Données issues de nos recherches

Données expérimentales générées pendant la thèse d’Anthony Blomme.

International competitions of constraint solvers help us improving our knowledge about components (e.g., filtering algorithms, search heuristics, exploration strategies, encoding/reformulation techniques, learning approaches, …) that are behind the efficiency of solving systems for combinatorial constrained problems.

Determining the association constant between a cyclodextrin and a guest molecule is an important task for various applications in various industrial and academical fields. However, such a task is time consuming, tedious and requires samples of both molecules. A significant number of association constants and relevant data is available from the literature. The availability of data makes the use of machine learning techniques to predict association constants possible. However, such data is mainly available from tables in articles or appendices.

Determining the association constant between a cyclodextrin and a guest molecule is an important task for various applications in various industrial and academical fields. However, such a task is time consuming, tedious and requires samples of both molecules. A significant number of association constants and relevant data is available from the literature. The availability of data makes the use of machine learning techniques to predict association constants possible. However, such data is mainly available from tables in articles or appendices.

International competitions of constraint solvers help us improving our knowledge about components (e.g., filtering algorithms, search heuristics, exploration strategies, encoding/reformulation techniques, learning approaches, …) that are behind the efficiency of solving systems for combinatorial constrained problems.

Dans ce projet, nous étudions si les humains respectent les principes du domaine de l’argumentation computationnelle.

Current implementations of pseudo-Boolean (PB) solvers working on native PB constraints are based on the CDCL architecture which empowers highly efficient modern SAT solvers. In particular, such PB solvers not only implement a (cutting-planes-based) conflict analysis procedure, but also complementary strategies for components that are crucial for the efficiency of CDCL, namely branching heuristics, learned constraint deletion and restarts. However, these strategies are mostly reused by PB solvers without considering the particular form of the PB constraints they deal with.