Artificial intelligence

lingi2261  2020-2021  Louvain-la-Neuve

Artificial intelligence
Due to the COVID-19 crisis, the information below is subject to change, in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
5 credits
30.0 h + 30.0 h
Q2
Teacher(s)
Language
English
Main themes
  • Problem solving by searching : formulating problems, uninformed and informed search search strategies, local search, evaluation of behavior and estimated cost, applications
  • Constraint satisfaction : formulating problems as CSP, backtracking and constraint propagation, applications
  • Games and adversarial search : minimax algorithm and Alpha-Beta pruning, applications
  • Propositional logic : representing knowledge in PL, inference and reasoning, applications
  • First-order logic : representing knowledge in FOL, inference and reasoning, forward and backward chaining, rule-based systems, applications
  • Planning : languages of planning problems, search methods, planning graphs, hierarchical planning, extensions, applications
  • AI, philosophy and ethics : "can machines act intelligently ?", "can machines really think ?", ethics and risks of AI, future of AI
Aims

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

1 Given the learning outcomes of the "Master in Computer Science and Engineering" 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
Given the learning outcomes 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
Given the learning outcomes 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 completing successfully this course will be able to
  • explain the basic knowledge representation, problem solving and reasonning methods in artificial intelligence
  • assess the applicability, strength, and weaknesses of the basic knowledge representation, problem solving and reasonning in solving particular engineering problems
  • develop intelligent systems by assembling solutions to concrete computational problems
  • discuss the role of knowledge representation, problem solving and reasonning in intelligent-system engineering
Students will have developed skills and operational methodology. In particular, they have developed their ability to:
  • master a new programming language using online tutorial
  • deal with deadlines and competitivity in developping the most efficient solution.
 
Content
  • Introduction
  • Search
  • Informed search
  • Local search
  • Adversarial search
  • Constraint Satisfaction Problem
  • Logical Agent
  • First-order logic and Inference
  • Classical Planning
  • Planning in the real world
  • Learning from examples
  • Philosophical foundations & Present and future of AI
Teaching methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

  • Problem-Based Learning
  • Learning by doing
  • 5 assignments (one per two weeks)
  • Team of two students
  • Limited teaching (1 hour / week)
  • Feed-back of problems (1/2 hour )
  • Discussion of current problem (1/2 hour)
Evaluation methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

  • Exam : 70%
  • Assignments : 30%.  
    Assignments must be personnal (team of 2). No collaboration between groups. No copying from Internet. Cheating = 0/20 all assignments. In case of failure of the missions the weight of this part will be more important.
  • Assignments may be realized only during the quadrimester of the course. It's not possible to realize the assignments during another quadrimester or for the exam session of september.
  • The exam will be written, but in case of doubt on the part of the teacher as to the grade to be given to a student, the student may be questioned orally.
Bibliography
  • Stuart Russell, Peter Norvig, Artificial Intelligence : a Modern Approach, 3nd Edition, 2010, 1132 pages, Prentice Hall
  • transparents en ligne
Faculty or entity


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

Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Computer Science and Engineering

Master [120] in Computer Science

Master [120] in Data Science Engineering

Master [120] in Data Science: Information Technology

Master [120] in Biomedical Engineering

Master [60] in Computer Science