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International Conference on Inductive Logic Programming

ILP 2006: Inductive Logic Programming pp 10–24Cite as

First-Order Probabilistic Languages: Into the Unknown

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4455))

Abstract

This paper surveys first-order probabilistic languages (FOPLs), which combine the expressive power of first-order logic with a probabilistic treatment of uncertainty. We provide a taxonomy that helps make sense of the profusion of FOPLs that have been proposed over the past fifteen years. We also emphasize the importance of representing uncertainty not just about the attributes and relations of a fixed set of objects, but also about what objects exist. This leads us to Bayesian logic, or BLOG, a language for defining probabilistic models with unknown objects. We give a brief overview of BLOG syntax and semantics, and emphasize some of the design decisions that distinguish it from other languages. Finally, we consider the challenge of constructing FOPL models automatically from data.

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References

  1. Elidan, G., Friedman, N.: Learning hidden variable networks: The information bottleneck approach. JMLR 6, 81–127 (2005)

    MathSciNet  Google Scholar 

  2. Fierens, D., Blockeel, H., Bruynooghe, M., Ramon, J.: Logical Bayesian networks and their relation to other probabilistic logical models. In: Kramer, S., Pfahringer, B. (eds.) ILP 2005. LNCS (LNAI), vol. 3625, Springer, Heidelberg (2005)

    Google Scholar 

  3. Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: Proc. 16th IJCAI, pp. 1300–1307 (1999)

    Google Scholar 

  4. Getoor, L., Friedman, N., Koller, D., Taskar, B.: Learning probabilistic models of relational structure. In: Proc. 18th ICML, pp. 170–177 (2001)

    Google Scholar 

  5. Halpern, J.Y.: An analysis of first-order logics of probability. Artificial Intelligence 46, 311–350 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  6. Heckerman, D., Meek, C., Koller, D.: Probabilistic models for relational data. Technical Report MSR-TR-2004-30, Microsoft Research (2004)

    Google Scholar 

  7. Jaeger, M.: Relational Bayesian networks. In: Proc. 13th UAI, pp. 266–273 (1997)

    Google Scholar 

  8. Kersting, K., Raedt, L.D.: Adaptive Bayesian logic programs. In: Rouveirol, C., Sebag, M. (eds.) ILP 2001. LNCS (LNAI), vol. 2157, Springer, Heidelberg (2001)

    Google Scholar 

  9. Kok, S., Domingos, P.: Learning the structure of Markov logic networks. In: Proc. 22nd ICML, pp. 441–448 (2005)

    Google Scholar 

  10. Koller, D., Pfeffer, A.: Probabilistic frame-based systems. In: Proc. 15th AAAI, pp. 580–587 (1998)

    Google Scholar 

  11. Laskey, K.B., da Costa, P.C.G.: Of starships and Klingons: First-order Bayesian logic for the 23rd century. In: Proc. 21st UAI, pp. 346–353 (2005)

    Google Scholar 

  12. Lukasiewicz, T., Kern–Isberner, G.: Probabilistic logic programming under maximum entropy. In: Hunter, A., Parsons, S. (eds.) ECSQARU 1999. LNCS (LNAI), vol. 1638, pp. 279–292. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  13. Milch, B., Marthi, B., Russell, S., Sontag, D., Ong, D.L., Kolobov, A.: BLOG: Probabilistic models with unknown objects. In: Proc. 19th IJCAI (2005)

    Google Scholar 

  14. Milch, B., Marthi, B., Sontag, D., Russell, S., Ong, D.L., Kolobov, A.: Approximate inference for infinite contingent Bayesian networks. In: Proc. 10th AISTATS (2005)

    Google Scholar 

  15. Milch, B., Russell, S.: General-purpose MCMC inference over relational structures. In: Proc. 22nd UAI, pp. 349–358 (2006)

    Google Scholar 

  16. Muggleton, S., Buntine, W.: Machine invention of first-order predicates by inverting resolution. In: Proc. 5th ICML, pp. 339–352 (1988)

    Google Scholar 

  17. Muggleton, S.H.: Stochastic logic programs. In: De Raedt, L. (ed.) Advances in Inductive Logic Programming, pp. 254–264. IOS Press, Amsterdam (1996)

    Google Scholar 

  18. Muggleton, S.H.: Learning structure and parameters of stochastic logic programs. Electronic Trans. on AI 6 (2002)

    Google Scholar 

  19. J.,, Neville, D.J.: Dependency networks for relational data. In: Proc. 4th IEEE Int’l Conf. on Data Mining, IEEE Computer Society Press, Los Alamitos (2004)

    Google Scholar 

  20. Neville, J., Jensen, D., Friedland, L., Hay, M.: Learning relational probability trees. In: Proc. 9th KDD (2003)

    Google Scholar 

  21. Ng, R.T., Subrahmanian, V.S.: Probabilistic logic programming. Information and Computation 101(2), 150–201 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  22. Ngo, L., Haddawy, P.: Answering queries from context-sensitive probabilistic knowledge bases. Theoretical Comp. Sci. 171(1–2), 147–177 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  23. Otero, R., Muggleton, S.: On McCarthy’s appearance and reality problem. In: ILP 2006: Short Papers (2006)

    Google Scholar 

  24. Pasula, H., Marthi, B., Milch, B., Russell, S., Shpitser, I.: Identity uncertainty and citation matching. In: NIPS 15, MIT Press, Cambridge, MA (2003)

    Google Scholar 

  25. Perlich, C., Provost, F.: Aggregation-based feature invention and relational concept classes. In: Proc. 9th KDD (2003)

    Google Scholar 

  26. Pfeffer, A.: IBAL: A probabilistic rational programming language. In: Proc. 17th IJCAI (2001)

    Google Scholar 

  27. Poole, D.: Probabilistic Horn abduction and Bayesian networks. Artificial Intelligence 64(1), 81–129 (1993)

    Article  MATH  Google Scholar 

  28. Poole, D.: The Independent Choice Logic for modelling multiple agents under uncertainty. Artificial Intelligence 94(1–2), 5–56 (1997)

    MathSciNet  Google Scholar 

  29. Popescul, A., Ungar, L.H.: Cluster-based concept invention for statistical relational learning. In: Proc. 10th KDD (2004)

    Google Scholar 

  30. Popescul, A., Ungar, L.H., Lawrence, S., Pennock, D.M.: Statistical relational learning for document mining. In: Proc. 3rd IEEE Int’l Conf. on Data Mining, pp. 275–282. IEEE Computer Society Press, Los Alamitos (2003)

    Google Scholar 

  31. Puech, A., Muggleton, S.: A comparison of stochastic logic programs and Bayesian logic programs. In: IJCAI Workshop on Learning Statistical Models from Relational Data (2003)

    Google Scholar 

  32. Revoredo, V.K., Paes, A., Zaverucha, G., Costa, S.: Combining predicate invention and revision of probabilistic FOL theories. In: ILP 2006: Short Papers (2006)

    Google Scholar 

  33. Richardson, M., Domingos, P.: Markov logic networks. MLJ 62, 107–136 (2006)

    Google Scholar 

  34. Sato, T., Kameya, Y.: PRISM: A symbolic–statistical modeling language. In: Proc. 15th IJCAI, pp. 1330–1335 (1997)

    Google Scholar 

  35. Sato, T., Kameya, Y.: Parameter learning of logic programs for symbolic–statistical modeling. JAIR 15, 391–454 (2001)

    MATH  MathSciNet  Google Scholar 

  36. Taskar, B., Abbeel, P., Koller, D.: Discriminative probabilistic models for relational data. In: Proc. 18th UAI, pp. 485–492 (2002)

    Google Scholar 

  37. Thomas, A., Spiegelhalter, D., Gilks, W.: BUGS: A program to perform Bayesian inference using Gibbs sampling. In: Bernardo, J., Berger, J., Dawid, A., Smith, A. (eds.) Bayesian Statistics 4, Oxford Univ. Press, Oxford (1992)

    Google Scholar 

  38. Van Assche, A., Vens, C., Blockeel, H., Džeroski, S.: First order random forests: Learning relational classifiers with complex aggregates. MLJ 64, 149–182 (2006)

    MATH  Google Scholar 

  39. Vennekens, J., Verbaeten, S., Bruynooghe, M.: Logic programs with annotated disjunctions. In: Demoen, B., Lifschitz, V. (eds.) ICLP 2004. LNCS, vol. 3132, pp. 431–445. Springer, Heidelberg (2004)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Science and AI Laboratory, Massachusetts Institute of Technology, 32 Vassar St. Room 32-G480, Cambridge, MA 02139, USA

    Brian Milch

  2. Computer Science Division, University of California at Berkeley, Berkeley, CA 94720-1776, USA

    Stuart Russell

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  1. Brian Milch
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  2. Stuart Russell
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Stephen Muggleton Ramon Otero Alireza Tamaddoni-Nezhad

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Milch, B., Russell, S. (2007). First-Order Probabilistic Languages: Into the Unknown. In: Muggleton, S., Otero, R., Tamaddoni-Nezhad, A. (eds) Inductive Logic Programming. ILP 2006. Lecture Notes in Computer Science(), vol 4455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73847-3_3

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  • DOI: https://doi.org/10.1007/978-3-540-73847-3_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73846-6

  • Online ISBN: 978-3-540-73847-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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