摘要翻译：
本文提出了一个与领域无关的启发式条带规划系统GRT。GRT分两个阶段解决问题。在预处理阶段，它估计每个事实与问题目标之间的距离，在一个向后的方向。然后，在搜索阶段，这些估计被用来进一步估计每个中间状态与目标之间的距离，从而在最佳优先的基础上引导搜索过程向前发展。本文介绍了在预处理阶段和搜索阶段之间采用相反方向的方法的好处，讨论了在预处理阶段出现的一些困难，并介绍了解决这些困难的技术。此外，本文还提出了几种提高启发式效率的方法，通过丰富启发式的表示形式和减小问题的规模来提高启发式的效率。最后，提出了一种基于区域公理的克服局部最优状态的方法。根据它，困难的问题被分解成必须顺序解决的更容易的子问题。从各个领域的表现结果，包括最近的规划竞赛，表明GRT是最快的规划者之一。
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英文标题：
《The GRT Planning System: Backward Heuristic Construction in Forward
  State-Space Planning》
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作者：
I. Refanidis, I. Vlahavas
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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：
  This paper presents GRT, a domain-independent heuristic planning system for STRIPS worlds. GRT solves problems in two phases. In the pre-processing phase, it estimates the distance between each fact and the goals of the problem, in a backward direction. Then, in the search phase, these estimates are used in order to further estimate the distance between each intermediate state and the goals, guiding so the search process in a forward direction and on a best-first basis. The paper presents the benefits from the adoption of opposite directions between the preprocessing and the search phases, discusses some difficulties that arise in the pre-processing phase and introduces techniques to cope with them. Moreover, it presents several methods of improving the efficiency of the heuristic, by enriching the representation and by reducing the size of the problem. Finally, a method of overcoming local optimal states, based on domain axioms, is proposed. According to it, difficult problems are decomposed into easier sub-problems that have to be solved sequentially. The performance results from various domains, including those of the recent planning competitions, show that GRT is among the fastest planners.
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PDF链接：
https://arxiv.org/pdf/1106.0285