摘要翻译：
在一般推理机制的研究领域中，有适当的基准是必不可少的。理想情况下，基准应该反映已开发技术的可能应用。在人工智能规划中，研究人员越来越倾向于从国际规划竞赛(IPC)中使用的基准集合中提取测试示例。因此，在第四个IPC-4（确定性部分）的组织中，作者投入了大量精力来创建一组有用的基准。它们来自五个不同的（潜在的）现实世界规划应用：机场地面交通控制、管道网络中的石油衍生品运输、模型检查安全特性、电力供应恢复和UMTS呼叫设置。调整和准备这样一个应用程序以作为IPC中的基准，在当时涉及不可避免的（通常是剧烈的）简化，以及在域编码之间的仔细选择和设计。在IPC中，我们第一次使用编译以简单语言（如STRIPS）来表示复杂的领域特性，而不是仅仅在简单语言子集中删除更有趣的问题约束。本文解释和讨论了五个应用领域及其适应性，以形成IPC-4中使用的PDDL测试套件。我们总结了已知的关于区域结构性质的理论结果，包括它们的计算复杂性和它们在H+函数（松弛计划启发式的理想化版本）下拓扑的可证明性质。我们提出了新的（经验的）结果来说明一些性质，如最广泛的启发式函数（计划图、序列计划图和放松计划）的质量，命题表示在实例大小上的增长，以及实现每个事实的可用动作的数量；我们结合参加IPC-4的不同类型的规划者所取得的最佳成果来讨论这些数据。
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英文标题：
《Engineering Benchmarks for Planning: the Domains Used in the
  Deterministic Part of IPC-4》
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作者：
S. Edelkamp, R. Englert, J. Hoffmann, F. Liporace, S. Thiebaux, S.
  Trueg
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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：
  In a field of research about general reasoning mechanisms, it is essential to have appropriate benchmarks. Ideally, the benchmarks should reflect possible applications of the developed technology. In AI Planning, researchers more and more tend to draw their testing examples from the benchmark collections used in the International Planning Competition (IPC). In the organization of (the deterministic part of) the fourth IPC, IPC-4, the authors therefore invested significant effort to create a useful set of benchmarks. They come from five different (potential) real-world applications of planning: airport ground traffic control, oil derivative transportation in pipeline networks, model-checking safety properties, power supply restoration, and UMTS call setup. Adapting and preparing such an application for use as a benchmark in the IPC involves, at the time, inevitable (often drastic) simplifications, as well as careful choice between, and engineering of, domain encodings. For the first time in the IPC, we used compilations to formulate complex domain features in simple languages such as STRIPS, rather than just dropping the more interesting problem constraints in the simpler language subsets. The article explains and discusses the five application domains and their adaptation to form the PDDL test suites used in IPC-4. We summarize known theoretical results on structural properties of the domains, regarding their computational complexity and provable properties of their topology under the h+ function (an idealized version of the relaxed plan heuristic). We present new (empirical) results illuminating properties such as the quality of the most wide-spread heuristic functions (planning graph, serial planning graph, and relaxed plan), the growth of propositional representations over instance size, and the number of actions available to achieve each fact; we discuss these data in conjunction with the best results achieved by the different kinds of planners participating in IPC-4.
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PDF链接：
https://arxiv.org/pdf/1110.1016