IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v240y2016i1d10.1007_s10479-015-2028-9.html
   My bibliography  Save this article

What we can learn from conflicts in propositional satisfiability

Author

Listed:
  • Youssef Hamadi

    (Microsoft Research
    LIX École Polytechnique)

  • Saïd Jabbour

    (CRIL-CNRS, Université Lille Nord de France - Artois)

  • Lakhdar Saïs

    (CRIL-CNRS, Université Lille Nord de France - Artois)

Abstract

Learning is a general concept, playing an important role in many Artificial intelligence domains. In this paper, we address the learning paradigm used to explain failures or conflicts encountered during search. This explanation, derived by conflict analysis, and generally expressed as a new constraint, is usually used to dynamically avoid future occurrences of similar situations. Before focusing on clause learning in Boolean satisfiability (SAT), we first overview some important works on this powerful reasoning tool in other domains such as constraint satisfaction and truth maintenance systems. Then, we present a comprehensive survey of the most important works having led to what is called today—conflict driven clause learning—which is one of the key components of modern SAT solvers. We also overview some of the original extensions or variants of clauses learning. In theory, current SAT solvers with clause learning are as powerful as general resolution proof systems. In practice, real-world SAT instances with millions of variables and clauses are now in the scope of this solving paradigm.

Suggested Citation

  • Youssef Hamadi & Saïd Jabbour & Lakhdar Saïs, 2016. "What we can learn from conflicts in propositional satisfiability," Annals of Operations Research, Springer, vol. 240(1), pages 13-37, May.
    • Handle: RePEc:spr:annopr:v:240:y:2016:i:1:d:10.1007_s10479-015-2028-9
    DOI: 10.1007/s10479-015-2028-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-015-2028-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-015-2028-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. John N. Hooker, 1989. "Input Proofs and Rank One Cutting Planes," INFORMS Journal on Computing, INFORMS, vol. 1(3), pages 137-145, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. John N. Hooker, 2002. "Logic, Optimization, and Constraint Programming," INFORMS Journal on Computing, INFORMS, vol. 14(4), pages 295-321, November.
    2. S. Bollapragada & O. Ghattas & J. N. Hooker, 2001. "Optimal Design of Truss Structures by Logic-Based Branch and Cut," Operations Research, INFORMS, vol. 49(1), pages 42-51, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:annopr:v:240:y:2016:i:1:d:10.1007_s10479-015-2028-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.