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    Solving set constraint satisfaction problems using ROBDDS

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    Author
    Hawkins, P; Lagoon, V; Stuckey, PJ
    Date
    2005-01-01
    Source Title
    JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
    Publisher
    AI ACCESS FOUNDATION
    University of Melbourne Author/s
    LAGOON, VITALY; Stuckey, Peter; HAWKINS, PETER JAMES
    Affiliation
    Computer Science and Software Engineering
    Metadata
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    Document Type
    Journal Article
    Citations
    Hawkins, 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.
    Access Status
    This item is currently not available from this repository
    URI
    http://hdl.handle.net/11343/29298
    DOI
    10.1613/jair.1638
    Abstract
    <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>
    Keywords
    Artificial Intelligence and Image Processing

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