People
The DTAI research group contains about 50 researchers.
Are you interested in doing a PhD? Have a look at the ML jobs page, the KRR page or the Analysis page.
Research
The DTAI research group is subdivided in three subgroups:
 Machine Learning (ML) Machine learning is the subfield of artificial intelligence and computer science that studies how machines can learn. A machine learns when it improves its performance on specific tasks with experience. In order to learn, machine learning methods analyze...
 Knowledge Representation and Reasoning (KRR) ID-Logic extends classical logic with inductive definitions, yielding an intuitive and expressive knowledge representation language. The research of the KRR group focuses on this language ....
 Design, Analysis and Implementation of Declarative Programming Languages (Analysis) Programming is a labour intensive and error-prone process. One way to ease software construction is the development of high-level languages allowing a representation that is tightly related to the application's problem space. These programming languages provide a simple and clear semantics that is an excellent basis for automatic program analysis.
|
Education
 The professors of the DTAI research group are responsible for courses in the domain of artifical intelligence, machine learning, logic programming, ....
[ All courses ]
Publications
 [ All publications ]
Here are some key publications of the group:
- Kimmig, Angelika; Demoen, Bart; De Raedt, Luc; Santos Costa, Vitor; Rocha, Ricardo. On the implementation of the probabilistic logic programming language ProbLog, Theory and Practice of Logic Programming, volume 11, pages 235-262, 2011
- Schietgat, Leander; Costa, Fabrizio; Ramon, Jan; De Raedt, Luc. Effective feature construction by maximum common subgraph sampling, Machine Learning, volume 83, issue 2, pages 137-161, 2011
- Vanschoren, Joaquin; Blockeel, Hendrik; Pfahringer, Bernhard; Holmes, Geoffrey. Experiment databases. A new way to share, organize and learn from experiments, Machine Learning, 2011
- De Grave, Kurt; Costa, Fabrizio. Molecular graph augmentation with rings and functional groups, Journal of Chemical Information and Modeling, volume 50, issue 9, pages 1660-1668, 2010
- Denecker, Marc; Cortés-Calabuig, Alvaro; Bruynooghe, Maurice; Arieli, Ofer. Towards a logical reconstruction of a theory for locally closed databases, ACM Transactions on Database Systems, volume 35, issue 3, pages 1-60, 2010
- Wittocx, Johan; Mariën, Maarten; Denecker, Marc. Grounding FO and FO(ID) with bounds, The Journal of Artificial Intelligence Research, volume 38, pages 223-269, 2010
- Ramon, Jan; Nijssen, Siegfried. Polynomial-delay enumeration of monotonic graph classes, Journal of Machine Learning Research, volume 10, pages 907-929, 2009
- Schrijvers, Tom; Stuckey, Peter; Wadler, Philip. Monadic constraint programming, Journal of Functional Programming, volume 19, issue 6, pages 663-697, 2009
- Sneyers, Jon; Schrijvers, Tom; Demoen, Bart. The computational power and complexity of Constraint Handling Rules, ACM Transactions on Programming Languages and Systems, volume 31, issue 2, 42 p. pages, 2009
- Vennekens, Joost; Denecker, Marc; Bruynooghe, Maurice. CP-logic: A language of causal probabilistic events and its relation to logic programming, Theory and Practice of Logic Programming, volume 9, issue 3, pages 245-308, 2009
- Guns, Tias, Nijssen, Siegfried, De Raedt, Luc. Itemset mining: a constraint programming perspective, Artificial Intelligence, 2011.
- Guy Van den Broeck, On the completeness of first-order knowledge compilation for lifted probabilistic inference. NIPS 2011.
- Bernd Gutmann, Ingo Thon, Luc De Raedt: Learning the Parameters of Probabilistic Logic Programs from Interpretations. ECML/PKDD 2011.
- De Raedt, Luc; Kersting, Kristian; Kimmig, Angelika; Revoredo, Kate; Toivonen, Hannu, Compressing probabilistic Prolog programs, Machine learning, 2008
|
Probabilistic logic learning