摘要翻译：
约束问题可以通过并行探索搜索树的不同分支来并行解决。以前的方法侧重于在求解器中实现该功能，对用户来说或多或少是透明的。我们提出了一种新的方法，改进了问题的约束模型。一个现有的模型被分割成新的模型，新的模型增加了分割搜索空间的约束。可选地，还会施加额外的约束，以排除已经完成的搜索。我们的方法的优点是易于实现，计算可以停止和重新开始，转移到不同的机器上，实际上在根本无法相互通信的机器上求解。
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英文标题：
《Distributed solving through model splitting》
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作者：
Lars Kotthoff and Neil C.A. Moore
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最新提交年份：
2010
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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：
  Constraint problems can be trivially solved in parallel by exploring different branches of the search tree concurrently. Previous approaches have focused on implementing this functionality in the solver, more or less transparently to the user. We propose a new approach, which modifies the constraint model of the problem. An existing model is split into new models with added constraints that partition the search space. Optionally, additional constraints are imposed that rule out the search already done. The advantages of our approach are that it can be implemented easily, computations can be stopped and restarted, moved to different machines and indeed solved on machines which are not able to communicate with each other at all.
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PDF链接：
https://arxiv.org/pdf/1008.4328