The case for dynamic difficulty adjustment in games 论文
2005引用 425
Artificial Intelligence in GamesEducational Games and GamificationReinforcement Learning in Robotics
详细信息
- 发表日期
- 2005-06-15
- 发表年份
- 2005
关键词
Artificial Intelligence in GamesEducational Games and GamificationReinforcement Learning in Robotics
摘要
Conventional wisdom suggests that while players enjoy unpredictability or novelty during gameplay experiences, they will feel "cheated" if games are adjusted during or across play sessions. In order for adjustment to be effective, it must be performed without disrupting or degrading the core player experience. This paper examines basic design requirements for effective dynamic difficulty adjustment (DDA) given this constraint, presents an interactive DDA system (Hamlet), and offers preliminary evaluation results which challenge common assumptions about player enjoyment and adjustment dynamics.