5.00 credits
30.0 h + 30.0 h
Q2
This learning unit is not open to incoming exchange students!
Language
French
Prerequisites
The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.
Main themes
- Research-based problem solving: problem formulation, informed and uninformed research strategies, local research, behavioral assessment and estimated cost, applications
- Constraint satisfaction: formulation problems, constraint tracing and propagation, applications
- Games and adversarial research: minimax algorithm and Alpha-Beta pruning, applications
- Propositional logic: knowledge representation, inference and reasoning, applications
- First-order logic: knowledge representation, inference and reasoning, forward and backward chaining, rule-based systems, applications
- Planning: planning problem languages, research methods, planning graphs, hierarchical planning, extensions, applications
- AI, philosophy and ethics: "can machines act intelligently?", "can machines really think?", ethics and the risks of artificial intelligence, the future of artificial intelligence
Learning outcomes
At the end of this learning unit, the student is able to : | |
With regard to the AA reference of the "Master's degree in computer science" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes: INFO1.1-3 INFO2.2-4 INFO5.2, INFO5.5 INFO6.1, INFO6.4 With regard to the AA reference 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 SINF5.2, SINF5.5 SINF6.1, SINF6.4 With regard to the AA reference of the "Master [60] in computer science" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes: 1SINF1.M4 1SINF2.2-4 1SINF5.2, 1SINF5.5 1SINF6.1, 1SINF6.4 Students who successfully complete this course will be able to
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Content
- Introduction
- Research
- Informed search
- Local search
- Search with opponent
- Constraint Satisfaction Problem
- Logical agent
- First-order logic and inference
- Classic planning
- Planning in the real world
- Learn from examples
- Philosophical foundations, the present and the future of AI
Teaching methods
- problem-based learning
- Learning by doing
- 5 missions (of two weeks)
- teams of two students
- Lecture (1 hour / week)
- Feedback on closed missions (1 / 2 hour)
- Discussion of the current mission (1 / 2 hour)
Evaluation methods
- Review: 70%
- Assignments: 30%. The work must be personal (team of 2). No collaboration between groups. No copy from internet. Cheating = 0 / 20 for all missions. In case of failure of the missions the weighting of this part will be more important.
- The work can only be carried out during the quadrimester of the course. It is not possible to redo the work during another semester or for the September session.
Other information
Bibliography:
- Stuart Russell, Peter Norvig, Artificial Intelligence : a Modern Approach, 3nd Edition, 2010, 1132 pages, Prentice Hall
- transparents en ligne
Online resources
Faculty or entity
SINC
