摘要翻译：
我们提出了两种不同的方法来估计解决SAT问题的成本。这些方法侧重于回溯求解器的在线行为，以及问题的结构。现代SAT求解器在估计搜索成本方面存在一些挑战，包括应对非时序回溯、学习和重启。我们的第一种方法改进了现有的估计搜索树大小的算法来解决这些问题。然后，我们提出了第二种方法，使用在搜索开始时在线收集的数据上训练的线性模型。我们使用随机和结构化问题比较了这两种方法的有效性。我们还证明了在早期重启中所做的预测可以用来改进以后的预测。我们的结论是，基于这样的成本估计，从投资组合中选择一个求解者可以减少解决一组问题的成本。
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英文标题：
《Online Search Cost Estimation for SAT Solvers》
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作者：
Shai Haim and Toby Walsh
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最新提交年份：
2009
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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：
  We present two different methods for estimating the cost of solving SAT problems. The methods focus on the online behaviour of the backtracking solver, as well as the structure of the problem. Modern SAT solvers present several challenges to estimate search cost including coping with nonchronological backtracking, learning and restarts. Our first method adapt an existing algorithm for estimating the size of a search tree to deal with these challenges. We then suggest a second method that uses a linear model trained on data gathered online at the start of search. We compare the effectiveness of these two methods using random and structured problems. We also demonstrate that predictions made in early restarts can be used to improve later predictions. We conclude by showing that the cost of solving a set of problems can be reduced by selecting a solver from a portfolio based on such cost estimations.
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PDF链接：
https://arxiv.org/pdf/0907.5033