发表机构
Université Catholique de Louvain; Università di Bologna（鲁汶天主教大学; 博洛尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究为能量推理传播器构建预言器的可行性，通过机器学习获得预言函数合并相关工作，使其灵活易嵌入求解器，实验表明能获高预测准确率，还给出分类特征建议及构建预言器需关注的重要问题。
AI 中文摘要
约束编程的主要优势之一是能够通过传播来减少搜索空间。然而，传播是一把双刃剑，更多的剪枝能力是以更长的计算时间为代价的。对于每个问题约束，最佳传播器取决于特定实例，并且可能在搜索时发生变化。在文献中，机器学习（ML）技术和基于活动的启发式方法已分别用于（静态地）为一批问题选择传播器以及（动态地）调整传播强度。我们建议通过使用经由ML获得的预言函数来合并这些工作，以决定是否对目标约束运行复杂传播器。多种设计选择使该方法灵活且易于嵌入到最先进的求解器中。在本文中，我们专注于研究为能量推理传播器构建预言器的可行性。我们的实验表明可以获得高预测准确率，为分类特征提供建议，并突出构建此类预言器时要解决的重要问题。
英文摘要
One of the main strengths of Constraint Programming is the ability to reduce the search space via propagation. However, propagation is a double-edged sword, with more pruning power coming at the price of larger computation time. For each problem constraint, the best propagator depends on the specific instance and may change at search time. In the literature, Machine Learning (ML) techniques and activity-based heuristics have been applied respectively for choosing (statically) the propagators for a batch of problems and to adapt (dynamically) the propagation strength. We propose to merge those efforts by using an oracle function, obtained via ML, to decide whether to run complex propagators for a target constraint. A combination of design choices makes the approach flexible and easy to embed in state-of-the-art solvers. In this paper, we focus on investigating the feasibility of building an oracle for the Energetic Reasoning propagator. Our experiments show that high prediction accuracy can be obtained, provide suggestions for classification features, and highlight important issues to address when building such an oracle.
