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Combining Nogoods in Restart-Based Search

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10416))

Abstract

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.

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References

  1. Boussemart, F., Hemery, F., Lecoutre, C., Sais, L.: Boosting systematic search by weighting constraints. In: Proceedings of 16th European Conference on Artificial Intelligence, ECAI 2004, Including Prestigious Applicants of Intelligent Systems, PAIS 2004, Valencia, Spain, 22–27 August 2004, pp. 146–150. IOS Press (2004)

    Google Scholar 

  2. Dechter, R.: Enhancement schemes for constraint processing: backjumping, learning, and cutset decomposition. Artif. Intell. 41(3), 273–312 (1990)

    Article  Google Scholar 

  3. Feydy, T., Stuckey, P.J.: Lazy clause generation reengineered. In: Gent, I.P. (ed.) CP 2009. LNCS, vol. 5732, pp. 352–366. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04244-7_29

    Chapter  Google Scholar 

  4. Frost, D., Dechter, R.: Dead-end driven learning. In: Proceedings of 12th National Conference on Artificial Intelligence, Seattle, WA, USA, 31 July - 4 August 1994, vol. 1, pp. 294–300. AAAI Press/The MIT Press (1994)

    Google Scholar 

  5. Ginsberg, M.L.: Dynamic backtracking. J. Artif. Intell. Res. (JAIR) 1, 25–46 (1993)

    MATH  Google Scholar 

  6. Grégoire, É., Lagniez, J.-M., Mazure, B.: A CSP solver focusing on FAC variables. In: Lee, J. (ed.) CP 2011. LNCS, vol. 6876, pp. 493–507. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23786-7_38

    Chapter  Google Scholar 

  7. Jussien, N., Debruyne, R., Boizumault, P.: Maintaining arc-consistency within dynamic backtracking. In: Dechter, R. (ed.) CP 2000. LNCS, vol. 1894, pp. 249–261. Springer, Heidelberg (2000). doi:10.1007/3-540-45349-0_19

    Chapter  Google Scholar 

  8. Katsirelos, G., Bacchus, F.: Unrestricted nogood recording in CSP search. In: Rossi, F. (ed.) CP 2003. LNCS, vol. 2833, pp. 873–877. Springer, Heidelberg (2003). doi:10.1007/978-3-540-45193-8_70

    Chapter  Google Scholar 

  9. Lecoutre, C., Sais, L., Tabary, S., Vidal, V.: Nogood recording from restarts. In: IJCAI 2007, Proceedings of 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, 6–12 January 2007, pp. 131–136 (2007)

    Google Scholar 

  10. Lecoutre, C., Sais, L., Tabary, S., Vidal, V.: Recording and minimizing nogoods from restarts. J. Satisf. Boolean Model. Comput. (JSAT) 1(3–4), 147–167 (2007)

    MATH  Google Scholar 

  11. Lecoutre, C., Tabary, S.: Symmetry-reinforced nogood recording from restarts. In: 11th International Workshop on Symmetry in Constraint Satisfaction Problems (SymCon 2011), Perugia, Italy, pp. 13–27 (2011)

    Google Scholar 

  12. Lee, J.H.M., Schulte, C., Zhu, Z.: Increasing nogoods in restart-based search. In: Proceedings of 30th AAAI Conference on Artificial Intelligence, 12–17 February 2016, Phoenix, Arizona, USA, pp. 3426–3433. AAAI Press (2016)

    Google Scholar 

  13. Lee, J.H.M., Zhu, Z.: An increasing-nogoods global constraint for symmetry breaking during search. In: O’Sullivan, B. (ed.) CP 2014. LNCS, vol. 8656, pp. 465–480. Springer, Cham (2014). doi:10.1007/978-3-319-10428-7_35

    Google Scholar 

  14. Schiex, T., Verfaillie, G.: Nogood recording for static and dynamic constraint satisfaction problems. Int. J. Artif. Intell. Tools 3(2), 187–208 (1994)

    Article  Google Scholar 

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Acknowledgement

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

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Correspondence to Gael Glorian .

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Glorian, G., Boussemart, F., Lagniez, JM., Lecoutre, C., Mazure, B. (2017). Combining Nogoods in Restart-Based Search. In: Beck, J. (eds) Principles and Practice of Constraint Programming. CP 2017. Lecture Notes in Computer Science(), vol 10416. Springer, Cham. https://doi.org/10.1007/978-3-319-66158-2_9

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  • DOI: https://doi.org/10.1007/978-3-319-66158-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66157-5

  • Online ISBN: 978-3-319-66158-2

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