Automatic detection of at-most-one and exactly-one relations for improved SAT encodings of pseudo-boolean constraints

Autor: Carlos Ansótegui, Juan Luis Esteban, Nguyen Dang, Josep Suy, Jordi Coll, Ian Miguel, András Z. Salamon, Mateu Villaret, Peter Nightingale, Miquel Bofill
Přispěvatelé: Universitat Politècnica de Catalunya. Departament de Ciències de la Computació
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Recercat. Dipósit de la Recerca de Catalunya
instname
Lecture Notes in Computer Science ISBN: 9783030300470
CP
Popis: 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.
Databáze: OpenAIRE