Solving set constraint satisfaction problems using ROBDDS
AuthorHawkins, P; Lagoon, V; Stuckey, PJ
Source TitleJOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
PublisherAI ACCESS FOUNDATION
AffiliationComputer Science and Software Engineering
Document TypeJournal Article
CitationsHawkins, P., Lagoon, V. & Stuckey, P. J. (2005). Solving set constraint satisfaction problems using ROBDDS. JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 24, pp.109-156. https://doi.org/10.1613/jair.1638.
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<jats:p>In this paper we present a new approach to modeling finite set domain constraint problems using Reduced Ordered Binary Decision Diagrams (ROBDDs). We show that it is possible to construct an efficient set domain propagator which compactly represents many set domains and set constraints using ROBDDs. We demonstrate that the ROBDD-based approach provides unprecedented flexibility in modeling constraint satisfaction problems, leading to performance improvements. We also show that the ROBDD-based modeling approach can be extended to the modeling of integer and multiset constraint problems in a straightforward manner. Since domain propagation is not always practical, we also show how to incorporate less strict consistency notions into the ROBDD framework, such as set bounds, cardinality bounds and lexicographic bounds consistency. Finally, we present experimental results that demonstrate the ROBDD-based solver performs better than various more conventional constraint solvers on several standard set constraint problems.</jats:p>
KeywordsArtificial Intelligence and Image Processing
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