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Approximate Compilation of Constraints into Multivalued Decision Diagrams

  • Tarik Hadzic
  • John N. Hooker
  • Barry O’Sullivan
  • Peter Tiedemann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5202)

Abstract

We present an incremental refinement algorithm for approximate compilation of constraint satisfaction models into multivalued decision diagrams (MDDs). The algorithm uses a vertex splitting operation that relies on the detection of equivalent paths in the MDD. Although the algorithm is quite general, it can be adapted to exploit constraint structure by specializing the equivalence tests for partial assignments to particular constraints. We show how to modify the algorithm in a principled way to obtain an approximate MDD when the exact MDD is too large for practical purposes. This is done by replacing the equivalence test with a constraint-specific measure of distance. We demonstrate the value of the approach for approximate and exact MDD compilation and evaluate its benefits in one of the main MDD application domains, interactive configuration.

Keywords

Equivalence Class Equivalence Test Binary Decision Diagram Partial Assignment Individual Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Tarik Hadzic
    • 1
  • John N. Hooker
    • 2
  • Barry O’Sullivan
    • 1
  • Peter Tiedemann
    • 3
  1. 1.Cork Constraint Computation Centre 
  2. 2.Carnegie Mellon University 
  3. 3.IT University of Copenhagen 

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