论文标题

CSP通过代数产品的复杂性分类转移

Complexity Classification Transfer for CSPs via Algebraic Products

论文作者

Bodirsky, Manuel, Jonsson, Peter, Martin, Barnaby, Mottet, Antoine, Semanišinová, Žaneta

论文摘要

我们研究了无限域约束满意度问题的复杂性:我们的基本设置是,可以将结构$ \ mathfrak的一阶扩展的CSP进行复杂性分类。我们利用与各个多态性克隆的产物相对应的结构(代数产品)的产物,并对CSP进行了完全的复杂性分类,用于$ n $ fold代数的一阶扩展,$(\ mathbb {q}}; <)$。各种代数和逻辑方法证明了这一点,以及了解$(\ Mathbb {q}; <)$的可拖动的一阶扩展的多态性以及对语法中表达关系的明确描述,以句法关系的一阶限制一阶公式。通过将我们的分类结果与一般分类转移技术相结合,我们为高度相关的形式主义(例如艾伦的间隔代数,$ n $ dimensientional块代数和基本方向计算,即使允许较高的方向关系)获得了令人惊讶的强大新分类结果。我们的结果证实了对于难以通过较旧方法进行分析的结构类别的无限域障碍猜想。对于具有二元签名的结构的特殊情况,可以实质上加强结果并与Ord-Horn公式密切相关。这解决了AI文献中的几个长期开放问题。

We study the complexity of infinite-domain constraint satisfaction problems: our basic setting is that a complexity classification for the CSPs of first-order expansions of a structure $\mathfrak A$ can be transferred to a classification of the CSPs of first-order expansions of another structure $\mathfrak B$. We exploit a product of structures (the algebraic product) that corresponds to the product of the respective polymorphism clones and present a complete complexity classification of the CSPs for first-order expansions of the $n$-fold algebraic power of $(\mathbb{Q};<)$. This is proved by various algebraic and logical methods in combination with knowledge of the polymorphisms of the tractable first-order expansions of $(\mathbb{Q};<)$ and explicit descriptions of the expressible relations in terms of syntactically restricted first-order formulas. By combining our classification result with general classification transfer techniques, we obtain surprisingly strong new classification results for highly relevant formalisms such as Allen's Interval Algebra, the $n$-dimensional Block Algebra, and the Cardinal Direction Calculus, even if higher-arity relations are allowed. Our results confirm the infinite-domain tractability conjecture for classes of structures that have been difficult to analyse with older methods. For the special case of structures with binary signatures, the results can be substantially strengthened and tightly connected to Ord-Horn formulas; this solves several longstanding open problems from the AI literature.

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