• DocumentCode
    2856312
  • Title

    Towards a Generic CNF Simplifier for Minimising Structured Problem Hardness

  • Author

    Anbulagan ; Slaney, John

  • Author_Institution
    NICTA, Australian Nat. Univ., Canberra, ACT, Australia
  • fYear
    2009
  • fDate
    2-4 Nov. 2009
  • Firstpage
    99
  • Lastpage
    106
  • Abstract
    CNF simplifiers play a very important role in minimising structured problem hardness. Although they can be used in an in-search process, most of them serve in a pre-search phase and rely on one form or another of resolution. Based on our understanding about problem structure, in the paper, we extend the single pre-search process to a multiple one in order to further simplify the hard structure in a problem. This extension boosts the performance of state-of-the-art clause learning and lookahead based SAT solvers when solving both satisfiable and unsatisfiable instances of many real-world hard combinatorial problems.
  • Keywords
    computability; learning (artificial intelligence); SAT solvers; conjunctive normal form; generic CNF simplifier; machine learning techniques; real-world hard combinatorial problems; satisfiability; single presearch process; state-of-the-art clause learning; structured problem hardness minimisation; Artificial intelligence; Investments; Large-scale systems; Laser sintering; Learning; Polynomials; Power generation; Random number generation; Stochastic systems; Sugar; CNF Simplifier; Real-world benchmarks; Satisfiability Problem;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence, 2009. ICTAI '09. 21st International Conference on
  • Conference_Location
    Newark, NJ
  • ISSN
    1082-3409
  • Print_ISBN
    978-1-4244-5619-2
  • Electronic_ISBN
    1082-3409
  • Type

    conf

  • DOI
    10.1109/ICTAI.2009.47
  • Filename
    5365721