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摘要翻译：
本文描述了参加第四届国际规划竞赛(IPC4)的规划师马文。Marvin使用操作序列记忆技术来生成宏操作，然后在搜索解决方案时使用宏操作。我们概述了它的体系结构和搜索行为，并详细说明了所使用的算法。我们还实证地证明了其特征在各种规划领域中的有效性；特别是宏操作的使用对性能的影响，其搜索行为的新特征，以及ADL和派生谓词的本地支持。
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英文标题：
《Marvin: A Heuristic Search Planner with Online Macro-Action Learning》
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作者：
A. I. Coles, A. J. 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：
This paper describes Marvin, a planner that competed in the Fourth International Planning Competition (IPC 4). Marvin uses action-sequence-memoisation techniques to generate macro-actions, which are then used during search for a solution plan. We provide an overview of its architecture and search behaviour, detailing the algorithms used. We also empirically demonstrate the effectiveness of its features in various planning domains; in particular, the effects on performance due to the use of macro-actions, the novel features of its search behaviour, and the native support of ADL and Derived Predicates.
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PDF链接：
https://arxiv.org/pdf/1110.2736
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