2023 XCSP3 competition: Models, Instances and Results - Oct 18, 2023
Gilles Audemard, Christophe Lecoutre, Emmanuel Lonca, doi.org/10.5281/zenodo.10017717, 2023

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.

Curated Dataset of Association Constants Between a Cyclodextrin and a Guest for Machine Learning: Raw Data and Generation Script - Jan 27, 2023
Gökhan Tahıl, Fabien Delorme, Daniel Le Berre, Éric Monflier, Adlane Sayede, Sébastien Tilloy, doi.org/10.5281/zenodo.7575579, 2023

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.

Curated Dataset of Association Constants Between a Cyclodextrin and a Guest for Machine Learning - Jan 27, 2023
Gökhan Tahıl, Fabien Delorme, Daniel Le Berre, Éric Monflier, Adlane Sayede, Sébastien Tilloy, doi.org/10.5281/zenodo.7575539, 2023

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.

2022 XCSP3 competition: models, instances and results - Jan 27, 2023
Gilles Audemard, Christophe Lecoutre, Emmanuel Lonca, doi.org/10.5281/zenodo.7575992, 2023

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.

Data from the psychological experiment - Oct 3, 2022
Srdjan Vesic, Predrag Teovanovic, Bruno Yun, 2022

In this project, we study whether humans comply with the principles from the area of computational argumentation.

On Dedicated CDCL Strategies for PB Solvers Companion Artifact - May 12, 2021
Daniel Le Berre, Romain Wallon, doi.org/10.5281/zenodo.4751685, 2021

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.