基于Agent的《愤怒的小鸟》动态难度自适应关卡生成
Agent-Based Adaptive Level Generation for Dynamic Difficulty Adjustment in Angry Birds
摘要：
针对物理类益智游戏《愤怒的小鸟》，提出了一种自适应关卡生成算法。所提出的算法是基于一个预先存在的水平发生器为这个游戏，但其中所产生的水平的难度可以根据玩家的表现进行调整。这允许创建专门针对玩家自身能力的个性化级别。我们提出的方法的有效性进行评估，使用不同的策略和AI技术的代理。通过使用这些代理作为真实人类玩家特征的模型/表示，我们可以在大量的世代中有效地优化级别属性。作为二次调查，我们还证明，通过将几个代理的性能结合在一起，可以生成对某些玩家特别具有挑战性的水平，而不是其他玩家。
英文摘要：
This paper presents an adaptive level generation algorithm for the physics-based puzzle game Angry Birds. The proposed algorithm is based on a pre-existing level generator for this game, but where the difficulty of the generated levels can be adjusted based on the player's performance. This allows for the creation of personalised levels tailored specifically to the player's own abilities. The effectiveness of our proposed method is evaluated using several agents with differing strategies and AI techniques. By using these agents as models / representations of real human player's characteristics, we can optimise level properties efficiently over a large number of generations. As a secondary investigation, we also demonstrate that by combining the performance of several agents together it is possible to generate levels that are especially challenging for certain players but not others.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
