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Identifying and Exploiting Problem Structures Using Explanation-based Constraint Programming

机译：使用基于解释的约束编程识别和利用问题结构

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摘要

Identifying structures in a given combinatorial problem is often a key step for designing efficient search heuristics or for understanding the inherent complexity of the problem. Several Operations Research approaches apply decomposition or relaxation strategies upon such a structure identified within a given problem. The next step is to design algorithms that adaptively integrate that kind of information during search. We claim in this paper, inspired by previous work on impact-based search strategies for constraint programming, that using an explanation-based constraint solver may lead to collect invaluable information on the intimate dynamically revealed and static structures of a problem instance. Moreover, we discuss how dedicated OR solving strategies (such as Benders decomposition) could be adapted to constraint programming when specific relationships between variables are exhibited.
机译：识别给定组合问题中的结构通常是设计有效搜索试探法或理解问题固有复杂性的关键步骤。几种运筹学方法将分解或松弛策略应用于在给定问题内确定的这种结构。下一步是设计在搜索过程中自适应整合此类信息的算法。我们认为，在先前基于影响力的约束编程搜索策略研究工作的启发下，本文认为使用基于解释的约束求解器可能会收集有关问题实例的动态动态显示和静态结构的宝贵信息。此外，我们讨论了当变量之间存在特定关系时，如何将专用的OR解决策略（例如Benders分解）调整为约束编程。

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