Algorithms for Inference and Constraints
In the last two decades, spectalular improvements were achieved in the area of constraint satisfaction problems (CSP), propositional satisfiability (SAT), temporal and spacial representation and reasoning, and extensions of those different problems. The size of the instances of those combinatorial problems currently solved often increased by several orders of magnitude whereas their scope of applicability keeps growing.
The thematic axis “Algorithms for Inference and Constraints” (AIC) deals with those related research topics and their cross-fertilization. It aims at improving techniques at the conceptual level and to implement them inside innovative solvers (often as open source software).
Research is conducted by a group of internalionally recognized scientists, whose papers are published in the most selective and prestigious conferences and journals of the domain. Furthermore, several solvers were awarded during international solver competitions.
Members in the axis
- Constraint-based Pattern Extraction • Ikram Nekkache
- Constraints-based Mining of Symbolic Data • Abdelhamid Boudane
- Distributed Optimization for combinatorial problems with large constraints • Gaël Glorian
- From graph theory to propositional satisfiability: a new approach for the characterization of tractable classes • Yazid Boumarafi
- Informatique ubiquitaire : techniques de curage d’informations perverties • Yacine Izza
- Practical resolution of the coherence of formulas in modal logic • Valentin Montmirail