通过学习领域内多个任务的地标关系，生成可复用的规划地标图谱。
From domain-landmark graph learning to problem-landmark graph generation
- 从多个任务中学习参数化地标的概率序关系，构建抽象图谱。
- 在新任务中通过初始与目标状态分别建图并合并，提取有效序关系。
- 适用于跨任务复用，提升自动化规划的通用性与效率。
地标在自动规划中长期发挥关键作用，是提升规划算法性能的重要元素。传统地标提取方法对特定规划任务敏感，导致地标仅针对单个实例定制，难以在同领域其他实例中复用。本文提出一种新方法，从同一规划领域的多个任务中学习地标间的关系，生成一个概率提升序图（probabilistic lifted ordering graph），该结构以加权形式捕获参数化地标间的抽象关系。尽管这些序关系并非百分之百准确（为概率性），但仍可在规划中发挥重要作用。当面对新规划任务时，该方法分两阶段进行实例化：首先分别基于初始状态和目标状态生成两个子图；其次通过搜索等价关系，将二者合并为统一图，提取出具体的地标序关系。我们在多个经典规划领域上评估了该方法在信息精度与召回率方面的表现。
原文摘要 · Abstract (English)
Landmarks have long played a pivotal role in automated planning, serving as crucial elements for improving the planning algorithms. The main limitation of classical landmark extraction methods is their sensitivity to specific planning tasks. This results in landmarks fully tailored to individual instances, thereby limiting their applicability across other instances of the same planning domain. We propose a novel approach that learns landmark relationships from multiple planning tasks of a planning domain. This leads to the creation of a \textit{probabilistic lifted ordering graph}, as a structure that captures weighted abstractions of relationships between parameterized landmarks. Although these orderings are not 100\% true (they are probabilistic), they can still be very useful in planning. Next, given a new planning task for that domain, we instantiate the relationships from that graph to this particular instance. This instantiation operates in two phases. First, it generates two graphs: the former instantiating information from the initial state and the latter from the goal state. Second, it combines these two graphs into one unified graph by searching equivalences to extract landmark orderings. We evaluate the precision and recallof the information found by our approach over well-known planning domains.
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