摘要翻译：
在本文中，我们回顾了标准的空移剪枝，并介绍了我们的扩展版本，我们称之为验证的空移剪枝。在验证的空移剪枝中，每当浅空移搜索指示失败高时，不是切断与当前节点的搜索，而是以减小的深度继续搜索。我们通过验证的空移剪枝实验表明，与标准的空移剪枝相比，平均而言，它构造了一个更小的搜索树，具有更大的战术强度。此外，与标准的空移剪枝在zugzwang位置失败严重不同，验证的空移剪枝能够检测到大多数zugzwang位置，并在这种情况下进行重新搜索以获得正确的结果。另外，验证的空移剪枝非常容易实现，任何标准的空移剪枝程序只需修改几行代码就可以使用验证的空移剪枝。
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英文标题：
《Verified Null-Move Pruning》
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作者：
Omid David-Tabibi and Nathan S. Netanyahu
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最新提交年份：
2008
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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 this article we review standard null-move pruning and introduce our extended version of it, which we call verified null-move pruning. In verified null-move pruning, whenever the shallow null-move search indicates a fail-high, instead of cutting off the search from the current node, the search is continued with reduced depth.   Our experiments with verified null-move pruning show that on average, it constructs a smaller search tree with greater tactical strength in comparison to standard null-move pruning. Moreover, unlike standard null-move pruning, which fails badly in zugzwang positions, verified null-move pruning manages to detect most zugzwangs and in such cases conducts a re-search to obtain the correct result. In addition, verified null-move pruning is very easy to implement, and any standard null-move pruning program can use verified null-move pruning by modifying only a few lines of code.
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PDF链接：
https://arxiv.org/pdf/0808.1125