Constraint programming

5.00 credits

30.0 h + 15.0 h

Teacher(s)

Schaus Pierre;

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)

Learning outcomes

At the end of this learning unit, the student is able to :

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:

INFO1.1-3
INFO2.2-4
INFO5.4-5
INFO6.1, INFO6.4

Given the learning outcomes of the "Master [120] in Computer Science" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:

SINF1.M4
SINF2.2-4
SINF5.4-5
SINF6.1, SINF6.4

Students completing successfully this course will be able to

explain and apply techniques for solving Constraint Satisfaction Problems
solve simple problems involving CSP
explain foundations of models and languages for constraint solving
identify problem classes where constraint programming can be apply successfully
model simple problems in the form of constraints, and express these models in a constraint programming language, including search strategies.

Students will have developed skills and operational methodology. In particular, they have developed their ability to:

master rapidly a new programming language;
use technical documents to deepen their knowledge of a topic.

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)

Project and problem sets are mandatory during the semester of the course and cannot be repeated for the second examination session.

Other information

A good background in data-structure and algorithms is required to follow this course and a good knowledge of Java language

Online resources

https://moodleucl.uclouvain.be/course/view.php?id=9158
www.minicp.org

Bibliography

Le site www.minicp.org + lectures suggérées pendant le semestre

Faculty or entity

INFO

Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme

Sigle

Credits

Prerequisites

Learning outcomes

Master [120] in Data Science Engineering

DATE2M

Master [120] in Computer Science and Engineering

INFO2M

Master [120] in Data Science: Information Technology

DATI2M

Master [120] in Computer Science

SINF2M