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Compiling CSPs: A Complexity Map of (Non-Deterministic) Multivalued Decision Diagrams

机译:编译CSP :(不确定性)多值决策图的复杂度图

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摘要

Constraint Satisfaction Problems (CSPs) offer a powerful framework for representing a great variety of problems. The difficulty is that most of the requests associated with CSPs are NP-hard. When these requests have to be addressed online, Multivalued Decision Diagrams (MDDs) have been proposed as a way to compile CSPs. In the present paper, we draw a compilation map of MDDs, in the spirit of the NNF compilation map, analyzing MDDs according to their succinctness and to their tractable transformations and queries. Deterministic ordered MDDs are a generalization of ordered binary decision diagrams to non-Boolean domains: unsurprisingly, they have similar capabilities. More interestingly, our study puts forward the interest of non-deterministic ordered MDDs: when restricted to Boolean domains, they capture OBDDs and DNFs as proper subsets and have performances close to those of DNNFs. The comparison to classical, deterministic MDDs shows that relaxing the determinism requirement leads to an increase in succinctness and allows more transformations to be satisfied in polynomial time (typically, the disjunctive ones). Experiments on random problems confirm the gain in succinctness.
机译:约束满足问题(CSP)提供了一个强大的框架来表示各种各样的问题。困难在于与CSP相关的大多数请求都是NP困难的。当必须在线解决这些请求时,已经提出了多值决策图(MDD)作为编译CSP的方法。在本文中,我们本着NNF编译图的精神绘制了MDD的编译图,根据MDD的简洁性以及易于处理的转换和查询来分析MDD。确定性有序MDD是有序二进制决策图到非布尔域的概括:毫无疑问,它们具有相似的功能。更有趣的是,我们的研究提出了不确定性有序MDD的兴趣:当限制为布尔域时,它们将OBDD和DNF捕获为适当的子集,并且性能接近DNNF。与经典确定性MDD的比较表明,放宽确定性要求会导致简洁性的提高,并允许在多项式时间内满足更多的转换(通常是析取式)。关于随机问题的实验证实了简洁性的提高。

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