Machine Learning

, Volume 70, Issue 2–3, pp 207–223 | Cite as

ALLPAD: approximate learning of logic programs with annotated disjunctions

Article

Abstract

Logic Programs with Annotated Disjunctions (LPADs) provide a simple and elegant framework for representing probabilistic knowledge in logic programming. In this paper we consider the problem of learning ground LPADs starting from a set of interpretations annotated with their probability. We present the system ALLPAD for solving this problem. ALLPAD modifies the previous system LLPAD in order to tackle real world learning problems more effectively. This is achieved by looking for an approximate solution rather than a perfect one. A number of experiments have been performed on real and artificial data for evaluating ALLPAD, showing the feasibility of the approach.

Keywords

Inductive logic programming Probabilistic logic programming Statistical relational learning Logic programs with annotated disjunctions 

References

  1. Blockeel, H. (2003). Prolog for first-order Bayesian networks: a meta-interpreter approach. In Proceedings of the second workshop on multi-relational data mining. Google Scholar
  2. Blockeel, H. (2004). Probabilistic logical models for Mendel’s experiments: an exercise. In Proceedings of the fourteenth international conference on inductive logic programming, work in progress track. Google Scholar
  3. Cussens, J. (2000). Stochastic logic programs: sampling, inference and applications. In Proceedings of the sixteenth conference on uncertainty in artificial intelligence (pp. 115–122). San Francisco: Kaufmann. Google Scholar
  4. Dehaspe, L., & Raedt, L. D. (1996). DLAB: a declarative language bias formalism. In Z. W. Ras & M. Michalewicz (Eds.), Proceedings of the ninth international symposium on foundations of intelligent systems (pp. 613–622). Berlin: Springer. Google Scholar
  5. Fierens, D., Blockeel, H., Ramon, J., & Bruynooghe, M. (2004). Logical Bayesian networks. In Proceedings of the third workshop on multi-relational data mining (pp. 19–30). Google Scholar
  6. Getoor, L., Friedman, N., Koller, D., & Pfeffer, A. (2001). Learning probabilistic relational models. In S. Dzeroski & N. Lavrac (Eds.), Relational data mining. Berlin: Springer. Google Scholar
  7. Greenberg, S. (1988). Using Unix: collected traces of 168 users (Tech. Rep. No. Research Report 88/333/45). Department of Computer Science, University of Calgary. Google Scholar
  8. Gutmann, B., & Kersting, K. (2006). TildeCRF: conditional random fields for logical sequences. In Proceedings of the seventeenth European conference on machine learning. Berlin: Springer. Google Scholar
  9. Jacobs, N., & Blockeel, H. (2003). User modeling with sequential data. In Proceedings of the tenth international conference on human–computer interaction (pp. 557–561). Mahwah: Lawrence Erlbaum Associates. Google Scholar
  10. Kersting, K., & Gärtner, T. (2004). Fisher kernels for logical sequences. In Proceedings of the fifteenth European conference on machine learning (pp. 205–216). Berlin: Springer. Google Scholar
  11. Kersting, K., & Raedt, L. D. (2000). Bayesian logic programs. In The tenth international conference on inductive logic programming, work in progress track. Available from http://SunSITE.Informatik.RWTH-Aachen.DE/Publications/CEUR-WS/Vol-35/.
  12. Kersting, K., & Raedt, L. D. (2001a). Bayesian logic programs (Tech. rep. No. 151). Freiburg, Germany: Institute for Computer Science, University of Freiburg. Google Scholar
  13. Kersting, K., & Raedt, L.D., (2001b). Towards combining inductive logic programming and Bayesian networks. In C. Rouveirol & M. Sebag (Eds.), Proceedings of the eleventh international conference on inductive logic programming. Berlin: Springer. Google Scholar
  14. Kersting, K., & Raiko, T. (2005). ‘Say EM’ for selecting probabilistic models for logical sequences. In Proceedings of the twenty first conference on uncertainty in artificial intelligence. Arlington: AUAI Press. Google Scholar
  15. Kersting, K., Raedt, L. D., & Raiko, T. (2006). Logical hidden Markov models. Journal of Artificial Intelligence Research, 25, 425–456. MathSciNetGoogle Scholar
  16. Kersting, K., Raiko, T., Kramer, S., & Raedt, L. D. (2003). Towards discovering structural signatures of protein folds based on logical hidden Markov models. In Proceedings of the pacific symposium on biocomputing (pp. 192–203). Singapore: World Scientific Press. Google Scholar
  17. Muggleton, S. H. (2000). Learning stochastic logic programs. Electronic Transactions in Artificial Intelligence, 4 (041). Available from http://www.ida.liu.se/ext/epa/cis/2000/041/tcover.html.
  18. Poole, D. (1997). The independent choice logic for modelling multiple agents under uncertainty. Artificial Intelligence, 94(1–2), 7–56. MATHCrossRefMathSciNetGoogle Scholar
  19. Riguzzi, F. (2004). Learning logic programs with annotated disjunctions. In Proceedings of the fourteenth international conference on inductive logic programming (pp. 270–287). Berlin: Springer. Google Scholar
  20. Riguzzi, F. (2006). ALLPAD: approximate learning of logic programs with annotated disjunctions (Tech. Rep. No. CS-2006-01). University of Ferrara. Available from http://www.ing.unife.it/aree_ricerca/informazione/cs/technical_reports/CS-2006-01.pdf.
  21. Santos Costa, V., Page, D., Qazi, M., & Cussens, J. (2003). CLP( \(\mathcal{BN}\) ): constraint logic programming for probabilistic knowledge. In Proceedings of the nineteenth conference on uncertainty in artificial intelligence. San Francisco: Kaufmann. Google Scholar
  22. Sato, T. (1995). A statistical learning method for logic programs with distribution semantics. In Proceedings of the twelfth international conference on logic programming (pp. 715–729). Cambridge: MIT Press. Google Scholar
  23. Sato, T., & Kameya, Y. (2001). Parameter learning of logic programs for symbolic-statistical modeling. Journal of Artificial Intelligence Research, 15, 391–454. MATHMathSciNetGoogle Scholar
  24. Stolle, C., Karwath, A., & Raedt, L. D. (2005). Cassic’cl: an integrated ILP system. In Proceedings of the eighth international conference on discovery science. Berlin: Springer. Google Scholar
  25. Turcotte, M., Muggleton, S., & Sternberg, M. J. E. (2001). The effect of relational background knowledge on learning of protein three-dimensional fold signatures. Machine Learning, 43(1/2), 81–95. MATHCrossRefGoogle Scholar
  26. Van Gelder, A., Ross, K. A., & Schlipf, J. S. (1991). The well-founded semantics for general logic programs. Journal of the ACM, 38(3), 620–650. MATHCrossRefGoogle Scholar
  27. Vennekens, J., & Verbaeten, S. (2003). Logic programs with annotated disjunctions (Tech. Rep. No CW386). K.U. Leuven. Available from http://www.cs.kuleuven.ac.be/~joost/techrep.ps.
  28. Vennekens, J., Verbaeten, S., & Bruynooghe, M., (2004). Logic programs with annotated disjunctions. In Proceedings of the twentieth international conference on logic programming. Berlin: Springer. Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Dipartimento di IngegneriaUniversità di FerraraFerraraItaly

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