摘要翻译：
变量消除是约束处理的一种通用技术。由于其空间复杂度高，常被丢弃。然而，当与其他技术结合时，它可能非常有用。本文研究了变量消元法在寻找静物这一具有挑战性的问题中的适用性。我们举例说明了几个备选方案：变量消除作为一个独立的算法，与搜索交织，以及作为一个良好质量的下界来源。我们表明，这些技术是最好的选择，无论是理论和经验。在我们的实验中，我们已经能够解决n=20的情况，这是其他方法远远无法达到的。
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英文标题：
《On the Practical use of Variable Elimination in Constraint Optimization
  Problems: 'Still-life' as a Case Study》
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作者：
J. Larrosa, E. Morancho, D. Niso
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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：
  Variable elimination is a general technique for constraint processing. It is often discarded because of its high space complexity. However, it can be extremely useful when combined with other techniques. In this paper we study the applicability of variable elimination to the challenging problem of finding still-lifes. We illustrate several alternatives: variable elimination as a stand-alone algorithm, interleaved with search, and as a source of good quality lower bounds. We show that these techniques are the best known option both theoretically and empirically. In our experiments we have been able to solve the n=20 instance, which is far beyond reach with alternative approaches.
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PDF链接：
https://arxiv.org/pdf/1109.2131