Schedule, syllabus and examination date

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Changes in the course due to coronavirus

Autumn 2020 we plan for teaching and examinations to be conducted as described in the course description and on semester pages. However, changes may occur due to the corona situation. You will receive notifications about any changes at the semester page and/or in Canvas.

Spring 2020: Teaching and examinations was digitilized. See changes and common guidelines for exams at the MN faculty spring 2020.

Course content

This course gives you an introduction to systems with multiple agents/units/robots that mutually depend on each other’s behaviors in order to evaluate own or collective system performance. The course will cover theory for strategic interaction between self-interested agents as well as more altruistic agents working explicitly together in complex distributed environments. Game theory and swarm intelligence will be central parts of the course curriculum. 

Learning outcome

The course gives you a comprehensive understanding of how to analyze, model and design complex multiagent systems. After completing the course, you:

  • can classify different types of multiagent systems
  • can apply and understand the meaning of the agent concept in a distributed environment
  • can design and use appropriate framework for agent communication and information sharing processes
  • have gained detailed knowledge of the different research methods, especially game theory and swarm intelligence, which are used for modeling the dynamics of distributed agent systems
  • have gained practical experience in modeling multiagent systems through computer simulation and experimentation
  • have gained knowledge in evolutionary game theory applied to multiagent systems

As a PhD candidate, you must also study the principles of evolutionary game theory and be able to present this in a written report.

Admission to the course

PhD candidates from the University of Oslo should apply for classes and register for examinations through Studentweb.

If a course has limited intake capacity, priority will be given to PhD candidates who follow an individual education plan where this particular course is included. Some national researchers’ schools may have specific rules for ranking applicants for courses with limited intake capacity.

PhD candidates who have been admitted to another higher education institution must apply for a position as a visiting student within a given deadline.

Programming experience equivalent to IN2010 – Algorithms and Data Structures. The course IN3050 – Introduction to Artificial Intelligence and Machine Learning may give a useful background.

Overlapping courses


The teaching includes 2 hours of lectures and 1 hour of group work each week throughout the semester.

This course has two mandatory exercises and one written report, which must be approved before you can sit the final exam.


  • Final oral exam which counts 100% towards the final grade.

In case of many students, the final exam may be written.

Two mandatory exercises and one written report must be approved before you can sit the final exam.

Examination support material

Approved calculator (only in Norwegian)

Language of examination

Subjects taught in English will only offer the exam paper in English. You may write your examination paper in Norwegian, Swedish, Danish or English.

Grading scale

Grades are awarded on a pass/fail scale. Read more about the grading system.

Resit an examination

Students who can document a valid reason for absence from the regular examination are offered a postponed examination at the beginning of the next semester.

Re-scheduled examinations are not offered to students who withdraw during, or did not pass the original examination.

Special examination arrangements, use of sources, explanations and appeals

See more about examinations at UiO

Last updated from FS (Common Student System) July 4, 2020 9:18:25 PM

Facts about this course

Teaching language
Norwegian (English on request)