The art of Prolog: advanced programming techniques
The art of Prolog: advanced programming techniques
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Machine Learning - special issue on inductive logic programming
Answering queries from context-sensitive probabilistic knowledge bases
Selected papers from the international workshop on Uncertainty in databases and deductive systems
Foundations of Logic Programming
Foundations of Logic Programming
Parameter Estimation in Stochastic Logic Programs
Machine Learning
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Bayesian Logic Programs
Learning probabilities for noisy first-order rules
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Learning probabilistic relational models
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Adaptive Bayesian Logic Programs
ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Statistical Relational Learning for Document Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Generalized Ordering-Search for Learning Directed Probabilistic Logical Models
Inductive Logic Programming
Learning Directed Probabilistic Logical Models: Ordering-Search Versus Structure-Search
ECML '07 Proceedings of the 18th European conference on Machine Learning
Integrating Logical Reasoning and Probabilistic Chain Graphs
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Boosting learning and inference in Markov logic through metaheuristics
Applied Intelligence
Predicate Logic Based Image Grammars for Complex Pattern Recognition
International Journal of Computer Vision
Abductive plan recognition by extending Bayesian logic programs
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
CLP(BN): constraint logic programming for probabilistic knowledge
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Statistical relational learning: an inductive logic programming perspective
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
PFORTE: revising probabilistic FOL theories
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
Statistical relational learning: an inductive logic programming perspective
ECML'05 Proceedings of the 16th European conference on Machine Learning
Logical bayesian networks and their relation to other probabilistic logical models
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Probabilistic first-order theory revision from examples
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Bayesian abductive logic programs: a probabilistic logic for abductive reasoning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Variational bayes inference for logic-based probabilistic models on BDDs
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
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Recently, new representation languages that integrate first order logic with Bayesian networks have been developed. Bayesian logic programs are one of these languages. In this paper, we present results on combining Inductive Logic Programming (ILP) with Bayesian networks to learn both the qualitative and the quantitative components of Bayesian logic programs. More precisely, we show how to combine the ILP setting learning from interpretations with score-based techniques for learning Bayesian networks. Thus, the paper positively answers Koller and Pfeffer's question, whether techniques from ILP could help to learn the logical component of first order probabilistic models.