论文标题
高血压和总订单前向分解优化
HyperTensioN and Total-order Forward Decomposition optimizations
论文作者
论文摘要
分层任务网络（HTN）计划者使用具有额外域知识的分解过程生成计划，以指导搜索计划任务。尽管域专家会开发HTN描述，但他们可能会反复描述相同的前提条件或很少使用或可能被分解的方法。通过利用三阶段的编译器设计，我们可以轻松地支持更多的语言描述和预处理优化，这些优化可以极大地提高此类域中的运行时效率。在本文中，我们使用HTN IPC 2020中使用的高血压HTN计划者评估了这种优化。
Hierarchical Task Networks (HTN) planners generate plans using a decomposition process with extra domain knowledge to guide search towards a planning task. While domain experts develop HTN descriptions, they may repeatedly describe the same preconditions, or methods that are rarely used or possible to be decomposed. By leveraging a three-stage compiler design we can easily support more language descriptions and preprocessing optimizations that when chained can greatly improve runtime efficiency in such domains. In this paper we evaluate such optimizations with the HyperTensioN HTN planner, used in the HTN IPC 2020.
