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Generative Modeling by PRISM

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Book cover Logic Programming (ICLP 2009)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 5649))

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Abstract

PRISM is a probabilistic extension of Prolog. It is a high level language for probabilistic modeling capable of learning statistical parameters from observed data. After reviewing it from various viewpoints, we examine some technical details related to logic programming, including semantics, search and program synthesis.

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References

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

    Article  MATH  Google Scholar 

  2. 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 

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

    Google Scholar 

  4. Poole, D.: The independent choice logic for modeling multiple agents under uncertainty. Artificial Intelligence 94(1-2), 7–56 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  5. Sato, T., Kameya, Y.: Parameter learning of logic programs for symbolic-statistical modeling. Journal of Artificial Intelligence Research 15, 391–454 (2001)

    MathSciNet  MATH  Google Scholar 

  6. Kersting, K., De Raedt, L.: Basic principles of learning bayesian logic programs. Technical Report Technical Report No. 174, Institute for Computer Science, University of Freiburg (2002)

    Google Scholar 

  7. Blockeel, H.: Prolog for Bayesian networks: a meta-interpreter approach. In: Proceedings of the 2nd International Workshop on Multi-Relational Data Mining (MRDM 2003), pp. 1–13 (2003)

    Google Scholar 

  8. 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)

    Chapter  Google Scholar 

  9. 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, vol. 3625, pp. 121–135. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  10. De Raedt, L., Kimmig, A., Toivonen, H.: ProbLog: A probabilistic Prolog and its application in link discoverry. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), pp. 2468–2473 (2007)

    Google Scholar 

  11. Baral, C., Gelfond, M., Rushton, N.: Probabilistic reasoning with answer sets. Theory and Practice of Logic Programming (TPLP) 9(1), 57–144 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  12. De Raedt, L., Kersting, K.: Probabilistic inductive logic programming. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S. (eds.) Probabilistic Inductive Logic Programming - Theory and Applications. LNCS, pp. 1–27. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  13. Breese, J.S.: Construction of belief and decision networks. Computational Intelligence 8(4), 624–647 (1992)

    Article  Google Scholar 

  14. Koller, D., Pfeffer, A.: Learning probabilities for noisy first-order rules. In: Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI 1997), pp. 1316–1321 (1997)

    Google Scholar 

  15. Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI 1999), pp. 1300–1309 (1999)

    Google Scholar 

  16. Pfeffer, A.: IBAL: A probabilistic rational programming language. In: Proceedings of the 17th International Conference on Artificial Intelligence (IJCAI 2001), pp. 733–740 (2001)

    Google Scholar 

  17. Jaeger, J.: Complex probabilistic modeling with recursive relational Bayesian networks. Annals of Mathematics and Artificial Intelligence 32(1-4), 179–220 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  18. Getoor, L., Friedman, N., Koller, D., Taskar, B.: Learning Probabilistic Models of Relational Structure. Journal of Machine Learning Research 3, 679–707 (2002)

    MATH  Google Scholar 

  19. Costa, V., Page, D., Qazi, M., Cussens, J.: CLP(BN): Constraint logic programming for probabilistic knowledge. In: Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence (UAI 2003), pp. 517–524 (2003)

    Google Scholar 

  20. Milch, B., Marthi, B., Russell, S., Sontag, D., Ong, D., Kolobov, A.: BLOG: Probabilistic models with unknown objects. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI 2005), pp. 1352–1359 (2005)

    Google Scholar 

  21. Laskey, K.: MEBN: A logic for open-world probabilistic reasoning. C4I Center Technical Report C4I06-01, George Mason University Department of Systems Engineering and Operations Research (2006)

    Google Scholar 

  22. Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62, 107–136 (2006)

    Article  Google Scholar 

  23. Getoor, L., Grant, J.: PRL: A probabilistic relational language. Journal of Machine Learning 62(1-2), 7–31 (2006)

    Article  Google Scholar 

  24. Getoor, L., Taskar, B. (eds.): Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)

    MATH  Google Scholar 

  25. Sato, T., Kameya, Y.: Statistical abduction with tabulation. In: Kakas, A., Sadri, F. (eds.) Computational Logic: Logic Programming and Beyond. LNCS (LNAI), vol. 2408, pp. 567–587. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  26. Zhou, N.F., Sato, T., Shen, Y.D.: Linear tabling strategies and optimization. Theory and Practice of Logic Programming 8(1), 81–109 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  27. Sato, T.: First Order Compiler: A deterministic logic program synthesis algorithm. Journal of Symbolic Computation 8, 605–627 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  28. 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)

    Chapter  Google Scholar 

  29. Milch, B., Marthi, B., Sontag, D., Russell, S., Ong, D., Kolobov, A.: Approximate Inference for Infinite Contingent Bayesian Networks. In: Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics (AISTATS 2005), pp. 1352–1359 (2005)

    Google Scholar 

  30. Domingos, P., Singla, P.: Markov logic in infinite domains. In: De Raedt, L., Dietterich, T., Getoor, L., Kersting, K., Muggleton, S. (eds.) Probabilistic, Logical and Relational Learning - A Further Synthesis. Dagstuhl Seminar Proceedings, vol. 07161 (2008)

    Google Scholar 

  31. Stolcke, A.: An efficient probabilistic context-free parsing algorithm that computes prefix probabilities. Computational Linguistics 21(2), 165–201 (1995)

    MathSciNet  Google Scholar 

  32. Gelfond, M., Lifshcitz, V.: The stable model semantics for logic programming, pp. 1070–1080 (1988)

    Google Scholar 

  33. Van Gelder, A., Ross, K., Schlipf, J.: The well-founded semantics for general logic programs. The journal of ACM (JACM) 38(3), 620–650 (1991)

    MathSciNet  MATH  Google Scholar 

  34. Tamaki, H., Sato, T.: OLD resolution with tabulation. In: Shapiro, E. (ed.) ICLP 1986. LNCS, vol. 225, pp. 84–98. Springer, Heidelberg (1986)

    Chapter  Google Scholar 

  35. Sagonas, K., Swift, T., Warren, D.: XSB as an efficient deductive database engine. In: Proceedings of the 1994 ACM SIGMOD International Conference on Management of Data, pp. 442–453 (1994)

    Google Scholar 

  36. Ramakrishnan, I., Rao, P., Sagonas, K., Swift, T., Warren, D.: Efficient tabling mechanisms for logic programs. In: Proceedings of the 12th International Conference on Logic Programming (ICLP 1995), pp. 687–711. The MIT Press, Cambridge (1995)

    Google Scholar 

  37. Guo, H.F., Gupta, G.: A simple scheme for implementing tabled logic programming systems based on dynamic reordering of alternatives. In: Codognet, P. (ed.) ICLP 2001. LNCS, vol. 2237, pp. 181–196. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  38. Sagonas, K., Stuckey, J.: Just enough tabling. In: Proceedings of the 6th ACM SIGPLAN international conference on Principles and practice of declarative programming (PPDP 2004), pp. 78–89. ACM, New York (2004)

    Google Scholar 

  39. Rabiner, L.R., Juang, B.: Foundations of Speech Recognition. Prentice-Hall, Englewood Cliffs (1993)

    MATH  Google Scholar 

  40. Baker, J.K.: Trainable grammars for speech recognition. In: Proceedings of Spring Conference of the Acoustical Society of America, pp. 547–550 (1979)

    Google Scholar 

  41. Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Francisco (1988)

    MATH  Google Scholar 

  42. 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 

  43. Sato, T., Kameya, Y., Kurihara, K.: Variational bayes via propositionalized probability computation in prism. Annals of Mathematics and Artificial Intelligence (to appear)

    Google Scholar 

  44. Cussens, J.: Parameter estimation in stochastic logic programs. Machine Learning 44(3), 245–271 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  45. Sato, T., Kameya, Y.: PRISM: a language for symbolic-statistical modeling. In: Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI 1997), pp. 1330–1335 (1997)

    Google Scholar 

  46. 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 

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Author information

Authors and Affiliations

  1. Tokyo Institute of Technology, Ookayama Meguro, Tokyo, Japan

    Taisuke Sato

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  1. Taisuke Sato
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Editor information

Editors and Affiliations

  1. School of Computing, University of Leeds, LS2 9JT, Leeds, UK

    Patricia M. Hill

  2. Deptartment of Computer Science, Stony Brook University, 11794-4400, Stony Brook, NY, USA

    David S. Warren

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© 2009 Springer-Verlag Berlin Heidelberg

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Sato, T. (2009). Generative Modeling by PRISM. In: Hill, P.M., Warren, D.S. (eds) Logic Programming. ICLP 2009. Lecture Notes in Computer Science, vol 5649. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02846-5_4

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  • DOI: https://doi.org/10.1007/978-3-642-02846-5_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02845-8

  • Online ISBN: 978-3-642-02846-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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