Abstract

After briefly mentioning the historical background of PLL/ SRL, we examine PRISM, a logic-based modeling language, as an instance of PLL/SRL research. We first look at the distribution semantics, PRISM’s semantics, which defines a probability measure on a set of possible Herbrand models. We then mention characteristic features of PRISM as a tool for probabilistic modeling.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    De Raedt, L., Kersting, K.: Probabilistic inductive logic programming. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S. (eds.) Probabilistic Inductive Logic Programming. LNCS, vol. 4911, pp. 1–27. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Getoor, L., Taskar, B. (eds.): Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)MATHGoogle Scholar
  3. 3.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Francisco (1988)MATHGoogle Scholar
  4. 4.
    Sato, T.: A statistical learning method for logic programs with distribution semantics. In: Proceedings of the 12th International Conference on Logic Programming (ICLP 1995), pp. 715–729 (1995)Google Scholar
  5. 5.
    Sato, T., Kameya, Y.: Parameter learning of logic programs for symbolic-statistical modeling. Journal of Artificial Intelligence Research 15, 391–454 (2001)MathSciNetMATHGoogle Scholar
  6. 6.
    Sato, T., Kameya, Y.: New Advances in Logid-Based Probabilistic Modeling by PRISM. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S. (eds.) Probabilistic Inductive Logic Programming. LNCS (LNAI), vol. 4911, pp. 118–155. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    Lloyd, J.W.: Foundations of Logic Programming. Springer, Heidelberg (1984)CrossRefMATHGoogle Scholar
  8. 8.
    Fenstad, J.E.: Representation of probabilities defined on first order languages. In: Crossley, J.N. (ed.) Sets, Models and Recursion Theory, pp. 156–172. North-Holland, Amsterdam (1967)CrossRefGoogle Scholar
  9. 9.
    Sato, T., Kameya, Y.: Statistical abduction with tabulation. In: Kakas, A.C., Sadri, F. (eds.) Computational Logic: Logic Programming and Beyond. LNCS (LNAI), vol. 2408, pp. 567–587. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  10. 10.
    Sato, T., Kameya, Y., Zhou, N.F.: Generative modeling with failure in PRISM. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI 2005), pp. 847–852 (2005)Google Scholar
  11. 11.
    Wetherell, C.S.: Probabilistic languages: a review and some open questions. Computing Surveys 12(4), 361–379 (1980)MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Manning, C.D., Schütze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)MATHGoogle Scholar
  13. 13.
    Sato, T.: Inside-Outside probability computation for belief propagation. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), pp. 2605–2610 (2007)Google Scholar
  14. 14.
    Zhou, N.F., Sato, T., Shen, Y.D.: Linear tabling strategies and optimization. Theory and Practice of Logic Programming 8(1), 81–109 (2008)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Sato, T., Kameya, Y., Kurihara, K.: Variational bayes via propositionalized probability computation in prism. Annals of Mathematics and Artificial Intelligence (to appear)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Taisuke Sato
    • 1
  1. 1.Tokyo Institute of Technology, Ookayama MeguroTokyoJapan

Personalised recommendations