Emotion-Based Dynamic Difficulty Adjustment Using Parameterized Difficulty And Self-Reports Of Emotion

Research has shown that dynamic difficulty adjustment (DDA) can benefit player experience in digital games. However, in some cases it can be difficult to assess when adjustments are necessary. In this paper, we propose an approach of emotion-based DDA that uses self-reported emotions to inform when an adaptation is necessary. In comparison to earlier DDA techniques based on affect, we use parameterized difficulty to define difficulty levels and select the suitable level based on players' frustration and boredom. We conducted a user study with 66 participants investigating performance and effects on player experience and perceived competence of this approach. The study further explored how self-reports of emotional state can be integrated in dialogues with non-player characters to provide less interruption. The results show that our emotion-based DDA approach works as intended and yields better player experience than constant or increasing difficulty approaches. While the dialogue-based self-reports did not positively affect player experience, they yielded high accuracy. Together, these findings indicate our emotion-based approach works as intended and provides good player experience, thus representing a useful tool for game developers to easily implement reliable DDA.