Statistical Modelling of Level Difficulty in Puzzle Games (2107.03305v2)
Abstract: Successful and accurate modelling of level difficulty is a fundamental component of the operationalisation of player experience as difficulty is one of the most important and commonly used signals for content design and adaptation. In games that feature intermediate milestones, such as completable areas or levels, difficulty is often defined by the probability of completion or completion rate; however, this operationalisation is limited in that it does not describe the behaviour of the player within the area. In this research work, we formalise a model of level difficulty for puzzle games that goes beyond the classical probability of success. We accomplish this by describing the distribution of actions performed within a game level using a parametric statistical model thus creating a richer descriptor of difficulty. The model is fitted and evaluated on a dataset collected from the game Lily's Garden by Tactile Games, and the results of the evaluation show that the it is able to describe and explain difficulty in a vast majority of the levels.
- Kolmogorov-Smirnov test - Encyclopedia of Mathematics.
- How players lose interest in playing a game: An empirical study based on distributions of total playing times. In 2012 IEEE Conference on Computational Intelligence and Games, CIG 2012, pages 139–146, 2012.
- Extended poisson–tweedie: Properties and regression models for count data. Statistical Modelling, 18(1):24–49, 2018.
- Flow: The psychology of optimal experience, volume 1990. Harper & Row New York, 1990.
- Monte Carlo tree search based algorithms for dynamic difficulty adjustment. In 2017 IEEE Conference on Computational Intelligence and Games, CIG 2017, pages 53–59. Institute of Electrical and Electronics Engineers Inc., 10 2017.
- Measuring perceived challenge in digital games: Development & validation of the challenge originating from recent gameplay interaction scale (corgis). International Journal of Human-Computer Studies, 137:102383, 2020.
- Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error, 1 2020.
- A traffic characterization of popular on-line games. IEEE/ACM Transactions On Networking, 13(3):488–500, 2005.
- Human-like playtesting with deep learning. In 2018 IEEE Conference on Computational Intelligence and Games (CIG), pages 1–8. IEEE, 2018.
- Exploring Game Space of Minimal Action Games via Parameter Tuning and Survival Analysis. IEEE TRANSACTIONS ON GAMES, 10(2), 2018.
- B. Jørgensen. The Theory of Dispersion Models. Chapman & Hall/CRC Monographs on Statistics & Applied Probability. Taylor & Francis, 1997.
- Strategies for using proximal policy optimization in mobile puzzle games. In International Conference on the Foundations of Digital Games, pages 1–10, 2020.
- Estimating player completion rate in mobile puzzle games using reinforcement learning. In 2020 IEEE Conference on Games (CoG), pages 636–639. IEEE, 2020.
- Statistical methods for survival data analysis, volume 476. John Wiley & Sons, 2003.
- Is difficulty overrated? the effects of choice, novelty and suspense on intrinsic motivation in educational games. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, CHI ’17, page 1028–1039, New York, NY, USA, 2017. Association for Computing Machinery.
- Modeling player experience for content creation. IEEE Transactions on Computational Intelligence and AI in Games, 2(1):54–67, 2010.
- Dynamic difficulty adjustment for maximized engagement in digital games. In Proceedings of the 26th International Conference on World Wide Web Companion, WWW ’17 Companion, page 465–471, Republic and Canton of Geneva, CHE, 2017. International World Wide Web Conferences Steering Committee.