The art of Prolog: advanced programming techniques
The art of Prolog: advanced programming techniques
Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
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
The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
The justification of logical theories based on data compression
Machine intelligence 13
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
Logical settings for concept-learning
Artificial Intelligence
Machine Learning - Special issue on learning with probabilistic representations
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
ACM SIGKDD Explorations Newsletter
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Parameter Estimation in Stochastic Logic Programs
Machine Learning
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Parameter Learning in Object-Oriented Bayesian Networks
Annals of Mathematics and Artificial Intelligence
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Learning Probabilistic Relational Models
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Structural Learning in Object Oriented Domains
Proceedings of the Fourteenth International Florida Artificial Intelligence Research Society Conference
Lookahead and Discretization in ILP
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Bayesian Logic Programs
Learning probabilities for noisy first-order rules
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Parameter learning of logic programs for symbolic-statistical modeling
Journal of Artificial Intelligence Research
Probabilistic classification and clustering in relational data
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Critical remarks on single link search in learning belief networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Fisher kernels for relational data
ECML'06 Proceedings of the 17th European conference on Machine Learning
Parameter Learning in Probabilistic Databases: A Least Squares Approach
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Learning Ground CP-Logic Theories by Leveraging Bayesian Network Learning Techniques
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
ProbLog Technology for Inference in a Probabilistic First Order Logic
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
On the implementation of the probabilistic logic programming language problog
Theory and Practice of Logic Programming
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
On the combination of logical and probabilistic models for information analysis
Applied Intelligence
Learning Ground CP-Logic Theories by Leveraging Bayesian Network Learning Techniques
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Implicit learning of common sense for reasoning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Bayesian logic programs tightly integrate definite logic programs with Bayesian networks in order to incorporate the notions of objects and relations into Bayesian networks. They establish a one-to-one mapping between ground atoms and random variables, and between the immediate consequence operator and the directly influenced by relation. In doing so, they nicely separate the qualitative (i.e. logical) component from the quantitative (i.e. the probabilistic) one providing a natural framework to describe general, probabilistic dependencies among sets of random variables. In this chapter, we present results on combining Inductive Logic Programming with Bayesian networks to learn both the qualitative and the quantitative components of Bayesian logic programs from data. More precisely, we show how the qualitative components can be learned by combining the inductive logic programming setting learning from interpretations with score-based techniques for learning Bayesian networks. The estimation of the quantitative components is reduced to the corresponding problem of (dynamic) Bayesian networks.