Learning ground CP-logic theories by leveraging bayesian network learning techniques

Fundamenta Informaticae

Volumn 89, Issue 1, 2008, Pages 131-160

  Meert, Wannes ^a   Struyf, Jan ^a   Blockeel, Hendrik ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

CP logic; Statistical relational learning

Indexed keywords

BAYESIAN NETWORKS; DISTRIBUTED PARAMETER NETWORKS; EDUCATION; INFERENCE ENGINES; INFORMATION MANAGEMENT; INTELLIGENT NETWORKS; MEDICAL APPLICATIONS; PROBABILITY; SPEECH ANALYSIS;

APPLICATION DOMAINS; ART METHODS; ARTIFICIAL DOMAINS; BAYESIAN NETWORK LEARNINGS; CAUSAL RELATIONS; COMPLEX THEORIES; CP-LOGIC; DO-MAINS; EXPERIMENTAL COMPARISONS; FULLY CONNECTED NETWORKS; LEARNING TECHNIQUES; LOGIC THEORIES; PARAMETER LEARNINGS; PROBABILISTIC MODELING; REFINEMENT OPERATORS; STATISTICAL RELATIONAL LEARNING; TRAINING DATUMS;

LEARNING ALGORITHMS;

EID: 58149485308     PISSN: 01692968     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (32)

1. Beerenwinkel, N., Rahnenfuhrer, J., Daumer, M., Hoffmann, D., Kaiser, R., Selbig, J., Lengauer, T.: Learning multiple evolutionary pathways from cross-sectional data., Journal of Computational Biology, 12(6), 2005, 584-598.
2. Blocked, H., Meert, W.: Towards learning non-recursive LPADs by transforming them into Bayesian networks, Inductive Logic Programming, ILP 2006, Revised Selected Papers, LNCS 4455, Springer, 2007.
3. Breese, J., Goldman, R., Wellman, M.: Introduction to the special section on knowledge-based construction of probabilistic and decision models, IEEE Transactions on Systems, Man, and Cybernetics, 24(11), 1994, 1577-1579.
4. De Raedt, L., Kimmig, A., Toivonen, H.: ProbLog: A Probabilistic Prolog and its Application in Link Discovery, Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI07), 2007.
5. Dempster, N., Laird, A., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm, J. R. Statist. Soc. B, 39, 1977, 185-197.
6. Fierens, D., Blockeel, H., Ramon, J., Bruynooghe, M.: Logical Bayesian networks, Proceedings of the 3rd international workshop on multi-relational data mining, 2004.
7. Friedman, N.: Learning belief networks in the presence of missing values and hidden variables, Proceedings of the 14th International Conference on Machine Learning (ICML97), Morgan Kaufmann, 1997.
8. Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning Probabilistic Relational Models, Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI01), 2001.
9. Gaitanis, K.: Open Bayes for Python, http://www.openbayes.org/, 2007.
10. Getoor, L., Taskar, B., Eds.: Statistical Relational Learning, MIT Press, 2007.
11. Heckerman, D., Brees, J. S.: Causal independence for probability assessment and inference using Bayesian networks, IEEE Transactions on Systems, Man & Cybernetics, Part A (Systems & Humans), 26(6), 1994, 826-831.
12. Heckerman, D., Geiger, D., Chickering, D. M.: Learning Bayesian networks: The combination of knowledge and statistical data, Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence (UAI95), Morgan Kaufmann, 1995.
13. Jaeger, M.: Parameter learning for relational Bayesian networks, Proceedings of the 24th International Conference on Machine Learning (ICML07), ACM Press New York, NY, USA, 2007.
14. Kersting, K., De Raedt, L.: Basic Principles of Learning Bayesian Logic Programs, Probabilistic Inductive Logic Programming, 2008, 189-221.
15. Leray, P., Francois, O.: BNT Structure Learning Package: Documentation and Experiments, Technical report, Laboratoire PS1, 2004.
16. Muggleton, S.: Learning Stochastic Logic Programs, Proceedings of the AAAI2000 Workshop on Learning Statistical Models from Relational Data, 2000.
17. Muggleton, S., Buntine, W.: Machine invention of first-order predicates by inverting resolution., Proceedings of the 5th International Conference on Machine Learning (ICML88), Morgan Kaufmann, 1988.
18. Murphy, K. P.: Bayesian Network Toolbox, http://bnt.sourceforge.net/, 2007.
19. Natarajan, S., Tadepalli, P., Altendorf, E., Dietterich, T., Fern, A., Restificar, A.: Learning first-order probabilistic models with combining rules, Proceedings of the 22nd International Conference on Machine Learning (ICML05), 119, ACM Press New York, NY, USA, 2005.
20. Neapolitan, R.: Learning Bayesian Networks, Prentice Hall, 2003.
21. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, 1988.
22. Pearl, J.: Causality: Models, Reasoning, and Inference, Cambridge University Press, 2000.
23. Poole, D.: The independent choice logic for modelling multiple agents under uncertainty, Artificial Intelligence, 94(1-2), 1997, 7-56.
24. Poole, D., Zhang, N.: Exploiting contextual independence in probabilistic inference, Journal of Artificial Intelligence Research, 18, 2003, 263-313.
25. Richardson, M., Domingos, P.: Markov logic networks, Machine Learning, 62(1), 2006, 107-136.
26. Riguzzi, F.: Learning logic programs with annotated disjunctions, 14th Internation Conference on Inductive Logic Programming (ILP04), LNCS 4455, Springer, 2004.
27. Riguzzi, F.: ALLPAD: Approximate learning of logic programs with annotated disjunctions, Machine Learning, 70, 2008, 207-223.
28. Sato, T., Kameya, Y.: New Advances in Logic-Based Probabilistic Modeling by PRISM, chapter 6, APRIL, 2007.
29. Vennekens, J.: Algebraic and Logical Study of Constructive Processes in Knowledge Representation, Ph.D. Thesis, Department of Computer Science, K.U.Leuven, May 2007.
30. Vennekens, J., Denecker, M., Bruynooghe, M.: Representing causal information about a probabilistic process, Logics in Artificial Intelligence, 10th European Conference, Proceedings, LNCS 4160, Springer, 2006.
31. Vennekens, J., Verbaeten, S., Bruynooghe, M.: Logic Programs with Annotated Disjunctions, Technical report, KULeuven, 2004.
32. Vomlel, J.: Noisy-or classifier, International Journal of Intelligent Systems, 21(3), 2006, 381-398.