Dynamic Difficulty Adjustment for Maximized Engagement in Digital Games

ABSTRACT

Dynamic difficulty adjustment (DDA) is a technique for adaptively changing a game to make it easier or harder. A common paradigm to achieve DDA is through heuristic prediction and intervention, adjusting game difficulty once undesirable player states (e.g., boredom or frustration) are observed. Without quantitative objectives, it is impossible to optimize the strength of intervention and achieve the best effectiveness. In this paper, we propose a DDA framework with a global optimization objective of maximizing a player's engagement throughout the entire game. Using level-based games as our example, we model a player's progression as a probabilistic graph. Dynamic difficulty reduces to optimizing transition probabilities to maximize a player's stay time in the progression graph. We have successfully developed a system that applies this technique in multiple games by Electronic Arts, Inc., and have observed up to 9% improvement in player engagement with a neutral impact on monetization.

References

1. Z. Benenson, F. Gassmann, and L. Reinfelder. Android and ios users' differences concerning security and privacy. In CHI '13 Extended Abstracts on Human Factors in Computing Systems, CHI EA '13, pages 817--822, New York, NY, USA, 2013. ACM.
2. R. Bernhaupt. User experience evaluation in entertainment. In Evaluating User Experience in Games, pages 3--7. Springer, 2010.
3. M. Csikszentmihalyi and I. S. Csikszentmihalyi. Optimal experience: Psychological studies of flow in consciousness. Cambridge university press, 1992.
4. P. Demasi and J. d. O. Adriano. On-line coevolution for action games. International Journal of Intelligent Games & Simulation, 2(2), 2003.
5. F. Hadiji, R. Sifa, A. Drachen, C. Thurau, K. Kersting, and C. Bauckhage. Predicting player churn in the wild. In 2014 IEEE Conference on Computational Intelligence and Games, pages 1--8. IEEE, 2014.
6. J. Hagelback and S. J. Johansson. Measuring player experience on runtime dynamic difficulty scaling in an rts game. In 2009 IEEE Symposium on Computational Intelligence and Games, pages 46--52. IEEE, 2009.
7. G. Hawkins, K. Nesbitt, and S. Brown. Dynamic difficulty balancing for cautious players and risk takers. Int. J. Comput. Games Technol., 2012:3:3--3:3, Jan. 2012.
8. 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, pages 429--433. ACM, 2005.
9. M. Jennings-Teats, G. Smith, and N. Wardrip-Fruin. Polymorph: dynamic difficulty adjustment through level generation. In Proceedings of the 2010 Workshop on Procedural Content Generation in Games, page 11. ACM, 2010.
10. S. Karpinskyj, F. Zambetta, and L. Cavedon. Video game personalisation techniques: A comprehensive survey. Entertainment Computing, 5(4):211--218, 2014.
11. O. Missura and T. G\"artner. Predicting dynamic difficulty. In J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. Pereira, and K. Q. Weinberger, editors, Advances in Neural Information Processing Systems 24, pages 2007--2015. Curran Associates, Inc., 2011.
12. J. Runge, P. Gao, F. Garcin, and B. Faltings. Churn prediction for high-value players in casual social games. In 2014 IEEE Conference on Computational Intelligence and Games, pages 1--8. IEEE, 2014.
13. P. Spronck, I. Sprinkhuizen-Kuyper, and E. Postma. Difficulty scaling of game AI. In Proceedings of the 5th International Conference on Intelligent Games and Simulation (GAME-ON 2004), pages 33--37, 2004.
14. C. Zhou, Y. Guo, Y. Chen, X. Nie, and W. Zhu. Characterizing user watching behavior and video quality in mobile devices. In 2014 23rd International Conference on Computer Communication and Networks (ICCCN), pages 1--6, Aug 2014.
15. A. Zook, S. Lee-Urban, M. R. Drinkwater, and M. O. Riedl. Skill-based mission generation: A data-driven temporal player modeling approach. In Proceedings of the The Third Workshop on Procedural Content Generation in Games, PCG'12, pages 6:1--6:8, New York, NY, USA, 2012. ACM.
16. A. Zook and M. O. Riedl. A temporal data-driven player model for dynamic difficulty adjustment. In Artificial Intelligence and Interactive Digital Entertainment, 2012.

Index Terms

• Computing methodologies

  • Machine learning

    • Machine learning approaches

      • Learning in probabilistic graphical models

      • Stochastic games