TDDE13 · 6 credits · Advanced level (A1N) · Datateknik, Datavetenskap
Multi Agent Systems
Active · next start HT 2026
100%
passed the latest regular sitting
LAB1 · 3 Feb 2026 · 41 students
100%
passed the regular sittings, all sittings over the last 5 years
LAB1 · 102 students
This module only reports passing results, so the pass rate does not show how many did not pass.
Part of 4 programmes
Hand-in or lab
LAB1 Laboratory work
- G
Main rounds
100% passed · 102 students
- 100%41
- 100%18
- 100%15
- 100%10
- 100%18
Later rounds
No sittings with at least 5 students.
Show small sittings (6)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%1
- 100%1
- 100%2
- 100%1
- 100%1
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
UPG1 Assignments
- 5
- 4
- 3
- U
Main rounds
98% passed · 98 students
- 95%42
- 100%16
- 100%12
- 100%10
- 100%18
Later rounds
No sittings with at least 5 students.
- Get 4 or 5: 71% (98 students)
Show small sittings (6)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 100%1
- 100%1
- 100%4
- 100%1
- 100%1
- 100%1
About the course
Aim
The overall aim of the course is to give an overview of multiagent systems and in depth knowledge of some areas of multiagent systems. After the course students should be able to:
- List and explain important problems and techniques in the area of multiagent systems.
- Explain how modern multiagent systems based on large language models work and can be used.
- Explain how central algorithms in the area of multiagent systems work.
- Be able to implement some central algorithm in the area of multiagent systems.
- Evaluate and apply different game theoretic approaches.
- Design and use auctions for allocating resources in a multiagent system.
- Model relevant aspects of multiagent system decision making using markov decision processes and logics.
Content
- Architectures for multiagent systems
- Modern agent systems based on large language models
- Distributed AI, including distributed constraint satisfaction and optimization
- Game theory, including normal form and extensive form games
- Communication, including speech acts
- Aggregated preferences, including voting
- Auctions for multiagent resource allocation
- Multiagent decision-making, including task allocation
- Examiner
- Fredrik Heintz
- Scheduled hours
- 60 h
- Main field of study
- Datateknik, Datavetenskap