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摘要翻译：
大多数当前的规划者假设完整的领域模型，并专注于生成正确的计划。不幸的是，领域建模是一项费力且容易出错的任务。虽然领域专家不能保证完整，但他们通常能够通过提供领域模型哪些部分可能不完整的注释来限定模型的不完整。在这种情况下，目标应该是生成对于域的任何已知不完备性都是健壮的计划。本文首先引入表示区域不完备性知识的注解，并对不完备区域模型形式化了规划鲁棒性的概念。然后，我们提出了一种将寻找鲁棒计划的问题编译成一致性概率规划问题的方法。我们用最先进的规划器概率-FF给出了实验结果，显示了我们的方法的前景。
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英文标题：
《Synthesizing Robust Plans under Incomplete Domain Models》
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作者：
Tuan Nguyen and Subbarao Kambhampati and Minh Do
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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：
Most current planners assume complete domain models and focus on generating correct plans. Unfortunately, domain modeling is a laborious and error-prone task. While domain experts cannot guarantee completeness, often they are able to circumscribe the incompleteness of the model by providing annotations as to which parts of the domain model may be incomplete. In such cases, the goal should be to generate plans that are robust with respect to any known incompleteness of the domain. In this paper, we first introduce annotations expressing the knowledge of the domain incompleteness, and formalize the notion of plan robustness with respect to an incomplete domain model. We then propose an approach to compiling the problem of finding robust plans to the conformant probabilistic planning problem. We present experimental results with Probabilistic-FF, a state-of-the-art planner, showing the promise of our approach.
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PDF链接：
https://arxiv.org/pdf/1104.5069
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