Advertisement

Towards Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm

  • Marcel Gehrke
  • Tanya Braun
  • Ralf Möller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11117)

Abstract

The lifted dynamic junction tree algorithm (LDJT) answers filtering and prediction queries efficiently for probabilistic relational temporal models by building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps. Unfortunately, a non-ideal elimination order can lead to unnecessary groundings.

References

  1. 1.
    Ahmadi, B., Kersting, K., Mladenov, M., Natarajan, S.: Exploiting symmetries for scaling loopy belief propagation and relational training. Mach. Learn. 92(1), 91–132 (2013)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Braun, T., Möller, R.: Lifted junction tree algorithm. In: Friedrich, G., Helmert, M., Wotawa, F. (eds.) KI 2016. LNCS (LNAI), vol. 9904, pp. 30–42. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46073-4_3CrossRefGoogle Scholar
  3. 3.
    Braun, T., Möller, R.: Preventing groundings and handling evidence in the lifted junction tree algorithm. In: Kern-Isberner, G., Fürnkranz, J., Thimm, M. (eds.) KI 2017. LNCS (LNAI), vol. 10505, pp. 85–98. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67190-1_7CrossRefGoogle Scholar
  4. 4.
    Braun, T., Möller, R.: Counting and conjunctive queries in the lifted junction tree algorithm. In: Croitoru, M., Marquis, P., Rudolph, S., Stapleton, G. (eds.) GKR 2017. LNCS (LNAI), vol. 10775, pp. 54–72. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-78102-0_3CrossRefGoogle Scholar
  5. 5.
    Gehrke, M., Braun, T., Möller, R.: Lifted dynamic junction tree algorithm. In: Chapman, P., Endres, D., Pernelle, N. (eds.) ICCS 2018. LNCS (LNAI), vol. 10872, pp. 55–69. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-91379-7_5CrossRefGoogle Scholar
  6. 6.
    Geier, T., Biundo, S.: Approximate online inference for dynamic Markov logic networks. In: Proceedings of the 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp. 764–768. IEEE (2011)Google Scholar
  7. 7.
    Lauritzen, S.L., Spiegelhalter, D.J.: Local computations with probabilities on graphical structures and their application to expert systems. J. R. Stat. Soc. Ser. B (Methodol.) 50, 157–224 (1988)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Milch, B., Zettlemoyer, L.S., Kersting, K., Haimes, M., Kaelbling, L.P.: Lifted probabilistic inference with counting formulas. In: Proceedings of AAAI, vol. 8, pp. 1062–1068 (2008)Google Scholar
  9. 9.
    Murphy, K., Weiss, Y.: The factored frontier algorithm for approximate inference in DBNs. In: Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, pp. 378–385. Morgan Kaufmann Publishers Inc. (2001)Google Scholar
  10. 10.
    Murphy, K.P.: Dynamic Bayesian networks: representation, inference and learning. Ph.D. thesis, University of California, Berkeley (2002)Google Scholar
  11. 11.
    Papai, T., Kautz, H., Stefankovic, D.: Slice normalized dynamic Markov logic networks. In: Proceedings of the Advances in Neural Information Processing Systems, pp. 1907–1915 (2012)Google Scholar
  12. 12.
    Poole, D.: First-order probabilistic inference. In: Proceedings of IJCAI, vol. 3, pp. 985–991 (2003)Google Scholar
  13. 13.
    Richardson, M., Domingos, P.: Markov logic networks. Mach. Learn. 62(1), 107–136 (2006)CrossRefGoogle Scholar
  14. 14.
    de Salvo Braz, R.: Lifted first-order probabilistic inference. Ph.D. thesis, Ph.D. dissertation, University of Illinois at Urbana Champaign (2007)Google Scholar
  15. 15.
    Taghipour, N., Fierens, D., Davis, J., Blockeel, H.: Lifted variable elimination: decoupling the operators from the constraint language. J. Artif. Intell. Res. 47(1), 393–439 (2013)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Thon, I., Landwehr, N., De Raedt, L.: Stochastic relational processes: efficient inference and applications. Mach. Learn. 82(2), 239–272 (2011)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Vlasselaer, J., Van den Broeck, G., Kimmig, A., Meert, W., De Raedt, L.: TP-compilation for inference in probabilistic logic programs. Int. J. Approx. Reason. 78, 15–32 (2016)CrossRefGoogle Scholar
  18. 18.
    Vlasselaer, J., Meert, W., Van den Broeck, G., De Raedt, L.: Efficient probabilistic inference for dynamic relational models. In: Proceedings of the 13th AAAI Conference on Statistical Relational AI, pp. 131–132. AAAIWS’14-13, AAAI Press (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Institute of Information SystemsUniversity of LübeckLübeckGermany

Personalised recommendations