Course content

The course topics will vary each year, dependent on available teachers and scientific interests. A selection of topics will be made public at the start of the semester. Possible topics include:

Introduction to artificial intelligence and intelligent agents

Problemsolving and search methods

Knowledge, reasoning, and planning (KRP)

Uncertainties and probabilities in KRP

Learning

Communication, perception, action

Typical methods and terminology that will be studied are:

Genetic algorithm (GA)

Neural networks (NN)

Particle swarm optimisation (PSO)

Ant colony optimisation (ACO)

Intelligent agents

Intelligent algorithms such as BFS,DFS, A*, D*, Dijkstra's algorithm

Expert systems

Fuzzy logic

Classification systems

Machine learning

Artificial intelligence (AI)

Computational intelligence (CI)

Etc.

Learning outcome

Læringsutbytte - Kunnskap:

present the selected topics, emphasising application, methods, and advantages/disadvantages.

 

Læringsutbytte - Ferdigheter:

construct models and implement simulations of the models within the scope of the selected topics.

solve practical and theoretical problems using the methods in the selected topics.

Læringsutbytte - Kompetanse:

find, study, understand and use relevant scientific literature as a foundation for developing own models and simulations.

document own work in a scientifically satisfactorily manner through the written assignments.

Learning methods and activities

Pedagogiske metoder:

Lectures, assignments individually or in groups, literature study, discussion, demonstrations, all with a focus on application and simulation. Compulsory assignment with feedback from the teacher. 

Obligatoriske arbeidskrav:

All compulsory assignments must be passed for admission to the exam. The assignments are gathered in a portfolio that forms the basis for the oral exam.

Compulsory assignments

• Mandatory assignment

Recommended previous knowledge

Courses from first and second year.