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Journal Articles International Journal on Artificial Intelligence Tools Year : 2014

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

Abstract

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.
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Dates and versions

hal-01303816 , version 1 (18-04-2016)

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Jerome Amilhastre, Hélène Fargier, Alexandre Niveau, Cédric Pralet. Compiling CSPs: A Complexity Map of (Non-Deterministic) Multivalued Decision Diagrams. International Journal on Artificial Intelligence Tools, 2014, 23 (4), pp.1460015. ⟨10.1142/S021821301460015X⟩. ⟨hal-01303816⟩
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