Serious Games, Interaction, and Simulation: 5th International Conference, SGAMES 2015, Novedrate, Italy, September 16–18, 2015, Revised Selected Papers 5, pages 21–27. Springer.
Yannakakis, G. N. e Togelius, J. (2018). Artificial intelligence and games, volume 2. Springer.
Yoon, T. B., Kim, D. M., Park, K. H., Lee, J. H., e You, K.-H. (2007). Game player modeling using d-fsms. In Human Interface and the Management of Information. Interacting in Information Environments: Symposium on Human Interface 2007, Held as Part of HCI International 2007, Beijing, China, July 22-27, 2007, Proceedings, Part II, pages 490–499. Springer.
Zhao, Y., Mokhtar Muhamad, M., Salina Mustakim, S., Li, W., Wu, X., e Wang, A. (2023). Adaptive mobile-assisted language learning: A bayesian framework study for optimal learning content selection. In 2023 3rd International Conference on Mobile Networks and Wireless Communications (ICMNWC), pages 1–6.
Zhu, J. e Ontañón, S. (2020). Player-centered ai for automatic game personalization: Open problems. In Proceedings of the 15th International Conference on the Foundations of Digital Games, pages 1–8.
Zook, A., Lee-Urban, S., Drinkwater, M. R., e Riedl, M. O. (2012). Skill-based mission generation: A data-driven temporal player modeling approach. In Proceedings of the The third workshop on Procedural Content Generation in Games, pages 1–8.
Zook, A. e Riedl, M. (2012). A temporal data-driven player model for dynamic difficulty adjustment. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, volume 8, pages 93–98.
Publicado
30/09/2025
Como Citar
LUCCAS, Matheus dos Santos; PEREIRA, Leonardo Tortoro; CASTELO BRANCO, Kalinka.
Detection of behavior in electronic games - a systematic mapping. In: SIMPÓSIO BRASILEIRO DE JOGOS E ENTRETENIMENTO DIGITAL (SBGAMES), 24. , 2025, Salvador/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 501-515.
DOI: https://doi.org/10.5753/sbgames.2025.9723.
