Abstract
The Dynamic Difficulty Adjustment (DDA) of games can play an important role in increasing the player engagement and fun. This work investigates the different mechanisms of a DDA system for a platform game to adequately adapt its difficulty level and keep the player in a state of flow. The proposed adjustment varies the size of the platform and the height of the jump, comparing different approaches from the game systems. Tests were made with sample groups, in which participants answered questionnaires and had their data collected for research purposes. The results indicated that the difficulty of platform games can be estimated by the components of the levels, including correlation between the difficulty and player performance data. In addition, player profiles were predicted from raw game session data and used with machine learning methods to define difficulty progression. Finally, the DDA models were able to adjust the game difficulty to the players, decreasing the dispersion between the performance data and keeping the player in a state of flow, especially when using a feed forward neural network to progress difficulty based on the player’s profile.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andrade, G., Ramalho, G., Gomes, A.S., Corruble, V.: Dynamic game balancing: an evaluation of user satisfaction. American Association for Artificial Intelligence (2006)
Bontchev, B.: Adaptation in affective video games: a literature review. Cybern. Inf. Technol. 16(3), 3–34 (2016)
Chang, D.M.J.: Dynamic difficulty adjustment in computer games. In: Proceedings of the 11th Annual Interactive Multimedia Systems Conference (2013)
Chen, J.: Flow in games (and everything else). ACM Mag. 50(4), 31–34 (2007)
Hawkins, G., Nesbitt, K., Brown, S.D.: Dynamic difficulty balancing for cautious players and risk takers. Int. J. Comput. Games Technol. 2012, 1–10 (2012)
Jackson, S., Marsh, H.: Development and validation of a scale to measure optimal experience: the flow state scale. J. Sport Exerc. Psychol. 18, 17–35 (1996)
John, O.P., Srivastava, S.: The big five trait taxonomy: history, measurement, and theoretical perspectives. In: Handbook of Personality: Theory and Research, pp. 102–138 (1999)
Krath, J., von Korflesch, H.F.O.: Player types and game element preferences: investigating the relationship with the gamification user types HEXAD scale. In: Fang, X. (ed.) HCII 2021. LNCS, vol. 12789, pp. 219–238. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77277-2_18
Kyriazos, T., Stalikas, A., Prassa, K., Galanakis, M., Flora, K., Chatzilia, V.: The flow short scale (fss) dimensionality and what mimic shows on heterogeneity and invariance. Psychology 9, 1357–1382 (2018)
Marczewski, A.: User types. In: Even Ninja Monkeys Like to Play: Gamification, Game Thinking, and Motivational Design, pp. 65–80 (2015)
Mourato, F.J.S.V., Birra, F., Santos, M.P.: Difficulty in action based challenges: success prediction, players’ strategies and profiling. In: Proceedings of the 11th Conference on Advances in Computer Entertainment Technology, pp. 1–10 (2014)
Mourato, F.J.S.V., Santos, M.P.: Measuring difficulty in platform videogames. In: 4\(^{\text{a}}\) Conferência Nacional em Interação Pessoa-Máquina (2010)
Or, D.B., Kolomenkin, M., Shabat, G.: DL-DDA - deep learning based dynamic difficulty adjustment with UX and gameplay constraints. In: 2021 IEEE Conference on Games (CoG), pp. 1–7 (2021)
Rosa, M.P.C., et al.: Dynamic difficulty adjustment using performance and affective data in a platform game. In: Stephanidis, C., et al. (eds.) HCII 2021. LNCS, vol. 13094, pp. 367–386. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-90238-4_26
Smith, G., Cha, M., Whitehead, J.: A framework for analysis of 2D platformer levels. In: Sandbox 2008 Proceedings of the 2008 ACM SIGGRAPH Symposium on Video Games, pp. 75–80 (2008)
Tondello, G., Wehbe, R., Diamond, L., Busch, M., Marczewski, A., Nacke, L.: The gamification user types Hexad scale. In:Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play, pp. 229–243 (2016)
Zohaib, M.: Dynamic difficulty adjustment (dda) in computer games: a review. In: Advances in Human-Computer Interaction, pp. 1–12 (11 2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 IFIP International Federation for Information Processing
About this paper
Cite this paper
Rosa, M.P.C., Castanho, C.D., e Silva, T.B.P., Sarmet, M.M., Jacobi, R.P. (2023). Dynamic Difficulty Adjustment by Performance and Player Profile in Platform Game. In: Ciancarini, P., Di Iorio, A., Hlavacs, H., Poggi, F. (eds) Entertainment Computing – ICEC 2023. ICEC 2023. Lecture Notes in Computer Science, vol 14455. Springer, Singapore. https://doi.org/10.1007/978-981-99-8248-6_1
Download citation
DOI: https://doi.org/10.1007/978-981-99-8248-6_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8247-9
Online ISBN: 978-981-99-8248-6
eBook Packages: Computer ScienceComputer Science (R0)