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Distance-based Goal-ordering heuristics for Graphplan

机译:Graphplan基于距离的目标排序启发法

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摘要

We will discuss the shortcomings of known variable and value ordering strategies for Graphplan's backward search phase, and propose a novel strategy that is based on a notion of the difficulty of achieving the corresponding subgoal. The difficulty of achievement is quantified in terms of the structure of the planning graph itself-specifically, the earliest level of the planning-graph at which that subgoal appears. We will present empirical results showing the surprising effectiveness of this simple heuristic on benchmark problems. We will end by contrasting the way distance-based heuristics are used in Graphplan and state-search planners like UNPOP, HSP and HSP-R.
机译:我们将讨论Graphplan向后搜索阶段的已知变量和值排序策略的缺点,并提出一种基于实现相应子目标难度的概念的新颖策略。实现计划的难度通过计划图本身的结构进行量化,具体来说,即子目标出现的计划图的最早级别。我们将提供经验结果,表明这种简单的启发式基准问题的惊人效果。我们将通过对比Graphplan和状态搜索计划程序(如UNPOP,HSP和HSP-R)中基于距离的启发式方法的使用方法来结束。

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