Combining Nogoods in Restart-Based Search

  • Gael Glorian
  • Frederic Boussemart
  • Jean-Marie Lagniez
  • Christophe Lecoutre
  • Bertrand Mazure
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10416)


Nogood recording is a form of learning that has been shown useful for solving constraint satisfaction problems. One simple approach involves recording nogoods that are extracted from the rightmost branches of the successive trees built by a backtrack search algorithm with restarts. In this paper, we propose several mechanisms to reason with so-called increasing-nogoods that exactly correspond to the states reached at the end of each search run. Interestingly, some similarities that can be observed between increasing-nogoods allow us to propose new original ways of dynamically combining them in order to improve the overall filtering capability of the learning system. Our preliminary results show the practical interest of our approach.


Learning Increasing nogoods Restarts Filtering 



This work has been supported by the project CPER DATA from the “Hauts-de-France” Region.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Gael Glorian
    • 1
  • Frederic Boussemart
    • 1
  • Jean-Marie Lagniez
    • 1
  • Christophe Lecoutre
    • 1
  • Bertrand Mazure
    • 1
  1. 1.CRIL, CNRSUniversity ArtoisLensFrance

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