By the same authors

Automatic Detection of At-Most-One and Exactly-One Relations for Improved SAT Encodings of Pseudo-Boolean Constraints

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  • Carlos Ansótegui
  • Miquel Bofill
  • Jordi Coll
  • Nguyen Dang
  • Juan Luis Esteban
  • Ian James Miguel
  • Peter Nightingale
  • András Z. Salamon
  • Josep Suy
  • Mateu Villaret


Publication details

Title of host publicationProceedings of the 25th International Conference on Principles and Practice of Constraint Programming
DateAccepted/In press - 1 Jul 2019
DatePublished (current) - 30 Sep 2019
Number of pages17
Original languageEnglish


Pseudo-Boolean (PB) constraints often have a critical role in constraint satisfaction and optimisation problems. Encoding PB constraints to SAT has proven to be an efficient approach in many applications, however care must be taken to encode them compactly and with good propagation properties. It has been shown that at-most-one (AMO) and exactly-one (EO) relations over subsets of the variables can be exploited in various encodings of PB constraints, improving their compactness and solving performance. In this paper we detect AMO and EO relations completely automatically and exploit them to improve SAT encodings that are based on Multi-Valued Decision Diagrams (MDDs). Our experiments show substantial reductions in encoding size and dramatic improvements in solving time thanks to automatic AMO and EO detection.

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