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Artificial intelligence: representation and reasoning [ LINGI2261 ]


6.0 crédits ECTS  30.0 h + 30.0 h   1q 

Teacher(s) Deville Yves ;
Language English
Place
of the course
Louvain-la-Neuve
Online resources

> https://icampus.uclouvain.be/claroline/course/index.php?cid=ingi2261

Prerequisites
  • Programminng abilities in a high-level language, algorithmics and data structures (e.g. SINF1121)
  • Discrete mathematics (e.g. INGI1101)
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

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.
Evaluation methods
  • Exam : 75%
  • Assignments : 25%.  
    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.
Teaching methods
  • 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)
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
Bibliography
  • Stuart Russell, Peter Norvig, Artificial Intelligence : a Modern Approach, 3nd Edition, 2010, 1132 pages, Prentice Hall
  • slides online
Other information

  

Cycle et année
d'étude
> Master [120] in Computer Science and Engineering
> Master [120] in Computer Science
> Master [60] in Computer Science
> Master [120] in Biomedical Engineering
Faculty or entity
in charge
> INFO


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