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DD2528 Dependable Autonomous Systems 7.5 credits

Autonomous systems rely on artificial intelligence and machine learning to achieve autonomy. It is therefore a challenge to ensure dependability of an autonomous system and guarantee that the risks associated with the system are acceptable. The course will introduce modeling, verification and analysis techniques for achieving dependability of autonomous systems.

About course offering

For course offering

Autumn 2024 Doktorand single courses students

Target group

For doctoral students at KTH only

Part of programme

No information inserted


P2 (7.5 hp)


28 Oct 2024
13 Jan 2025

Pace of study


Form of study

Normal Daytime

Language of instruction


Course location

KTH Campus

Number of places

Max: 1

Planned modular schedule


For course offering

Autumn 2024 Doktorand single courses students

Application code



For course offering

Autumn 2024 Doktorand single courses students


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Course coordinator

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Headings with content from the Course syllabus DD2528 (Autumn 2021–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

Techniques to achieve dependability, safety analysis, derivation of dependability requirements from safety analysis, modelling and verification of safety requirements, safety assurance case, multi-agent systems, emergent behaviour, goal-oriented modelling and verification of safe and reliable multi-agent autonomous systems, evolutionary algorithms and learning algorithms for mission planning and navigation, safety of mission planning.

Intended learning outcomes

After passing the course, the student shall be able to

  • describe dependability attributes formally
  • specify dynamic behaviour of autonomous systems and their properties
  • use risk assessment and safety analysis techniques to define dependability requirements
  • model and verify autonomous systems by means of automatic tools

in order to

  • be able to work with autonomous safety critical systems in research and/or development
  • be able to identify risks in connection with autonomous systems and use modelling, verification and security techniques to prevent them.

Literature and preparations

Specific prerequisites

  • Knowledge and skills in programming, at least 6 higher education credits, equivalent to completed course DD1331/DD1310/DD1311/DD1312/DD1314/DD1315/DD1316/DD1318/DD1321/DD100N/ID1018.
  • Knowledge in algorithms and data structures, at least 6 higher education credits, equivalent to completed course DD1320/DD1321/DD1325/DD1327/DD1338/DD2325/ID1020/ID1021.
  • Knowledge in mathematics equivalent to at least 22.5 higher education credits.

Recommended prerequisites

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Examination and completion

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

Grading scale

A, B, C, D, E, FX, F


  • LAB2 - Laboratory work, 6.5 credits, grading scale: A, B, C, D, E, FX, F
  • QUI1 - Digital quiz, 1.0 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

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Opportunity to raise an approved grade via renewed examination

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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

Main field of study

Computer Science and Engineering

Education cycle

Second cycle

Add-on studies

No information inserted

Supplementary information

In this course, the EECS code of honor applies, see: