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Knowledge Representation and Reasoning (6 ECTS)

Objective:

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.

Outline :

  • 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

Teachers:

Evaluation Marks are given after :

  • Oral midterm exam counting for 30%,
  • and a 180mn final written exam counting for 70%
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Page last modified on March 22, 2021, at 01:15 PM