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摘要翻译：
领域无关规划是一个难的组合问题。考虑到计划质量使任务更加困难。本文介绍了通过重写进行规划(PbR)，这是一种高效、高质量的与领域无关的规划的新范例。PbR利用声明性计划重写规则和高效的局部搜索技术将易于生成但可能次优的初始计划转换为高质量的计划。除了解决规划效率和规划质量问题外，该框架还提供了一种新的随时规划算法。我们已经实现了这个规划器，并将其应用到几个现有的域中。实验结果表明，PbR方法在生成高质量计划的同时，大大节省了计划工作量。
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英文标题：
《Planning by Rewriting》
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作者：
J. L. Ambite, C. A. Knoblock
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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：
Domain-independent planning is a hard combinatorial problem. Taking into account plan quality makes the task even more difficult. This article introduces Planning by Rewriting (PbR), a new paradigm for efficient high-quality domain-independent planning. PbR exploits declarative plan-rewriting rules and efficient local search techniques to transform an easy-to-generate, but possibly suboptimal, initial plan into a high-quality plan. In addition to addressing the issues of planning efficiency and plan quality, this framework offers a new anytime planning algorithm. We have implemented this planner and applied it to several existing domains. The experimental results show that the PbR approach provides significant savings in planning effort while generating high-quality plans.
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PDF链接：
https://arxiv.org/pdf/1106.0250
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