用于信念空间搜索的规划图启发式算法

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摘要翻译：
近年来条件规划中的一些工作提出了可达性启发式方法来提高规划器的可伸缩性，但许多工作缺乏对其距离估计性质的形式化描述。为了将以前的工作放在上下文中并扩展条件规划的启发式工作，我们提供了信念状态之间距离估计的形式基础。我们给出了信念状态间距离的定义，它依赖于集合潜在的状态距离度量。我们给出了几种聚合状态距离及其相关性质的技术。许多已有的启发式都表现出一个子集的性质，但为了提供一个标准化的比较，我们提出了几个推广的规划图启发式，用于一个单一的规划器。我们还研究了有效的规划图数据结构，将BDDs用于计算最有效的启发式，从而对我们的信念状态距离估计框架进行了补充。我们开发了两个规划师作为我们调查的试验台。第一个是CAltAlt，是一个使用*搜索的符合回归规划器。第二个是POND，是一个使用AO*搜索的条件进度计划器。我们在这些规划器中展示了我们的启发式技术的相对有效性。我们还比较了这些规划者与条件规划中几种最先进的方法的性能。
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英文标题：
《Planning Graph Heuristics for Belief Space Search》
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作者：
D. Bryce, S. Kambhampati, D. E. Smith
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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：
  Some recent works in conditional planning have proposed reachability heuristics to improve planner scalability, but many lack a formal description of the properties of their distance estimates. To place previous work in context and extend work on heuristics for conditional planning, we provide a formal basis for distance estimates between belief states. We give a definition for the distance between belief states that relies on aggregating underlying state distance measures. We give several techniques to aggregate state distances and their associated properties. Many existing heuristics exhibit a subset of the properties, but in order to provide a standardized comparison we present several generalizations of planning graph heuristics that are used in a single planner. We compliment our belief state distance estimate framework by also investigating efficient planning graph data structures that incorporate BDDs to compute the most effective heuristics.   We developed two planners to serve as test-beds for our investigation. The first, CAltAlt, is a conformant regression planner that uses A* search. The second, POND, is a conditional progression planner that uses AO* search. We show the relative effectiveness of our heuristic techniques within these planners. We also compare the performance of these planners with several state of the art approaches in conditional planning.
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PDF链接：
https://arxiv.org/pdf/1103.1711

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关键词：规划图 启发式 Presentation Standardized Intelligence planning several 我们 估计 条件