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

Author:

Amilhastre Jérôme1,Fargier Hélène2,Niveau Alexandre3,Pralet Cédric4

Affiliation:

1. Cameleon Software, 185 rue Galilée, F-31670 Labège, France

2. IRIT–CNRS, Université Paul Sabatier, F-31062 Toulouse Cedex 9, France

3. CRIL–CNRS, Université d'Artois, F-62307 Lens Cedex, France

4. ONERA — The French Aerospace Lab, F-31055, Toulouse, France

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.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Artificial Intelligence

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A Decision Diagram Operation for Reachability;Formal Methods;2023

2. A compositional approach to probabilistic knowledge compilation;International Journal of Approximate Reasoning;2021-11

3. MDDs Boost Equation Solving on Discrete Dynamical Systems;Integration of Constraint Programming, Artificial Intelligence, and Operations Research;2021

4. Extending Compact-Diagram to Basic Smart Multi-Valued Variable Diagrams;Integration of Constraint Programming, Artificial Intelligence, and Operations Research;2019

5. Defining and Evaluating Heuristics for the Compilation of Constraint Networks;Lecture Notes in Computer Science;2017

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