Abstract
Content
Intended audience
Topic
Format
References
Links
Presenters

"Probabilistic Logic Learning" - Tutorial


References

[Cus00] J. Cussens. Parameter estimation in stochastic logic programs. Machine Learning, 44(3):245-271, 2000.
[DK03] L. De Raedt and K. Kersting. Probabilistic Logic Learning. ACM-SIGKDD Explorations: Special issue on Multi-Relational Data Mining, 5(1):31­48, 2003.
[FGKP99] N. Friedman, L. Getoor, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In T. Dean, editor, Proceedings of the Sixteenth International Joint Conferences on Artificial Intelligence (IJCAI-99), pages 1300­1309, Stockholm, Sweden, 1999. Morgan Kaufmann.
[Get01] L. Getoor. Learning Statistical Models from Relational Data. PhD thesis, Stanford University, 2001.
[Hal89] J. Y. Halpern. An analysis of first-order logics of probability. Artificial Intelli- gence, 46:311­350, 1989.
[Jae97] M. Jaeger. Relational Bayesian networks. In D. Geiger and P. P. Shenoy, edi- tors, Proceedings of the Thirteenth Annual Conference on Uncertainty in Artifi- cial Intelligence (UAI-97), pages 266­273, Providence, Rhode Island, USA, 1997. Morgan Kaufmann.
[KD01a] K. Kersting and L. De Raedt. Adaptive Bayesian Logic Programs. In C. Rou- veirol and M. Sebag, editors, Proceedings of the Eleventh Conference on Inductive Logic Programming (ILP-01), volume 2157 of LNCS, Strasbourg, France, 2001. Springer.
[KD01b] K. Kersting and L. De Raedt. Towards Combining Inductive Logic Programming and Bayesian Networks. In C. Rouveirol and M. Sebag, editors, Proceedings of the Eleventh Conference on Inductive Logic Programming (ILP-01), volume 2157 of LNCS, Strasbourg, France, 2001. Springer.
[KRKD03] K. Kersting, T. Raiko, S. Kramer, and L. De Raedt. Towards discovering struc- tural signatures of protein folds based on logical hidden markov models. In R. B. Altman, A. K. Dunker, L. Hunter, T. A. Jung, and T. E. Klein, editors, Proceed- ings of the Pacific Symposium on Biocomputing, pages 192 ­ 203, Kauai, Hawaii, USA, 2003. World Scientific.
[Mug96] S. Muggleton. Stochastic logic programs. In L. De Raedt, editor, Advances in Inductive Logic Programming. IOS Press, 1996.
[NH97] L. Ngo and P. Haddawy. Answering queries from context-sensitive probabilistic knowledge bases. Theoretical Computer Science, 171:147­177, 1997.
[Pfe00] A. J. Pfeffer. Probabilistic Reasoning for Complex Systems. PhD thesis, Stanford University, 2000.
[Poo93] D. Poole. Probabilistic Horn abduction and Bayesian networks. Artificial Intelli- gence, 64:81­129, 1993.
[Sat95] T. Sato. A Statistical Learning Method for Logic Programs with Distribution Semantics. In L. Sterling, editor, Proceedings of the Twelfth International Con- ference on Logic Programming (ICLP-1995), pages 715 ­ 729, Tokyo, Japan, 1995. MIT Press.
[SDW03] S. Sanghai, P. Domingos, and D. Weld. Dynamic probabilistic relational mod- els. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI-03), Acapulco, Mexico, 2003.
[SK01] T. Sato and Y. Kameya. Parameter learning of logic programs for symbolic- statistical modeling. Journal of Artificial Intelligence Research, 15:391­454, 2001