Number of hours
- Lectures 36.0
- Projects -
- Tutorials -
- Internship -
- Laboratory works -
- Written tests -
ECTS
ECTS 6.0
Goal(s)
The course covers knowledge representation and reasoning algorithms in artificial intelligence. The focus is, in the first part, on logical and symbolic knowledge and, in a second one on probabilistic knowledge. The course will address logical languages, symbolic languages, probabilistic systems, and decision making with these languages and systems.
Responsible(s)
Danielle ZIEBELIN
Content(s)
Knowledge representation and reasoning based on classical logic: (4 lessons)
Rule-based reasoning (forward chaining, backward chaining),
Graph-based reasoning (Conceptual graphs, Knowledge graphs)
Description logics
Uncertain reasoning (4 lessons)
Bayesian models
Bayesian reasoning
Markovian models
Spatio-temporal reasoning (2 lessons)
Quantitative and qualitative approaches
Instant and interval algebra, temporal constraint and reasoning
Spatio-temporal reasoning
Test
Oral midterm exam counting for 30%,
and a 180mn final written exam counting for 70%
The exam is given in english only 
Calendar
The course exists in the following branches:
- Curriculum - Master in Computer Science - Semester 9 (this course is given in english only
)
Additional Information
Course ID : WMM9MO24
Course language(s):
The course is attached to the following structures:
You can find this course among all other courses.