Due to the COVID-19 crisis, the information below is subject to change,
in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
5 credits
30.0 h + 15.0 h
Q2
Teacher(s)
Deville Yves; Schaus Pierre; Schaus Pierre (compensates Deville Yves);
Language
English
Main themes
- Constraints and domains
- Practical aspects of constraint solvers
- Constraint Satisfaction Problems (CSP)
- Models and languages for constraint programming
- Methods and techniques for constraint solving (consistency, relaxation, optimization, search, linear programming, global constraints, ...)
- Search techniques and strategies
- Problem modelling and resolution
- Applications to differents problem classes (e.g. planification, scheduling, ressource allocation, economics, robotics)
Aims
At the end of this learning unit, the student is able to : | |
| 1 |
Given the learning outcomes of the "Master in Computer Science and Engineering" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:
|
Content
- Constraint Programming : a Declarative Programming paradigm
- Architecture of a constraint programming solver
- Global contraints and implementation techniques (incrementality, etc)
- Search techniques and strategies
- Combinatorial optimization problem modeling and solving
- Applications to different problem classes (e.g. planification, scheduling, resource allocation, economics, robotics)
Teaching methods
Due to the COVID-19 crisis, the information in this section is particularly likely to change.
Lectures and practice sessions
Evaluation methods
Due to the COVID-19 crisis, the information in this section is particularly likely to change.
- Projects (50% of final grade)
- Written exam (50% of final grade)
Other information
Background
- LINGI2261 : Artificial Intelligence
Online resources
Bibliography
Le site www.minicp.org + lectures suggérées pendant le semestre
Faculty or entity
INFO
