Enhancement Schemes for Constraint Processing: Backjumping, Learning, and Cutset Decomposition

  title={Enhancement Schemes for Constraint Processing: Backjumping, Learning, and Cutset Decomposition},
  author={R. Dechter},
  journal={Artif. Intell.},
  • R. Dechter
  • Published 1990
  • Mathematics, Computer Science
  • Artif. Intell.
  • Abstract Researchers in the areas of constraint satisfaction problems, logic programming, and truth maintenance systems have suggested various schemes for enhancing the performance of the backtracking algorithm. This paper defines and compares the performance of three such schemes: “backjumping,” “learning,” and “cycle-cutset.” The backjumping and the cycle-cutset methods work best when the constraint graph is sparse, while the learning scheme mostly benefits problem instances with dense… CONTINUE READING
    624 Citations
    An Optimal Backtrack Algorithm for Tree-Structured Constraint Satisfaction problems
    • 22
    Domain Filtering can Degrade Intelligent Backtracking Search
    • 74
    • PDF
    A Graph Based Backtracking Algorithm for Solving General CSPs
    • 7
    • PDF
    Constraint Propagation Based Scheduling of Job Shops
    • 25
    A generic bounded backtracking framework for solving CSPs
    • 1
    • PDF
    Dynamic variable ordering in graph based backjumping algorithms for csps
    • D. Gupta
    • Computer Science, Mathematics
    • Int. J. Comput. Math.
    • 2000
    Resolution versus Search: Two Strategies for SAT
    • 130
    • PDF
    Experimental Evaluation of Preprocessing Algorithms for Constraint Satisfaction Problems
    • 113


    Network-Based Heuristics for Constraint-Satisfaction Problems
    • 515
    Consistency in Networks of Relations
    • 2,864
    • PDF
    Increasing Tree Search Efficiency for Constraint Satisfaction Problems
    • 1,443
    • PDF
    A Sufficient Condition for Backtrack-Free Search
    • 809
    • PDF
    Intelligent Backtracking in Plan-Based Deduction
    • 30
    Search Rearrangement Backtracking and Polynomial Average Time
    • P. Purdom
    • Mathematics, Computer Science
    • Artif. Intell.
    • 1983
    • 200
    An Assumption-Based TMS
    • J. D. Kleer
    • Mathematics, Computer Science
    • Artif. Intell.
    • 1986
    • 1,857
    • PDF