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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.

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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.

Course disposition

No information inserted

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

No information inserted

Equipment

No information inserted

Literature

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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

Examination

  • 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

No information inserted

Opportunity to raise an approved grade via renewed examination

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Examiner

Profile picture Elena Troubitsyna

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 web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web DD2528

Offered by

EECS/Computer Science

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:
http://www.kth.se/en/eecs/utbildning/hederskodex