Evaluating and Comparing Skill Chains and Rating Systems for Dynamic Difficulty Adjustment

Authors

  • Anurag Sarkar Northeastern University
  • Seth Cooper Northeastern University

Abstract

Skill chains define how in-game skills build on each other and the order in which players ideally acquire them during gameplay. This can enable dynamic difficulty adjustment (DDA) by serving levels based on the skills that players currently have and those required to solve a given level. Similarly, DDA can also be achieved by using rating systems to match players with suitable levels by assigning ratings to players and levels based on ability and difficulty respectively. However, the relative effects of these two methods remain unclear, particularly in the context of human computation games (HCGs). In this paper, we present a general model for using skill chains and rating systems in a combined DDA system along with an evaluation comparing the two for difficulty balancing within HCGs, focusing on the relative merits of both methods when used separately as well as together. We evaluate our methods using the HCGs Iowa James and Paradox. Our findings suggest that incorporating skill chains can improve upon previously shown benefits of using only rating systems for DDA in HCGs.

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Published

2020-10-01

How to Cite

Sarkar, A., & Cooper, S. (2020). Evaluating and Comparing Skill Chains and Rating Systems for Dynamic Difficulty Adjustment. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 16(1), 273-279. Retrieved from https://ojs.aaai.org/index.php/AIIDE/article/view/7441