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Enseignement scientifique & technique - IA301 : Logics and Symbolic AI : knowledge representation and reasoning

Descriptif

This course aims at providing the bases of symbolic AI, along with a few selected advanced topics.
It includes courses on formal logics, ontologies, symbolic learning, typical AI topics such as revision, merging, etc., with illustrations on preference modeling and image understanding.

Objectifs pédagogiques

At the end of the course students will be able to understand  different kinds of logic families, formulate reasoning in such formal languages, and manioulate tools to represent knowledge and its adaptation to imprecise and incomplete domains through the use of OWL, Protegé and fuzzyDL.

nombre d'heure en présentiel

24

nombre de blocs

16

Diplôme(s) concerné(s)

Pour les étudiants du diplôme Echange non diplomant

Basic knowledge in computer sciences and algebra.

Pour les étudiants du diplôme Diplôme d'ingénieur

Basic knowledge in computer sciences and algebra.

Format des notes

Numérique sur 20

Littérale/grade européen

Pour les étudiants du diplôme Diplôme d'ingénieur

Vos modalités d'acquisition :

The course will be evaluated based on a written exam (50%) and the reports handed in after the practical work, which will require to create a couple of ontologies as part of a decision support system of a freely elected domain problem (50%).

L'UE est acquise si Note finale >= 10
  • Crédits ECTS acquis : 2 ECTS
  • Crédit d'Option 3A acquis : 2

La note obtenue rentre dans le calcul de votre GPA.

Pour les étudiants du diplôme Echange non diplomant

Vos modalités d'acquisition :

The course will be evaluated based on a written exam (50%) and the reports handed in after the practical work, which will require to create a couple of ontologies as part of a decision support system of a freely elected domain problem (50%).

La note obtenue rentre dans le calcul de votre GPA.

Programme détaillé

Introduction - Reminder on logics (syntax, semantics...) and overview of several logics (propositional, first order, modal...)
 Description logics, fuzzy logics, ontologies and Knowledge Graphs
Symbolic learning: formal concept analysis, decision trees
Tutorial on ontology engineering and design. Building your own ontologies using (Fuzzy) OWL, Protegé and fuzzyDL for real life knowledge graph problems- (practical work, including a report at the end of the course)
Some typical examples in AI: revision, merging, abduction, with illustrations on preference modeling and image understanding

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