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Bart Bogaerts
Bart Bogaerts
Associate Professor, AI Lab, Department of Computer Science, Vrije Universiteit Brussel
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Citirano
Citirano
Godina
Predicate logic as a modeling language: the IDP system
B De Cat, B Bogaerts, M Bruynooghe, G Janssens, M Denecker
Declarative Logic Programming: Theory, Systems, and Applications, 279-323, 2018
1112018
Improved static symmetry breaking for SAT
J Devriendt, B Bogaerts, M Bruynooghe, M Denecker
Theory and Applications of Satisfiability Testing–SAT 2016: 19th …, 2016
742016
Predicate logic as a modeling language: modeling and solving some machine learning and data mining problems with IDP3
M Bruynooghe, H Blockeel, B Bogaerts, B De Cat, S De Pooter, J Jansen, ...
Theory and Practice of Logic Programming 15 (6), 783-817, 2015
552015
Model expansion in the presence of function symbols using constraint programming
B De Cat, B Bogaerts, J Devriendt, M Denecker
2013 IEEE 25th International Conference on Tools with Artificial …, 2013
452013
Grounded fixpoints and their applications in knowledge representation
B Bogaerts, J Vennekens, M Denecker
Artificial Intelligence 224, 51-71, 2015
382015
Symmetric explanation learning: Effective dynamic symmetry handling for SAT
J Devriendt, B Bogaerts, M Bruynooghe
Theory and Applications of Satisfiability Testing–SAT 2017: 20th …, 2017
362017
Symmetry propagation: Improved dynamic symmetry breaking in SAT
J Devriendt, B Bogaerts, B De Cat, M Denecker, C Mears
2012 IEEE 24th International Conference on Tools with Artificial …, 2012
342012
Step-wise explanations of constraint satisfaction problems
B Bogaerts, E Gamba, J Claes, T Guns
ECAI 2020-24th European Conference on Artificial Intelligence, 29 August-8 …, 2020
262020
Solving QBF instances with nested SAT solvers
B Bogaerts, T Janhunen, S Tasharrofi
Workshops at the Thirtieth AAAI Conference on Artificial Intelligence, 2016
262016
A framework for step-wise explaining how to solve constraint satisfaction problems
B Bogaerts, E Gamba, T Guns
Artificial Intelligence 300, 103550, 2021
252021
Certified symmetry and dominance breaking for combinatorial optimisation
B Bogaerts, S Gocht, C McCreesh, J Nordström
Proceedings of the AAAI Conference on Artificial Intelligence 36 (4), 3698-3707, 2022
242022
Exploiting justifications for lazy grounding of answer set programs
B Bogaerts, A Weinzierl
IJCAI International Joint Conference on Artificial Intelligence 2018, 1737-1745, 2018
222018
Fixpoint semantics for active integrity constraints
B Bogaerts, L Cruz-Filipe
Artificial Intelligence 255, 43-70, 2018
202018
Stable-unstable semantics: Beyond NP with normal logic programs
B Bogaerts, T Janhunen, S Tasharrofi
Theory and Practice of Logic Programming 16 (5-6), 570-586, 2016
192016
Safe inductions and their applications in knowledge representation
B Bogaerts, J Vennekens, M Denecker
Artificial Intelligence 259, 167-185, 2018
17*2018
Weighted abstract dialectical frameworks through the lens of approximation fixpoint theory
B Bogaerts
Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 2686-2693, 2019
162019
BreakIDGlucose: On the importance of row symmetry in SAT
J Devriendt, B Bogaerts, M Bruynooghe
Proceedings 4th International Workshop on the Cross-Fertilization Between …, 2014
16*2014
Simulating dynamic systems using linear time calculus theories
B Bogaerts, J Jansen, M Bruynooghe, B De Cat, J Vennekens, ...
Theory and Practice of Logic Programming 14 (4-5), 477-492, 2014
14*2014
Certified core-guided MaxSAT solving
J Berg, B Bogaerts, J Nordström, A Oertel, D Vandesande
International Conference on Automated Deduction, 1-22, 2023
132023
QMaxSATpb: A certified MaxSAT solver
D Vandesande, W De Wulf, B Bogaerts
International Conference on Logic Programming and Nonmonotonic Reasoning …, 2022
132022
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