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摘要翻译：
随着规划被应用于更大更丰富的领域，构建领域描述所涉及的工作增加，并成为人类应用程序设计者的一个重大负担。如果要成功地将通用规划器应用于大型和复杂的领域，就有必要为领域设计人员提供一些帮助，帮助他们构建正确编码的领域。其中一种方法是提供与领域无关的技术，用于从领域描述中提取该描述中隐含的知识，这些知识可以帮助领域设计人员调试领域描述。这些知识也可以用来提高规划器的性能：一些研究人员已经探索了状态不变量在加快领域无关规划器性能方面的潜力。在本文中，我们描述了一个从自动推断的域类型结构中提取状态不变量的过程。这些技术正在由STAN开发，这是一个基于Graphplan的规划器，它使用状态分析技术来提高性能。
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英文标题：
《The Automatic Inference of State Invariants in TIM》
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作者：
M. Fox, D. Long
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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：
As planning is applied to larger and richer domains the effort involved in constructing domain descriptions increases and becomes a significant burden on the human application designer. If general planners are to be applied successfully to large and complex domains it is necessary to provide the domain designer with some assistance in building correctly encoded domains. One way of doing this is to provide domain-independent techniques for extracting, from a domain description, knowledge that is implicit in that description and that can assist domain designers in debugging domain descriptions. This knowledge can also be exploited to improve the performance of planners: several researchers have explored the potential of state invariants in speeding up the performance of domain-independent planners. In this paper we describe a process by which state invariants can be extracted from the automatically inferred type structure of a domain. These techniques are being developed for exploitation by STAN, a Graphplan based planner that employs state analysis techniques to enhance its performance.
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PDF链接：
https://arxiv.org/pdf/1105.5451
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