基于案例的多任务寻路算法

Yan Li, Lan-Ming Su, Qiang He
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引用次数: 2

摘要

寻径是电脑游戏中的一项重要任务,算法效率是关键问题。本文在A*算法的多任务寻径过程中引入了基于实例的推理方法。首先,我们将一些典型路径保存为案例。当有新任务到来时,它不再使用a *从头开始寻找路径,而是首先计算新任务与存储案例的相似度,以决定是否沿着先前找到的路径前进。对发现的相似案例进行适应,得到新任务的解。显然,这种基于内存的寻路可以减少搜索时间,但代价是使用更多的内存来存储找到的路径。实验结果表明,随着存储路径数量的增加,寻路过程中需要搜索的节点越来越少。
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Case-based multi-task pathfinding algorithm
Pathfinding is an important task in computer games, where the algorithm efficiency is the key issue. In this paper, we introduce case-based reasoning method in the process of A* algorithm in multi-task pathfinding. Firstly, we save some typical paths as cases. When a new task is coming, it no longer uses A* to find a path from scratch, but firstly computes the similarity of the new task and the stored cases to decide whether to go along the previous found paths or not. A solution to the new task will be obtained after adapting to the found similar case(s). Obviously, this memory-based pathfinding can reduce the search time at the cost of using more memory to store found paths as cases. Through experimental results, it is demonstrated that, as the number of stored paths is increasing, fewer nodes are needed to be searched during the pathfinding process.
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