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DD2438 Artificial Intelligence and Multi Agent Systems 15.0 credits

Administer About course

In this course, we will study problems related to

  • AI for computer games (such as Call of Duty, FIFA, Rocket League)
  • AI for robotics (such as multi robot search and rescue, robot soccer, domestic service robots)

This includes topics such as

  • Cooperative path planning
  • Cooperative task assignment
  • Formation keeping
  • Motion coordination 

In computer games, as well as in future robot systems, groups of cooperative agents, so-called multi agent systems, bring new possibilities in terms of capabilities, efficiency and flexibility.

In this course you will design and implement a number of solutions to multi agent problems. You will be using the simulation environment Unity 3D, that enable you to understand, test and develop you designs, in a polished environment, but also export them to stand alone games (if you want to).

The course will be given in English.

Information per course offering

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.

Termin

Information for Spring 2025 agent25 programme students

Course location

KTH Campus

Duration
14 Jan 2025 - 2 Jun 2025
Periods
P3 (7.0 hp), P4 (8.0 hp)
Pace of study

50%

Application code

60209

Form of study

Normal Daytime

Language of instruction

English

Course memo
Course memo is not published
Number of places

Places are not limited

Target group

TCSCM, TIEMM, TMAIM, TSCRM, TMEKM

Planned modular schedule
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Contact

Examiner
No information inserted
Course coordinator
No information inserted
Teachers
No information inserted
Contact

Petter Ögren, petter@kth.se, telefon: 790 6646

Course syllabus as PDF

Please note: all information from the Course syllabus is available on this page in an accessible format.

Course syllabus DD2438 (Spring 2025–)
Headings with content from the Course syllabus DD2438 (Spring 2025–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The students will in project form design and implement a multi-agent team performing a task. The actual course content can vary based on which solutions the students choose to use.
The following areas will to a smaller or greater extent, dependent on the students' choices, be treated in the course: 

  • Cooperative path planning
  • Cooperative task assignment
  • Formation keeping
  • Motion coordination 

The course will also train the ability to manage, plan and participate in larger projects,  assess existing solutions and their possible use, and work with existing code.

Intended learning outcomes

After passing the course, the student should be able to

  • use a number of important tools and technologies used in artificial intelligence and multi-agent systems
  • develop multi-agent systems
  • assess the value of, and to a suitable extent utilize, existing solutions as a part of a programming project
  • plan and lead the work in a larger project
  • present their work and results, both orally and in writing
  • write a basic scientific paper in English.

Literature and preparations

Specific prerequisites

Knowledge in introduction to robotics, 7,5 credits, corresponding to completed course DD2410

or

knowledge in artificial intelligence, 4 credits, corresponding to completed course DD2380/ID1214 or completed module LAB2 in DD2380 or completed module INL1 in ID1214.

Recommended prerequisites

DD2380 Artificial Intelligence (or a similar course)

Equipment

No information inserted

Literature

No information inserted

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

P, F

Examination

  • INL1 - Hand-in Assignment, 3.0 credits, grading scale: P, F
  • PRO1 - Software Engineering Project, 4.0 credits, grading scale: P, F
  • PRO2 - Software Engineering Project, 4.0 credits, grading scale: P, F
  • PRO3 - Software Engineering Project, 4.0 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

Examiner

Profile picture Petter Ögren

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

EECS/Intelligent Systems

Main field of study

Computer Science and Engineering, Information Technology, Information and Communication Technology

Education cycle

Second cycle

Add-on studies

No information inserted

Contact

Petter Ögren, petter@kth.se, telefon: 790 6646

Supplementary information

In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex