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An Increasing-Nogoods Global Constraint for Symmetry Breaking During Search

机译:搜索期间对称性破坏的Nogoods全局约束

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Symmetry Breaking During Search (SBDS) adds conditional symmetry breaking constraints (which are nogoods) dynamically upon backtracking to avoid exploring symmetrically equivalents of visited search space. The constraint store is proliferated with numerous such individual nogoods which are weak in constraint propagation. We introduce the notion of increasing nogoods, and give a global constraint of a sequence of increasing nogoods, incNGs. Reasoning globally with increasing nogoods allows extra prunings. We prove formally that nogoods accumulated for a given symmetry at a search node in SBDS and its variants are increasing. Thus we can maintain only one increasing-nogoods global constraint for each given symmetry during search. We give a polynomial time filtering algorithm for incNGs and also an efficient incremental counterpart which is stronger than GAC on each individual nogood. We demonstrate with extensive experimentation how incNGs can increase propagation and speed up search against SBDS, its variants, SBDD and carefully tailored static methods.
机译:搜索期间对称破坏(SBDS)在回溯时动态添加条件对称破坏约束(不好),以避免探索访问的搜索空间的对称等价物。约束存储中充斥着数量众多的约束传播较弱的单个杂物。我们介绍了增加杂物的概念,并给出了一系列增加杂物incNG的全局约束。在全球范围内增加品味的推理可以进行额外的修剪。我们正式证明,在SBDS中,对于给定的对称性,在搜索节点处积累的杂物及其变体正在增加。因此,对于搜索过程中的每个给定对称性,我们只能维护一个递增的nogoods全局约束。我们为incNG提供了多项式时间过滤算法,并且提供了一种有效的增量式对等算法,在每个个体上,它的性能均优于GAC。我们通过广泛的实验演示了incNG如何提高传播速度并加快针对SBDS,其变体,SBDD和精心定制的静态方法的搜索。

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