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.

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