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.
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.
Information per course offering
Information for Autumn 2024 Doktorand single courses students
- Course location
KTH Campus
- Duration
- 28 Oct 2024 - 13 Jan 2025
- Periods
- P2 (7.5 hp)
- Pace of study
50%
- Application code
50410
- Form of study
Normal Daytime
- Language of instruction
English
- Course memo
- Course memo is not published
- Number of places
Max: 1
- Target group
For doctoral students at KTH only
- Planned modular schedule
- [object Object]
- Schedule
- Schedule is not published
- Part of programme
- No information inserted
This offering is cancelled
Contact
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus DD2528 (Autumn 2021–)Headings with content from the Course syllabus DD2528 (Autumn 2021–) are denoted with an asterisk ( )
Content and learning outcomes
Course contents
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.
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
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
No information inserted
Examiner
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:
http://www.kth.se/en/eecs/utbildning/hederskodex