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Top-k Based Adaptive Enumeration in Constraint Programming

机译：约束规划中基于Top-k的自适应枚举

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摘要

Constraint programming effectively solves constraint satisfaction and optimization problems by basically building, pruning, and exploring a search tree of potential solutions. In this context, a main component is the enumeration strategy, which is responsible for selecting the order in which variables and values are selected to build a possible solution. This process is known to be quite important; indeed a correct selection can reach a solution without failed explorations. However, it is well known that selecting the right strategy is quite challenging as their performance is notably hard to predict. During the last years, adaptive enumeration appeared as a proper solution to this problem. Adaptive enumeration allows the solving algorithm being able to autonomously modifying its strategies in solving time depending on performance information. In this way, the most suitable order for variables and values is employed along the search. In this paper, we present a new and more lightweight approach for performing adaptive enumeration. We incorporate a powerful classification technique named Top-k. in order to adaptively select strategies along the resolution. We report results on a set of well-known benchmarks where the proposed approach noticeably competes with classical and modern adaptive enumeration methods for constraint satisfaction.
机译：约束编程通过基本构建，修剪和探索潜在解决方案的搜索树来有效解决约束满足和优化问题。在这种情况下，枚举策略是主要的组成部分，它负责选择选择变量和值以构建可能的解决方案的顺序。众所周知，此过程非常重要。确实，正确的选择可以在没有失败的探索的情况下达成解决方案。但是，众所周知，选择正确的策略非常具有挑战性，因为它们的性能非常难以预测。在最近几年中，自适应枚举似乎是解决此问题的适当方法。自适应枚举使求解算法能够根据性能信息自主地修改其求解时间的策略。以这种方式，沿着搜索采用了最合适的变量和值顺序。在本文中，我们提出了一种新的，更轻量的方法来执行自适应枚举。我们采用了功能强大的分类技术，称为Top-k。为了沿着分辨率自适应地选择策略。我们报告了一组著名基准的结果，其中所提出的方法明显与经典和现代的自适应枚举方法竞争，从而满足约束条件。

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