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Difficulty Modelling in Mobile Puzzle Games: An Empirical Study on Different Methods to Combine Player Analytics and Simulated Data (2401.17436v1)

Published 30 Jan 2024 in cs.AI

Abstract: Difficulty is one of the key drivers of player engagement and it is often one of the aspects that designers tweak most to optimise the player experience; operationalising it is, therefore, a crucial task for game development studios. A common practice consists of creating metrics out of data collected by player interactions with the content; however, this allows for estimation only after the content is released and does not consider the characteristics of potential future players. In this article, we present a number of potential solutions for the estimation of difficulty under such conditions, and we showcase the results of a comparative study intended to understand which method and which types of data perform better in different scenarios. The results reveal that models trained on a combination of cohort statistics and simulated data produce the most accurate estimations of difficulty in all scenarios. Furthermore, among these models, artificial neural networks show the most consistent results.

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References (48)
  1. “Gaming market - growth, trends, covid-19 impact, and forecasts (2022-2027),” https://www.mordorintelligence.com/industry-reports/global-gaming-market, accessed: 2022-07-08.
  2. “Global games market to generate $175.8 billion in 2021; despite a slight decline, the market is on track to surpass $200 billion in 2023),” https://newzoo.com/insights/articles/global-games-market-to-generate-175-8-billion-in-2021-despite-a-slight-decline-the-market-is-on-track-to-surpass-200-billion-in-2023/, accessed: 2022-07-08.
  3. “How much is the gaming industry worth in 2022? (revenue & stats),” https://earthweb.com/how-much-is-the-gaming-industry-worth/, accessed: 2022-07-08.
  4. J. Kristensen, C. Guckelsberger, P. Burelli, and P. Hämäläinen, “Personalized game difficulty prediction using factorization machines,” User Interface Software and Technology, vol. 2022, 2022.
  5. J. T. Kristensen and P. Burelli, “Operationalising difficulty in puzzle games,” Ph.D. dissertation, IT University of Copenhagen, 2022.
  6. A. Denisova, P. Cairns, C. Guckelsberger, and D. Zendle, “Measuring perceived challenge in digital games: Development & validation of the challenge originating from recent gameplay interaction scale (CORGIS),” International Journal of Human-Computer Studies, vol. 137, p. 102383, May 2020. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S1071581919301491
  7. A. Denisova, C. Guckelsberger, and D. Zendle, “Challenge in Digital Games: Towards Developing a Measurement Tool,” in Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems.   Denver Colorado USA: ACM, May 2017, pp. 2511–2519. [Online]. Available: https://dl.acm.org/doi/10.1145/3027063.3053209
  8. J. Chen, “Flow in games (and everything else),” Commun. ACM, vol. 50, no. 4, p. 31–34, apr 2007. [Online]. Available: https://doi.org/10.1145/1232743.1232769
  9. R. M. Ryan, C. S. Rigby, and A. Przybylski, “The Motivational Pull of Video Games: A Self-Determination Theory Approach,” Motivation and Emotion, vol. 30, no. 4, pp. 347–363, 2006.
  10. A. Tyack and E. D. Mekler, “Self-Determination Theory in HCI Games Research: Current Uses and Open Questions,” in Proc. Conference on Human Factors in Computing Systems (CHI).   ACM, 2020, pp. 1–22.
  11. J. T. Alexander, J. Sear, and A. Oikonomou, “An investigation of the effects of game difficulty on player enjoyment,” Entertainment Computing, vol. 4, no. 1, pp. 53–62, Feb. 2013. [Online]. Available: https://linkinghub.elsevier.com/retrieve/pii/S1875952112000134
  12. J. H. Brockmyer, C. M. Fox, K. A. Curtiss, E. McBroom, K. M. Burkhart, and J. N. Pidruzny, “The Development of the Game Engagement Questionnaire: A Measure of Engagement in Video Game-Playing,” Journal of Experimental Social Psychology, vol. 45, no. 4, pp. 624–634, 2009.
  13. M. Pusey, K. W. Wong, and N. A. Rappa, “The Puzzle Challenge Analysis Tool. A Tool for Analysing the Cognitive Challenge Level of Puzzles in Video Games,” Proceedings of the ACM on Human-Computer Interaction, vol. 5, no. CHI PLAY, pp. 1–27, Oct. 2021. [Online]. Available: https://dl.acm.org/doi/10.1145/3474703
  14. S. Roohi, C. Guckelsberger, A. Relas, H. Heiskanen, J. Takatalo, and P. Hämäläinen, “Predicting game difficulty and engagement using ai players,” Proceedings of the ACM on Human-Computer Interaction, vol. 5, no. CHI PLAY, pp. 1–17, 2021.
  15. J. T. Kristensen, A. Valdivia, and P. Burelli, “Estimating player completion rate in mobile puzzle games using reinforcement learning,” in 2020 IEEE Conference on Games (CoG), 2020, pp. 636–639.
  16. S. F. Gudmundsson, P. Eisen, E. Poromaa, A. Nodet, S. Purmonen, B. Kozakowski, R. Meurling, and L. Cao, “Human-like playtesting with deep learning,” in 2018 IEEE Conference on Computational Intelligence and Games (CIG).   IEEE, 2018, pp. 1–8.
  17. R. Hunicke, “The case for dynamic difficulty adjustment in games,” in Proceedings of the 2005 ACM SIGCHI International Conference on Advances in computer entertainment technology, 2005, pp. 429–433.
  18. M. Zohaib, “Dynamic difficulty adjustment (dda) in computer games: A review,” Advances in Human-Computer Interaction, vol. 2018, 2018.
  19. M. Gonzalez-Duque, R. B. Palm, and S. Risi, “Fast game content adaptation through bayesian-based player modelling,” in 2021 IEEE Conference on Games (CoG), 2021, pp. 01–08.
  20. S. Xue, M. Wu, J. Kolen, N. Aghdaie, and K. A. Zaman, “Dynamic Difficulty Adjustment for Maximized Engagement in Digital Games,” in Proceedings of the 26th International Conference on World Wide Web Companion - WWW ’17 Companion.   Perth, Australia: ACM Press, 2017, pp. 465–471. [Online]. Available: http://dl.acm.org/citation.cfm?doid=3041021.3054170
  21. D. B. Or, M. Kolomenkin, and G. Shabat, “Dl-dda-deep learning based dynamic difficulty adjustment with ux and gameplay constraints,” in 2021 IEEE Conference on Games (CoG).   IEEE, 2021, pp. 1–7.
  22. J. Pfau, J. D. Smeddinck, and R. Malaka, “Enemy within: Long-term motivation effects of deep player behavior models for dynamic difficulty adjustment,” in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 2020, pp. 1–10.
  23. T. Constant and G. Levieux, “Dynamic Difficulty Adjustment Impact on Players’ Confidence,” in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems.   Glasgow Scotland Uk: ACM, May 2019, pp. 1–12. [Online]. Available: https://dl.acm.org/doi/10.1145/3290605.3300693
  24. J. Li, H. Lu, C. Wang, W. Ma, M. Zhang, X. Zhao, W. Qi, Y. Liu, and S. Ma, “A Difficulty-Aware Framework for Churn Prediction and Intervention in Games,” in Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining.   Virtual Event Singapore: ACM, Aug. 2021, pp. 943–952. [Online]. Available: https://dl.acm.org/doi/10.1145/3447548.3467277
  25. F. Mourato, F. Birra, and M. P. dos Santos, “Difficulty in action based challenges: success prediction, players’ strategies and profiling,” in Proceedings of the 11th Conference on Advances in Computer Entertainment Technology, 2014, pp. 1–10.
  26. M. Van Kreveld, M. Löffler, and P. Mutser, “Automated puzzle difficulty estimation,” in 2015 IEEE Conference on Computational Intelligence and Games (CIG).   IEEE, 2015, pp. 415–422.
  27. J. T. Kristensen, A. Valdivia, and P. Burelli, “Statistical modelling of level difficulty in puzzle games,” in 2021 IEEE Conference on Games (CoG).   IEEE, 2021, pp. 1–8.
  28. D. Wheat, M. Masek, C. P. Lam, and P. Hingston, “Modeling perceived difficulty in game levels,” in Proceedings of the Australasian Computer Science Week Multiconference, 2016, pp. 1–8.
  29. A. E. Zook and M. O. Riedl, “A temporal data-driven player model for dynamic difficulty adjustment,” in Eighth Artificial Intelligence and Interactive Digital Entertainment Conference, 2012.
  30. S. Ariyurek, A. Betin-Can, and E. Surer, “Automated video game testing using synthetic and humanlike agents,” IEEE Transactions on Games, vol. 13, no. 1, pp. 50–67, 2019.
  31. A. Latos, “Automated playtesting on 2d video games. an agent-based approach on nethackclone via iv4xr framework,” Master’s thesis, Utrecht University, 2022.
  32. S. A. Dukkancı, “Level generation using genetic algorithms and difficulty testing using reinforcement learning in match-3 game,” Master’s thesis, Middle East Technical University, 2021.
  33. J. Bergdahl, C. Gordillo, K. Tollmar, and L. Gisslén, “Augmenting automated game testing with deep reinforcement learning,” in 2020 IEEE Conference on Games (CoG).   IEEE, 2020, pp. 600–603.
  34. S. Stahlke, A. Nova, and P. Mirza-Babaei, “Artificial players in the design process: Developing an automated testing tool for game level and world design,” in Proceedings of the Annual Symposium on Computer-Human Interaction in Play, 2020, pp. 267–280.
  35. B. Horn, J. A. Miller, G. Smith, and S. Cooper, “A monte carlo approach to skill-based automated playtesting,” in Fourteenth Artificial Intelligence and Interactive Digital Entertainment Conference, 2018.
  36. J. Pfau, A. Liapis, G. Volkmar, G. N. Yannakakis, and R. Malaka, “Dungeons & replicants: automated game balancing via deep player behavior modeling,” in 2020 IEEE Conference on Games (CoG).   IEEE, 2020, pp. 431–438.
  37. J. T. Kristensen and P. Burelli, “Strategies for using proximal policy optimization in mobile puzzle games,” in International conference on the foundations of digital games, 2020, pp. 1–10.
  38. L. Mugrai, F. Silva, C. Holmgård, and J. Togelius, “Automated playtesting of matching tile games,” in 2019 IEEE Conference on Games (CoG).   IEEE, 2019, pp. 1–7.
  39. Y. Shin, J. Kim, K. Jin, and Y. B. Kim, “Playtesting in match 3 game using strategic plays via reinforcement learning,” IEEE Access, vol. 8, pp. 51 593–51 600, 2020.
  40. I. Kamaldinov and I. Makarov, “Deep reinforcement learning in match-3 game,” in 2019 IEEE conference on games (CoG).   IEEE, 2019, pp. 1–4.
  41. E. R. Poromaa, “Crushing candy crush: predicting human success rate in a mobile game using monte-carlo tree search,” Master’s thesis, KTH, School of Computer Science and Communication (CSC), 2017.
  42. J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal Policy Optimization Algorithms,” arXiv e-prints, pp. 1–12, 7 2017. [Online]. Available: http://arxiv.org/abs/1707.06347
  43. Y. Zhao, I. Borovikov, F. de Mesentier Silva, A. Beirami, J. Rupert, C. Somers, J. Harder, J. Kolen, J. Pinto, R. Pourabolghasem et al., “Winning is not everything: Enhancing game development with intelligent agents,” IEEE Transactions on Games, vol. 12, no. 2, pp. 199–212, 2020.
  44. “Appbrain: Google play ranking: The top grossing puzzle games in the united states,” https://www.appbrain.com/stats/google-play-rankings/top_grossing/puzzle/us#, accessed: 2022-07-08.
  45. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” The journal of machine learning research, vol. 15, no. 1, pp. 1929–1958, 2014.
  46. A. Justel and D. Peña, “Gibbs sampling will fail in outlier problems with strong masking,” Journal of Computational and Graphical Statistics, vol. 5, no. 2, pp. 176–189, 1996. [Online]. Available: https://www.tandfonline.com/doi/abs/10.1080/10618600.1996.10474703
  47. Y. Juan, Y. Zhuang, W.-S. Chin, and C.-J. Lin, “Field-aware factorization machines for ctr prediction,” in Proceedings of the 10th ACM conference on recommender systems, 2016, pp. 43–50.
  48. F. Tahmasebi, M. Meghdadi, S. Ahmadian, and K. Valiallahi, “A hybrid recommendation system based on profile expansion technique to alleviate cold start problem,” Multimedia Tools and Applications, vol. 80, no. 2, pp. 2339–2354, 2021.
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