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摘要翻译：
我们提出了一种在线估算解决SAT问题成本的方法。现代SAT求解器在估计搜索成本方面面临着一些挑战，包括非时间回溯、学习和重启。我们的方法使用在搜索开始时收集的数据上训练的线性模型。我们用随机和结构化问题证明了该方法的有效性。我们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。我们还表明，我们可以使用这样的成本估计来选择一个解决方案从一个投资组合。
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英文标题：
《Online Estimation of SAT Solving Runtime》
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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 an online method for estimating the cost of solving SAT problems. Modern SAT solvers present several challenges to estimate search cost including non-chronological backtracking, learning and restarts. Our method uses a linear model trained on data gathered at the start of search. We show the effectiveness of this method using random and structured problems. We demonstrate that predictions made in early restarts can be used to improve later predictions. We also show that we can use such cost estimations to select a solver from a portfolio.
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PDF链接：
https://arxiv.org/pdf/0903.0695
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