Browse > Article
http://dx.doi.org/10.7583/JKGS.2017.17.3.21

Estimation Method of User's Gameplay Skill Level through the Performance of Gameplay Status  

Chang, Hee-Dong (Dept. of Game Engineering, Hoseo University)
Abstract
Computer games must keep the user immersed for fun. According to the immersion theory, maintaining the user's immersive state requires a continuous balance of game skill level and game difficulty level This study proposes a game skill estimation method of 9th grade that can be applied to a casual action game that can assume a zero-sum game, and additionally proposed a difficulty guessing method. The proposed methods can be implemented quickly and easily because it is a method determining by conditional mathematical expressions. Experiments on the accuracy of the proposed methods for the customized Pac-Man game show that the accuracy of the skill level was 1.2 grade as the difference on the average and the accuracy of the game difficulty level was 1.81 grade the difference on the average. The results show that the proposed methods are accurate enough to be applied to casual action games satisfying the zero-sum condition.
Keywords
Gameplay Skill; Gameplay Status; Gameplay Performance; Flow;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Y. Kim, "Introduction to Digital Visual", Jimmoon Press, ISBN 9788930307673, 1999.
2 R. Caillois, and S. Lee, "Play and Man", Translated by S. Lee, Moonye Books, 1994.
3 M. Csizkszentmihalyi, "Flow: The psychology of optimal experience", Harper Perennial, 1990.
4 Sweetser, Penelope, and Peta Wyeth. "GameFlow: a model for evaluating player enjoyment in games." Computers in Entertainment (CIE) 3.3 (2005): 3-3.
5 G. D. Ellis, J. E. Voelk, C. Morris, "Measurement and Analysis Issues with Explannation of Variance in Daily Experience Using the Flow Model", Journal of Leisure Research, vol 26, no 4, pp. 337-356, 1994.   DOI
6 Chen, H., Wigand, R., & Nilan, M. S., "Optimal experience of web activities", Computers in Human Behavior, 15, pp.585-608, 1999.   DOI
7 J. Webster, L. Trevino, I. Ryan, "The dimensionality and correlates of flow in human computer interactions", Computer, Human Behavior, vol. 9, no. 4, pp.411-426, 1993.   DOI
8 Andrade, Gustavo, et al. "Extending reinforcement learning to provide dynamic game balancing." Proceedings of the Workshop on Reasoning, Representation, and Learning in Computer Games, 19th International Joint Conference on Artificial Intelligence (IJCAI). 2005.
9 Jennings-Teats, Martin, Gillian Smith, and Noah Wardrip-Fruin. "Polymorph: dynamic difficulty adjustment through level generation." Proceedings of the 2010 Workshop on Procedural Content Generation in Games. ACM, 2010.
10 Tan, Chin Hiong, Kay Chen Tan, and Arthur Tay. "Dynamic game difficulty scaling using adaptive behavior-based AI." IEEE Transactions on Computational Intelligence and AI in Games 3.4 (2011): 289-301.   DOI
11 Beume, Nicola, et al. "Measuring flow as concept for detecting game fun in the Pac-Man game." Evolutionary Computation, 2008. CEC 2008.(IEEE World Congress on Computational Intelligence). IEEE Congress on. IEEE, 2008.
12 Yannakakis, Georgios N., and John Hallam. "Evolving opponents for interesting interactive computer games." From animals to animats 8 (2004): 499-508.
13 Pacman Tutorial Download: http://gmc.yoyogames.com/index.php?showtopic=493044
14 Heedong Chang. "Measurement Method of User's Gameplay Skill Level in a Computer Game." 한국게임학회 논문지 12.5 (2012): 23-34.   DOI