Martin Jennings-Teats
ABSTRACT
Players begin games at different skill levels and develop their skill at different rates so that even the best-designed games are uninterestingly easy for some players and frustratingly difficult for others. A proposed answer to this challenge is Dynamic Difficulty Adjustment (DDA), a general category of approaches that alter games during play, in response to player performance. However, nearly all these techniques are focused on basic parameter tweaking, while the difficulty of many games is connected to aspects that are more challenging to adjust dynamically, such as level design. Further, most DDA techniques are based on designer intuition, which may not reflect actual play patterns. Responding to these challenges, we present Polymorph, which employs techniques from level generation and machine learning to understand game component difficulty and player skill, dynamically constructing a 2D platformer game with continually-appropriate challenge. We believe this will create a play experience that is unique because the changes are both personalized and structural, while also providing an example of a promising new research and development approach.
- Blizzard Entertainment 1997. Diablo.Google Scholar
- Booth, M. 2009. The AI Systems of Left 4 Dead. Keynote, Fifth Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE '09). Stanford, CA. October 14--16, 2009.Google Scholar
- Fullerton, T., Swain, C., and Hoffman, S. 2004. Improving player choices. Gamasutra (March 2004). http://www.gamasutra.com/features/20040310/fullerton_01.s html. Online Feb. 1, 2005.Google Scholar
- Herbrich, R., Graepel, T., and Obermayer, K. 2000. Large Margin Rank Boundaries for Ordinal Regression. Advances in Large Margin Classifiers, 115--132, Liu Press.Google Scholar
- Hunicke, R. 2005. The case for dynamic difficulty adjustment in games. In Proceedings of the 2005 ACM SIGCHI international Conference on Advances in Computer Entertainment Technology (Valencia, Spain, June 15--17, 2005). ACE '05, vol. 265. ACM, New York, NY, 429--433. Google ScholarDigital Library
- Juul, J. 2009. Fear of Failing? The Many Meanings of Difficulty in Video Games. The Video Game Theory Reader 2, B. Perron and M. Wolf, Ed. Routledge, London.Google Scholar
- Kazemi, D. 2008. Metrics and Dynamic Difficulty in Ritual's SiN Episodes. OrbusGameWorks.com. http://orbusgameworks.com/blog/article/70/metrics-and-dynamic-difficulty-in-rituals-sin-episodes-part-1Google Scholar
- Nintendo 1985. Super Mario Bros.Google Scholar
- Nitsche, M., Ashmore, C., Hankinson, W., Fitzpatrick, R., Kelly, J., and Margenau, K. 2006. Designing Procedural Game Spaces: A Case Study. In Proceedings of FuturePlay 2006. London, Ontario. October 10--12, 2006.Google Scholar
- Pedersen, C., Togelius, J., and Yannakakis, G. 2009. Modeling Player Experience in Super Mario Bros. Proceedings of the 2009 IEEE Symposium on Computational Intelligence and Games (Politecnico di Milano, Milano, Italy, September 07--10, 2009). Google ScholarDigital Library
- Persson, M. Infinite Mario Bros.Google Scholar
- Phillips, B. 2009. Staying Power: Rethinking Feedback to Keep Players in the Game. Gamasutra.com. http://www.gamasutra.com/view/feature/4171/staying_power _rethinking_feedback_.phpGoogle Scholar
- Ritual Entertainment 1998. SiN Episodes.Google Scholar
- Smith, G., Treanor, M., Whitehead, J., Mateas, M. 2009. Rhythm-Based Level Generation for 2D Platformers. Proceedings of the 2009 Int'l Conference on the Foundations of Digital Games (Orlando, FL, USA, April 26--30, 2009). Google ScholarDigital Library
- Togelius, J., De Nardi, R., and Lucas, S. 2007. Towards automatic personalised content creation for racing games. Proceedings of the 2007 IEEE Symposium on Computational Intelligence and Games (2007).Google ScholarDigital Library
- Togelius, J., Yannakakis, G., Stanley, K., and Browne, C. 2010. Search-based Procedural Content Generation. To be presented at Evostar (Istanbul Technical University, Istanbul, Turkey, April 07--09, 2010).Google Scholar
- Toy, M. and Wichman, G. 1980. Rogue.Google Scholar
- Valve Software. 2008. Left 4 Dead.Google Scholar
- Yu, D. 2009. Spelunky.Google Scholar
Index Terms
- Polymorph: dynamic difficulty adjustment through level generation
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