Welcome to the DTAI research group
Introduction
The activities of DTAI (Declaratieve Talen en Artificiele Intelligentie = Declarative Languages and Artificial Intelligence) are centred around research and education in programming languages and artificial intelligence. Main themes of study are in the fields of declarative languages, machine learning, and knowledge representation.
DTAI started in the mid-seventies, closely following the invention of logic programming and became one of the world's leading centres for research in logic programming. Gradually, the scope of its research broadened, including functional programming and more artificial intelligence oriented topics in knowledge representation and machine learning. The use of logic is a common thread to almost all activities.
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.
Projects
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Probabilistic logic learning is a newly emerging subfield of artificial intelligence lying at the intersection of knowledge representation, reasoning about uncertainty and machine learning. The GOA project aims to realise breakthroughs in probabilistic logic learning in several ways ....
Education
The professors of the DTAI research group are responsible for courses in the domain of artifical intelligence, machine learning, logic programming, ....
People
- The DTAI research group contains about 50 researchers.
Publications
- De Raedt, Luc; Kersting, Kristian; Kimmig, Angelika; Revoredo, Kate; Toivonen, Hannu, Compressing probabilistic Prolog programs, Machine learning, volume 70, issue 2-3, pages 151-168, 2008
- Kersting, Kristian; De Raedt, Luc, Bayesian Logic Programming: Theory and Tool, an Introduction to Statistical Relational Learning, pages 291-322, 2007
- Blockeel, Hendrik; Dehaspe, Luc; Demoen, Bart; Janssens, Gerda; Ramon, Jan; Vandecasteele, Henk, Improving the efficiency of inductive logic programming through the use of query packs, Journal of artificial intelligence research, volume 16, pages 135-166, 2002
- Ramon, Jan; Bruynooghe, Maurice, A polynomial time computable metric between point sets, Acta informatica, volume 37, issue 10, pages 765-780, 2001
- Dzeroski, S; De Raedt, Luc; Driessens, Kurt, Relational reinforcement learning, Machine learning, volume 43, issue 1-2, pages 7-52, 2001
- Kosala, Raymondus; Blockeel, Hendrik, Web mining research : A survey, SIGKDD Explorations - Newsletter of the ACM Special Interest Group on Knowledge Discovery and Data M, volume 2, issue 1, pages 1-15, 2000
- Denecker, Marc, Extending classical logic with inductive definitions, Computational logic - cl 2000, volume 1861, pages 703-717, 2000
- Dehaspe, Luc; Toivonen, H, Discovery of frequent DATALOG patterns, Data mining and knowledge discovery, volume 3, issue 1, pages 7-36, 1999
- Blockeel, Hendrik; De Raedt, Luc, Top-down induction of first-order logical decision trees, Artificial intelligence, volume 101, issue 1-2, pages 285-297, 1998
- Blockeel, Hendrik; De Raedt, Luc; Ramon, Jan, Top-down induction of clustering trees, Proceedings of the 15th International Conference on Machine Learning, pages 55-63, 1998
- Muggleton, Stephen; De Raedt, Luc, Inductive logic programming: theory and methods, Journal of logic programming, volume 19+20, pages 629-679, 1994
