A5056109180- Constraint Satisfaction and Optimization
- Data Management and Algorithms
- Data Mining Algorithms and Applications
- Formal Methods in Verification
- Rough Sets and Fuzzy Logic
- Advanced Database Systems and Queries
- Bayesian Modeling and Causal Inference
- Cellular Automata and Applications
- Neural Networks and Reservoir Computing
- AI-based Problem Solving and Planning
- Advanced Memory and Neural Computing
- Topic Modeling
- Innovative Teaching and Learning Methods
- Information Technology and Learning
- Natural Language Processing Techniques
- Model-Driven Software Engineering Techniques
- Auction Theory and Applications
- Machine Learning and Algorithms
- Logic, programming, and type systems
KU Leuven
2024
UCLouvain
2017-2023
Polytechnique Montréal
2022
In eXplainable Constraint Solving (XCS), it is common to extract a Minimal Unsatisfiable Subset (MUS) from set of unsatisfiable constraints. This helps explain user why constraint specification does not admit solution. Finding MUSes can be computationally expensive for highly symmetric problems, as many combinations constraints need considered. the traditional context solving satisfaction symmetry has been well studied, and effective ways detect exploit symmetries during search exist....
Multi-Valued Decision Diagrams (MDDs) are instrumental in modeling combinatorial problems with Constraint Programming.In this paper, we propose a related data structure called sMDD (semi-MDD) where the central layer of diagrams is non-deterministic.We show that it easy and efficient to transform any table (set tuples) into an sMDD.We also introduce new filtering algorithm, Compact-MDD, which based on bitwise operations, can be applied both MDDs sMDDs.Our experimental results practical...
Table constraints are very useful for modeling combinatorial constrained problems, and thus play an important role in Constraint Programming (CP). During the last decade, many algorithms have been proposed enforcing property known as Generalized Arc Consistency (GAC) on such constraints. A state-of-the art GAC algorithm called Compact-Table (CT), which has recently proposed, significantly outperforms all previously algorithms. In this paper, we extend order to deal with both short supports...
Decision trees are among the most popular classification models in machine learning. Traditionally, they learned using greedy algorithms. However, such algorithms have their disadvantages: it is difficult to limit size of decision while maintaining a good accuracy, and hard impose additional constraints on that learned. For these reasons, there has been recent interest exact flexible for learning trees. In this paper, we introduce new approach learn constraint programming. Compared earlier...
In recent years, there has been a growing interest in using learning-based approaches for solving combinatorial problems, either an end-to-end manner or conjunction with traditional optimization algorithms. both scenarios, the challenge lies encoding targeted problems into structure compatible learning algorithm. Many existing works have proposed problem-specific representations, often form of graph, to leverage advantages \textit{graph neural networks}. However, these lack generality, as...
In industrial contexts, effective workforce allocation is crucial for operational efficiency. This paper presents an ongoing project focused on developing a decision-making tool designed allocation, emphasising the explainability to enhance its trustworthiness. Our objective create system that not only optimises of teams scheduled tasks but also provides clear, understandable explanations decisions, particularly in cases where problem infeasible. By incorporating human-in-the-loop...
In eXplainable Constraint Solving (XCS), it is common to extract a Minimal Unsatisfiable Subset (MUS) from set of unsatisfiable constraints. This helps explain user why constraint specification does not admit solution. Finding MUSes can be computationally expensive for highly symmetric problems, as many combinations constraints need considered. the traditional context solving satisfaction symmetry has been well studied, and effective ways detect exploit symmetries during search exist....
Twenty-seven years ago, E. Freuder highlighted that "Constraint programming represents one of the closest approaches computer science has yet made to Holy Grail programming: user states problem, solves it". Nowadays, CP users have great modeling tools available (like Minizinc and CPMpy), allowing them formulate problem then let a solver do rest job, getting closer stated goal. However, this still requires know formalism respect it. Another significant challenge lies in expertise required...