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摘要翻译：
我们描述并评估了用于FF规划系统的算法技术。像HSP系统一样，FF依赖于前向状态空间搜索，使用一种启发式，通过忽略删除列表来估计目标距离。与HSP的启发式不同，我们的方法并不假设事实是独立的。本文提出了一种将爬山搜索与系统搜索相结合的新搜索策略，并说明了如何提取其他强大的启发式信息，并利用这些信息对搜索空间进行剪枝。在最近的AIPS-2000规划竞赛中，FF是最成功的自动规划器。我们回顾了比赛的结果，给出了其他基准域的数据，并研究了FF与HSP相比运行时性能下降的原因。
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英文标题：
《The FF Planning System: Fast Plan Generation Through Heuristic Search》
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作者：
J. Hoffmann, B. Nebel
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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：
We describe and evaluate the algorithmic techniques that are used in the FF planning system. Like the HSP system, FF relies on forward state space search, using a heuristic that estimates goal distances by ignoring delete lists. Unlike HSP's heuristic, our method does not assume facts to be independent. We introduce a novel search strategy that combines hill-climbing with systematic search, and we show how other powerful heuristic information can be extracted and used to prune the search space. FF was the most successful automatic planner at the recent AIPS-2000 planning competition. We review the results of the competition, give data for other benchmark domains, and investigate the reasons for the runtime performance of FF compared to HSP.
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PDF链接：
https://arxiv.org/pdf/1106.0675
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