Efficient algorithms for combinatorial search
ID
MSCA-2020-BBogaerts04
Supervisors
Project description
Research towards efficient algorithms for combinatorial search, in domains such as answer set programming, Boolean satisfiability, Quantified Boolean Formulas, Constraint Programming, Pseudo-Boolean solving and Mixed Integer Programming is greatly valued in my lab. The research can involve efficient general purpose algorithms, pre-processing techniques (of particular interest here is symmetry detection and the combination with group algebra, as well as different methods for symmetry exploitation), in-processing techniques, and translations between formalisms.
Not just the satisfiability problem itself, but also related problems such as finding unsatisfiable subsets (that are optimal with respect to some criterion) are of interest.
Also of particular interest is research towards combining techniques from different fields and hybrid systems (e.g., lazy clause generation), as well as how to exploit high-level representations (such as minizinc models, first-order logic theories, and first-order answer set programs) and the benefits that can be obtained by starting from this representation rather than a lower-level (e.g. propositional) encoding.
About the research Group
Artificial Intelligence Lab
The Artificial Intelligence Lab, founded in 1983 by Prof. Dr. Luc Steels, is the first AI lab on the European mainland. It is headed by Prof. Dr. Ann Nowé and Prof. Dr. Bernard Manderick. During its history of more than three decades, the VUB AI Lab has been following two main routes towards the understanding of intelligence: both the symbolic route (classical AI) as the dynamics route (complex systems science).
Since its foundation, more than 50 people have received a PhD degree at the Artificial Intelligence Lab, of which 18 in the past 5 years only. The total amount of publications sums up to more than 850 publications which are cited more than 24.000 times, 8500 times in the last 5 years. The lab also has experience in setting up of spin-offs (5 since its beginning). The research group is provided with standard hardware and software utilities and departmental services (secretary and ICT support). The COMO group of the AI-lab focuses on the one hand on the modeling of natural phenomena, and on the other hand on developing algorithms for complex problem solving inspired by these natural phenomena. The lab has experience in a wide range of learning techniques such as: Reinforcement Learning, Genetic Algorithms, Neural Networks, Support Vector Machines, Graphical models including Bayesian Networks, Genetic algorithms etc.