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Incremental ARA*: An Incremental Anytime Search Algorithm for Moving-Target Search

机译:增量ARA *:移动目标搜索的增量随时搜索算法

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Moving-target search, where a hunter has to catch a moving target, is an important problem for video game developers. In our case, the hunter repeatedly moves towards the target and thus has to solve similar search problems repeatedly. We develop Incremental ARA* (I-ARA*) for this purpose, the first incremental anytime search algorithm for movingtarget search in known terrain. We provide an error bound on the lengths of the paths found by I-ARA* and show experimentally in known four-neighbor gridworlds that I-ARA* can be used with smaller time limits between moves of the hunter than competing state-of-the-art moving-target search algorithms, namely repeated A*, G-FRA*, FRA*, and sometimes repeated ARA*. The hunter tends to make more moves with I-ARA* than repeated A*, G-FRA* or FRA*, which find shortest paths for the hunter, but fewer moves with I-ARA* than repeated ARA*, which finds suboptimal paths for the hunter like I-ARA*. Also, the error bounds on the lengths of the paths of the hunter tend to be smaller with I-ARA* than repeated ARA*.
机译:移动目标搜索,猎人必须捕获移动目标,是视频游戏开发人员的重要问题。在我们的情况下,猎人反复向目标移动,因此必须反复解决类似的搜索问题。我们为此目的开发增量ARA *(I-ARA *),是在已知地形中的移动特征搜索的第一个增量随时搜索算法。我们在I-ARA *发现的路径的长度上提供错误,并在已知的四邻格世界中进行实验显示,即I-ARA *可以在猎人的动作之间与竞争状态的较小的时间限制一起使用-ART移动目标搜索算法,即重复A *,G-FRA *,FRA *,有时重复ARA *。猎人倾向于与I-ARA *重复一个*,G-FRA *或FRA *,这对猎人的最短路径进行了更多的动作,但是与I-ARA *相比,较少的ARA *逐渐减少,这发现次优路径对于像I-ARA这样的猎人*。此外,猎人路径的长度上的误差界限往往与I-ARA *比重复的ARA *更小。

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