Artificial intelligence

linfo1361  2025-2026  Louvain-la-Neuve

Artificial intelligence
The version you’re consulting is not final. This course description may change. The final version will be published on 1st June.
5.00 credits
30.0 h + 30.0 h
Q2
Teacher(s)
Deville Yves;
Language
French
Prerequisites
LEPL1402: Programming in a high-level language
Main themes
  •  Problem solving through research: problem formulation, informed and uninformed search strategies, local search, performance evaluation and cost estimation, practical applications.
    • Constraint satisfaction: problem formulation, monitoring and propagation of constraints, use cases and various applications.
    • Games and adversarial search: Minimax algorithm, Alpha-Beta pruning, Monte-Carlo Tree Search, and application examples.
    • Logic: propositional and first-order logic, knowledge representation, inference, reasoning, and applications in various contexts.
    • Decision making: simple and complex decisions, collective decisions, probability theory, decision networks, Markov decision-making processes, game theory, multi-agent systems.
    • Learning: introduction to supervised learning, decision trees, reinforcement learning, regression, and practical applications.
Learning outcomes

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

With regard to the AA framework of the “Bachelor in Computer Science” program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:
    SINFS2.2-4
    SINFS3.2-3
    SINFS4.3
    SINFS5.1-3
With regard to the AA standard of the “Bachelor of Engineering Sciences, civil engineer orientation” program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:
    FSA1BA2.3-4, FSA1BA2.5-8
    FSA1BA3.1
    FSA1BA4.2, FSA1BA4.4
Students who successfully complete this course will be able to:
  • Explain and appropriately use the fundamental concepts of knowledge representation, problem solving and reasoning methods, as employed in Artificial Intelligence.
  • Evaluate the applicability, strengths and limitations of knowledge representation techniques, problem solving and reasoning methods in the context of solving concrete problems in engineering.
  • Develop intelligent systems by integrating various solutions adapted to concrete problems.
  • Discuss the central role of knowledge representation, problem solving, and reasoning methods in the design and implementation of intelligent systems.
  • Understand and deepen concepts as well as practical application skills through numerous projects and assignments.
Students will have acquired methodological and operational skills. In particular, they will have developed their ability to:
  • Manage deadline constraints and competition when developing an application aimed at optimizing its efficiency.
  • Work in groups on various missions and projects aimed at automatically solving concrete problems or producing artificially intelligent systems.
 
Content
  • Introduction and Intelligent Agents
  • Uninformed and Informed Search
  • Local Search and Heuristics
  • Constraint Satisfaction Problems (CSP)
  • Adversarial Search (Games) and Monte Carlo Tree Search (MCTS) Techniques
  • Logical Agents (First-Order Logic and Inference)
  • Simple Decision Making
  • Complex Decision Making
  • Multi-Agent Decision Making
  • Supervised Learning from Examples
  • Reinforcement Learning
Teaching methods
  • Problem-based learning
  • Learning by doing
  • 3 long-term projects to be completed in pairs (over several weeks)
  • Lectures
  • Theoretical and practical exercises depending on the topic
  • Feedback on completed projects and exercise corrections
Evaluation methods
  • The evaluation will be based on continuous assessment of the missions and assignments completed during the year, as well as a final exam.
  • Continuous assessment includes graded assignments, which will result in a single overall score, communicated at the end of the final assignment. Failure to follow the methodological guidelines defined on Moodle, especially regarding the use of online resources or collaboration between students, will result in a global score of 0 for continuous assessment.
  • The use of ChatGPT or any equivalent tool is strictly prohibited for completing missions and assignments. The professor reserves the right to summon students for an additional oral Q&A session to verify understanding of the submitted work. In case of failure, a global score of 0 will be awarded for the assignment.
  • The weighting of assignments and the final exam is as follows: if all assignments are graded 15/20 or higher, their weight will be 40%, and the exam will count for 60%. If the average of the assignments is below 15/20, their weight will be reduced to 30%, and the exam will count for 70%.
  • Assignments can only be completed during the course term. It is not possible to redo the assignments during another semester or for the August/September session.
  • The exam will be written, but in case of doubt about the grade to be assigned to a student, an additional oral exam may be arranged.
Online resources
https://moodle.uclouvain.be/course/view.php?id=1338
Bibliography
  • Stuart Russell, Peter Norvig, Artificial Intelligence : a Modern Approach, 3nd Edition, 2010, 1132 pages, Prentice Hall
  • transparents en ligne
Faculty or entity
> INFO


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

Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Specialization track in Computer Science
FILINFO
5
Bachelor in Computer Science
SINF1BA
5
Master [120] in Electro-mechanical Engineering
ELME2M
5
Master [120] in Data Science Engineering
DATE2M
5
Master [120] in Data Science: Information Technology
DATI2M
5
Mineure Polytechnique
MINPOLY
5