De elektronische studiegids voor het academiejaar 2025 - 2026 is onder voorbehoud.





Artificial Intelligence and Logic Programming (4729)
  
Coordinating lecturer :Prof. dr. Jan VAN DEN BUSSCHE 

Language of instruction : English
Credits: 6,0
  
Period: semester 1 (6sp)
  
2nd Chance Exam1: Yes
  
Final grade2: Numerical
 
Exam contract: not possible
 
Sequentiality
 
   Mandatory sequentiality bound on the level of programme components
 
 
  Following programme components must have been included in your study programme in a previous education period
    Probability theory and statistics (2941) 6.0 stptn
 

Content

·Heuristics for searching in a large space of solutions
.Planning
·Knowledge representation
.Logic programming
·SAT solving
·Probabilistic reasoning
·Neural networks
·Reinforcement learning
·Natural language processing
·Philosophical and societal aspects of AI.


The course does not address topics related to learning from data (data mining, machine learning), as existing courses already deal with these topics.


Organisational and teaching methods
Organisational methods  
Lecture ✔  
Response lecture ✔  


Evaluation

Period 1    Credits 6,00

Evaluation method
Oral evaluation during teaching period30 %
Presentation✔
Written exam70 %
Closed-book ✔

Second examination period

Evaluation second examination opportunity different from first examination opprt
No
 

Compulsory textbooks (bookshop)
 

Textbook 1:

Artificial Intelligence : A Modern Approach, S. Russell & P. Norvig, Fourth Edition, Pearson


Learning outcomes
Master of Computer Science
  •  EC 

    • EC 1: A graduate of the Master of Computer Science programme has insight into the most important technological developments in the field of computer science and the underlying scientific principles.

  •  EC 

    • EC 4: A graduate of the Master of Computer Science programme takes account of the limitations of computer science, such as the existence of undecidedness and the existence of important unresolved problems in computer science such as the P=NP problem.

  •  EC 

    • EC 9: A graduate of the Master of Computer Science programme is able to clearly report both orally and verbally on his or her work in a national and international context.

  •  EC 

    • EC 10: A graduate of the Master of Computer Science programme is able to work in team; he or she is able to distribute and coordinate the activities through cooperation in small and large groups.

 
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Offered inTolerance3
Master Computer Science profile Artificial Intelligence J
Master of Computer Science choice J



1   Education, Examination and Legal Position Regulations art.12.2, section 2.
2   Education, Examination and Legal Position Regulations art.15.1, section 3.
3   Education, Examination and Legal Position Regulations art.16.9, section 2.