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Using a Disjoint Skill Model for Game and Task Difficulty in Human Computation Games

Prior research has used player rating systems to balance difficulty in human computation games (HCGs) without having to modify their levels by assigning ratings to levels to indicate level difficulty. Skill chains have also been used to define difficulty progressions for such games. Both these methods typically involve associating a level with a single rating or set of skills as being representative of the difficulty of both the in-game mechanics of the level and the complexity of the task that it models, taken together as a single unit. Though effective, this may not be suitable for HCGs where the game and the task being modeled require different sets of skills and abilities. To this end, we introduce a disjoint skill model that separately tracks game and task skill and difficulty in a 2D platformer HCG. We find that the disjoint model enables players to solve more difficult tasks compared to a baseline model.