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) IDLogic 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 errorprone process. One way to ease software construction is the development of highlevel 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:
 Demoen, Bart; Garcia de la Banda, Maria. Redundant sudoku rules, Theory and Practice of Logic Programming, 2013
 Schietgat, Leander; Ramon, Jan; Bruynooghe, Maurice. A polynomialtime maximum common subgraph algorithm for outerplanar graphs and its application to chemoinformatics, Annals of Mathematics and Artificial Intelligence, 2013
 Wittocx, Johan; Denecker, Marc; Bruynooghe, Maurice. Constraint propagation for firstorder logic and inductive definitions, ACM Transactions on Computational Logic, 2013
 Berendt, Bettina. More than modelling and hiding: Towards a comprehensive view of Web mining and privacy, Data Mining and Knowledge Discovery, volume 24, issue 3, pages 697737, 2012
 Paridel, Koosha; Mantadelis, Theofrastos; Yasar, AnsarUlHaque; Preuveneers, Davy; Janssens, Gerda; Vanrompay, Yves; Berbers, Yolande. Analyzing the efficiency of contextbased grouping on collaboration in VANETs with largescale simulation, Journal of Ambient Intelligence and Humanized Computing, volume 3, pages 116, 2012
 Renkens, Joris; Van den Broeck, Guy; Nijssen, Siegfried. koptimal: A novel approximate inference algorithm for ProbLog, Machine Learning, volume 89, issue 3, pages 215231, 2012
 Gutmann, Bernd; Thon, Ingo; Kimmig, Angelika; Bruynooghe, Maurice; De Raedt, Luc. The magic of logical inference in probabilistic programming, Theory and Practice of Logic Programming, volume 11, pages 663680, 2011
 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 235262, 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 137161, 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 16601668, 2010
 Denecker, Marc; CortésCalabuig, 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 160, 2010
 Wittocx, Johan; Mariën, Maarten; Denecker, Marc. Grounding FO and FO(ID) with bounds, The Journal of Artificial Intelligence Research, volume 38, pages 223269, 2010
 Ramon, Jan; Nijssen, Siegfried. Polynomialdelay enumeration of monotonic graph classes, Journal of Machine Learning Research, volume 10, pages 907929, 2009
 Schrijvers, Tom; Stuckey, Peter; Wadler, Philip. Monadic constraint programming, Journal of Functional Programming, volume 19, issue 6, pages 663697, 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. CPlogic: A language of causal probabilistic events and its relation to logic programming, Theory and Practice of Logic Programming, volume 9, issue 3, pages 245308, 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 firstorder 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
