Advertisement

Lifted Temporal Most Probable Explanation

  • Marcel GehrkeEmail author
  • Tanya Braun
  • Ralf Möller
Conference paper
  • 370 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11530)

Abstract

The lifted dynamic junction tree algorithm (LDJT) answers filtering and prediction queries efficiently for temporal probabilistic relational models by building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps. Another type of query asks for a most probable explanation (MPE) for given events. Specifically, this paper contributes (i) LDJT\(^{mpe}\) to efficiently solve the temporal MPE problem for temporal probabilistic relational models and (ii) a combination of LDJT and LDJT\(^{mpe}\) to efficiently answer assignment queries for a given number of time steps.

Keywords

Relational temporal probabilistic models Lifting MPE MAP 

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.
    Apsel, U., Brafman, R.I.: Exploiting uniform assignments in first-order MPE. In: Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, pp. 74–83. AUAI Press (2014)Google Scholar
  3. 3.
    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
  4. 4.
    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
  5. 5.
    Braun, T., Möller, R.: Lifted most probable explanation. In: Chapman, P., Endres, D., Pernelle, N. (eds.) ICCS 2018. LNCS (LNAI), vol. 10872, pp. 39–54. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-91379-7_4CrossRefGoogle Scholar
  6. 6.
    Braun, T., Möller, R.: Parameterised queries and lifted query answering. In: Proceedings of IJCAI 2018, pp. 4980–4986 (2018)Google Scholar
  7. 7.
    Dignös, A., Böhlen, M.H., Gamper, J.: Temporal alignment. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, pp. 433–444. ACM (2012)Google Scholar
  8. 8.
    Dylla, M., Miliaraki, I., Theobald, M.: A temporal-probabilistic database model for information extraction. Proc. VLDB Endow. 6(14), 1810–1821 (2013)CrossRefGoogle Scholar
  9. 9.
    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
  10. 10.
    Gehrke, M., Braun, T., Möller, R.: Preventing unnecessary groundings in the lifted dynamic junction tree algorithm. In: Mitrovic, T., Xue, B., Li, X. (eds.) AI 2018. LNCS (LNAI), vol. 11320, pp. 556–562. Springer, Cham (2018).  https://doi.org/10.1007/978-3-030-03991-2_51CrossRefGoogle Scholar
  11. 11.
    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
  12. 12.
    Lauritzen, S.L., Spiegelhalter, D.J.: Local computations with probabilities on graphical structures and their application to expert systems. J. R. Stat. Society. Ser. B (Methodol.) 50(2), 157–224 (1988)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Manfredotti, C.E.: Modeling and inference with relational dynamic Bayesian networks. Ph.D. thesis, Ph.D. dissertation, University of Milano-Bicocca (2009)Google Scholar
  14. 14.
    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
  15. 15.
    Murphy, K.P.: Dynamic Bayesian networks: representation, inference and learning. Ph.D. thesis, University of California, Berkeley (2002)Google Scholar
  16. 16.
    Nitti, D., De Laet, T., De Raedt, L.: A particle filter for hybrid relational domains. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2764–2771. IEEE (2013)Google Scholar
  17. 17.
    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
  18. 18.
    Poole, D.: First-order probabilistic inference. In: Proceedings of IJCAI, vol. 3, pp. 985–991 (2003)Google Scholar
  19. 19.
    Richardson, M., Domingos, P.: Markov logic networks. Mach. Learn. 62(1), 107–136 (2006)CrossRefGoogle Scholar
  20. 20.
    de Salvo Braz, R.: Lifted first-order probabilistic inference. Ph.D. thesis, Ph.D. dissertation, University of Illinois at Urbana Champaign (2007)Google Scholar
  21. 21.
    de Salvo Braz, R., Amir, E., Roth, D.: MPE and partial inversion in lifted probabilistic variable elimination. In: AAAI, vol. 6, pp. 1123–1130 (2006)Google Scholar
  22. 22.
    Sharma, V., Sheikh, N.A., Mittal, H., Gogate, V., Singla, P.: Lifted marginal MAP inference. In: UAI-18 Proceedings of the 34th Conference on Uncertainty in Artificial Intelligence, pp. 917–926. AUAI Press (2018)Google Scholar
  23. 23.
    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
  24. 24.
    Thon, I., Landwehr, N., De Raedt, L.: Stochastic relational processes: efficient inference and applications. Mach. Learn. 82(2), 239–272 (2011)MathSciNetCrossRefGoogle Scholar
  25. 25.
    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
  26. 26.
    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, AAAIWS’14-13, pp. 131–132. AAAI Press (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Information SystemsUniversity of LübeckLübeckGermany

Personalised recommendations