JACIII Vol.27 No.2 pp. 223-234
doi: 10.20965/jaciii.2023.p0223

Research Paper:

Extracting Branch Factors of Scenarios from a Gaming Simulation Using Log-Cluster Analysis

Akinobu Sakata* ORCID Icon, Takamasa Kikuchi**, Masaaki Kunigami*, Atsushi Yoshikawa*, Masayuki Yamamura*, and Takao Terano***

*Tokyo Institute of Technology
4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8503, Japan

**Keio University
4-1-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa 223-8526, Japan

***Chiba University of Commerce
1-3-1 Konodai, Ichikawa, Chiba 272-8512, Japan

March 20, 2022
November 23, 2022
March 20, 2023
gaming simulation, knowledge extraction, branch factors, log-cluster analysis

This study proposes a method for analyzing gaming simulation results. The gaming simulation we focus on intends to be played by both human and computer agent players. To extract the knowledge of what and how they have played, we must determine what type of decision-making process leads to specific scenarios. Such simulation results, however, tend to have so many branch factors of scenarios that it is hard to understand by manual operations. To deal with the issues, we have developed a method for obtaining the branch factors of scenarios from gaming simulation results. We have demonstrated the effectiveness of the proposed method by identifying the branching factors of scenarios as follows. First, software agents were asked to play a gaming simulation for career education. Next, logs acquired through gaming were classified into multiple scenarios using machine learning techniques. Finally, decision-making factors separating the scenarios were identified using a decision tree.

Branch factor analysis

Branch factor analysis

Cite this article as:
A. Sakata, T. Kikuchi, M. Kunigami, A. Yoshikawa, M. Yamamura, and T. Terano, “Extracting Branch Factors of Scenarios from a Gaming Simulation Using Log-Cluster Analysis,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.2, pp. 223-234, 2023.
Data files:
  1. [1] C. S. Greenblat, “Designing Games and Simulations: An Illustrated Handbook,” SAGE Publications Inc., 1988.
  2. [2] B. Roungas, S. Meijer, and A. Verbraeck, “The tacit knowledge in games: From validation to debriefing,” Int. Simulation and Gaming Association Conf., pp. 74-83, 2019.
  3. [3] C. F. Petranek, S. Corey, and R. Black, “Three levels of learning in simulations: Participating, debriefing, and journal writing,” Simulation & Gaming, Vol.23, No.2, pp. 174-185, 1992.
  4. [4] D. C. Thatcher and M. J. Robinson, “Me–The Slow Learner: Reflections Eight Years on from its Original Design,” Simulation & Gaming, Vol.21, No.3, pp. 291-302, 1990.
  5. [5] T. Kikuchi, Y. Tanaka, M. Kunigami, T. Yamada, H. Takahashi, and T. Terano, “Debriefing Framework for Business Games Using Simulation Analysis,” General Conf. on Emerging Arts of Research on Management and Administration, pp. 64-76, 2018.
  6. [6] A. Sakata, T. Kikuchi, R. Okumura, M. Kunigami, A. Yoshikawa, M. Yamamura, and T. Terano, “Methodology for Extracting Knowledge from a Gaming Simulation Using Data Envelopment Analysis,” Int. J. on Advances in Software, Vol.14, No.12, pp. 107-121, 2021.
  7. [7] C. Alonso-Fernández, I. Martínez-Ortiz, R. Caballero, M. Freire, and B. Fernández-Manjón, “Predicting Students’ Knowledge After Playing a Serious Game Based on Learning Analytics Data: A Case Study,” J. of Computer Assisted Learning, Vol.36, No.3, pp. 350-358, 2020.
  8. [8] C. Alonso-Fernández, M. Freire, I. Martínez-Ortiz, and B. Fernández-Manjón, “Improving evidence-based assessment of players using serious games,” Telematics and Informatics, Vol.60, Article No.101583, 2021.
  9. [9] R. S. Baker and J. Clarke-Midura, “Predicting Successful Inquiry Learning in a Virtual Performance Assessment for Science,” Int. Conf. on User Modeling, Adaptation, and Personalization, pp. 203-214, 2013.
  10. [10] J. Sabourin, L. R. Shores, B. W. Mott, and J. C. Lester, “Predicting Student Self-Regulation Strategies in Game-Based Learning Environments,” 11th Int. Conf. on Intelligent Tutoring Systems, pp. 141-150, 2012.
  11. [11] Y. Tanaka, M. Kunigami, and T. Terano, “What Can Be Learned from the Systematic Analysis of the Log Cluster of Agent Simulation,” Simulation & Gaming, Vol.27, No.1, pp. 31-41, 2018.
  12. [12] M. Prietula, K. Carley, and L. Gasser, “Simulating Organizations: Computational Models of Institutions and Groups,” AAAI Press, 1998.
  13. [13] A. Charnes, W. W. Cooper, and E. Rhodes, “Measuring the efficiency of decision making units,” European J. of Operational Research, Vol.2, No.6, pp. 429-444, 1978.
  14. [14] A. Boussofiane, R. G. Dyson, and E. Thanassoulis, “Applied data envelopment analysis,” European J. of Operational Research, Vol.52, No.1, pp. 1-15, 1991.
  15. [15] C. S. Loh and Y. Sheng, “Performance metrics for serious games: Will the (real) expert please step forward?,” Proc. of Int. Conf. on Computer Games, pp. 202-206, 2013.
  16. [16] C. S. Loh and Y. Sheng, “Maximum Similarity Index (MSI): A metric to differentiate the performance of novices vs. multiple-experts in serious games,” Computers in Human Behavior, Vol.39, pp. 322-330, 2014.
  17. [17] C. S. Loh and Y. Sheng, “Measuring the (dis-)similarity between expert and novice behaviors as serious games analytics,” Education and Information Technologies, Vol.20, No.1, pp. 5-19, 2015.
  18. [18] C. S. Loh, I. H. Li, and Y. Sheng, “Comparison of similarity measures to differentiate players’ actions and decision-making profiles in serious games analytics,” Computers in Human Behavior, Vol.64, pp. 562-574, 2016.
  19. [19] D. Gaurav, Y. Kaushik, S. Supraja, M. Yadav, M. P. Gupta, and M. Chaturvedi, “Empirical Study of Adaptive Serious Games in Enhancing Learning Outcome,” Int. J. of Serious Games, Vol.9, No.2, pp. 27-42, 2022.
  20. [20] J. Kang, D. An, L. Yan, and M. Liu, “Collaborative Problem-Solving Process in A Science Serious Game: Exploring Group Action Similarity Trajectory,” Proc. of 12th Int. Conf. on Educational Data Mining, pp. 336-341, 2019.
  21. [21] S. Comu, G. Kazar, and Z. Marwa, “Evaluating the attitudes of different trainee groups towards eye tracking enhanced safety training methods,” Advanced Engineering Informatics, Vol.49, Article No.101353, 2021.
  22. [22] J. Kang and M. Liu, “Investigating navigational behavior patterns of students across at-risk categories within an open-ended serious game,” Technology, Knowledge and Learning, Vol.27, No.1, pp. 183-205, 2022.
  23. [23] R. Thawonmas and K. Iizuka, “Haar wavelets for online-game player classification with dynamic time warping,” J. Adv. Comput. Intell. Intell. Inform., Vol.12, No.2, pp. 150-155, 2007.
  24. [24] T. Liu and M. Israel, “Uncovering students’ problem-solving processes in game-based learning environments,” Computers & Education, Vol.182, Article No.104462, 2022.
  25. [25] A. Sakata, T. Kikuchi, R. Okumura, M. Kunigami, A. Yoshikawa, M. Yamamura, and T. Terano, “The Shin-Life Career Game: Pursuing Your New Life Style Through Gaming Simulation,” 13th Int. Conf. on Information, Process, and Knowledge Management, pp. 14-20, 2021.
  26. [26] S. S. Boocock and J. S. Coleman, “Games with Simulated Environments in Learning,” Sociology of Education, Vol.39, No.3, pp. 215-236, 1966.
  27. [27] S. S. Boocock, “The Life Career Game,” The Personnel and Guidance J., Vol.46, No.4, pp. 328-334, 1967.
  28. [28] J. Ferrara, “Games for Persuasion: Argumentation, Procedurality, and the Lie of Gamification,” Games and Culture, Vol.8, No.4, pp. 289-304, 2013.
  29. [29] I. Dunwell, P. Lameras, S. d. Freitas, P. Petridis, K. Star, M. Hendrix, and S. Arnab, “MeTycoon: A game-based approach to career guidance,” 5th Int. Conf. on Games and Virtual Worlds for Serious Applications, 2013.
  30. [30] A. Sakata, T. Kikuchi, R. Okumura, M. Kunigami, A. Yoshikawa, M. Yamamura, and T. Terano, “A Basic Research to Develop a Method to Classify Game Logs and Analyze Them by Clusters,” 7th Int. Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII2021), Article No.T2-3-2, 2021.
  31. [31] W. Munsen, J. Horan, L. Miano, and C. I. Stone, “Another look at the Life Career Game,” Pennsylvania Personnel and Guidance Association J., Vol.4, pp. 36-38, 1976.
  32. [32] R. D. Duke, “Gaming: The Future’s Language,” John Wiley & Sons Inc., 1974.
  33. [33] V. I. Levenshtein, “Binary codes capable of correcting deletions, insertions, and reversals,” Soviet Physics Doklady, Vol.10, No.8, pp. 707-710, 1965.
  34. [34] J. H. Ward Jr., “Hierarchical Grouping to Optimize an Objective Function,” J. of the American Statistical Association, Vol.58, No.301, pp. 236-244, 1963.
  35. [35] J. R. Quinlan, “C4.5: Programs for Machine Learning,” Elsevier, 2014.
  36. [36] scikit-learn, “scikit-learn.” [Accessed February 28, 2023]

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