Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
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:
|
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
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
Lectures and practice sessions
Evaluation methods
- 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
