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摘要翻译：
许多已知的规划任务在实现目标的最佳顺序方面都有内在的约束。为了发现这些限制因素并将其用于指导搜索，已经进行了一些研究工作，希望加快规划过程。我们超越了以前的方法，不仅考虑了（顶层）目标的排序约束，而且考虑了在规划过程中必然出现的子目标的排序约束。地标是在每一个有效的解决方案计划中的某一点上必须为真的事实。我们将Koehler和Hoffmann关于顶层目标之间合理顺序的定义推广到更一般的地标情形。我们展示了如何找到地标，如何近似它们的合理顺序，以及如何利用这些信息将给定的规划任务分解为几个较小的子任务。我们的方法是完全独立于领域和计划者的。该方法在FF和LPG等先进的次优规划系统中作为一个控制回路，可以显著提高系统的运行时性能。
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英文标题：
《Ordered Landmarks in Planning》
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作者：
J. Hoffmann, J. Porteous, L. Sebastia
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最新提交年份：
2011
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Many known planning tasks have inherent constraints concerning the best order in which to achieve the goals. A number of research efforts have been made to detect such constraints and to use them for guiding search, in the hope of speeding up the planning process. We go beyond the previous approaches by considering ordering constraints not only over the (top-level) goals, but also over the sub-goals that will necessarily arise during planning. Landmarks are facts that must be true at some point in every valid solution plan. We extend Koehler and Hoffmann's definition of reasonable orders between top level goals to the more general case of landmarks. We show how landmarks can be found, how their reasonable orders can be approximated, and how this information can be used to decompose a given planning task into several smaller sub-tasks. Our methodology is completely domain- and planner-independent. The implementation demonstrates that the approach can yield significant runtime performance improvements when used as a control loop around state-of-the-art sub-optimal planning systems, as exemplified by FF and LPG.
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PDF链接：
https://arxiv.org/pdf/1107.0052

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关键词：Intelligence Improvements Presentation Constraints Approximate Hoffmann 系统 进行 planning sub