TDDD48 · 6 credits · Advanced level (A1N) · Informationsteknologi, Datateknik, Datavetenskap
Automated Planning
Active · next start VT 2027
75%
passed the latest ordinary exam
DIT1 · 4 Jun 2026 · 12 students
85%
passed the ordinary exams, all sittings over the last 5 years
DIT1 · 61 students
Part of 4 programmes
Exam
DIT1 Digital examination
- 5
- 4
- 3
- U
Ordinary exams
85% passed · 61 students
- 75%12
- 90%10
- 86%14
- 100%8
- 82%17
Re-exams
No sittings with at least 5 students.
- Get 4 or 5: 51% (61 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.
- 75%4
- 100%1
- 50%2
- 100%1
- 50%2
- 100%2
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Exam
TEN1 Written examination
- 4
- 3
- U
No sittings with at least 5 students.
Show small sittings (4)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 0%1
- 0%1
- 100%1
- 100%3
Hand-in or lab
LAB2 Laboratory work
- G
Main rounds
100% passed · 27 students
- 100%10
- 100%17
Later rounds
100% passed · 15 students
- 100%15
- Pass a later round: 100% (15 students), compared with 100% (27 students) on the ordinary exam
Show small sittings (2)
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%3
Hand-in or lab
LAB3 Laboratory work
- 5
- 4
- U
Main rounds
97% passed · 30 students
- 100%15
- 93%15
Later rounds
No sittings with at least 5 students.
- Get 4 or 5: 97% (30 students)
Hand-in or lab
LAB1 Laboratory work
- G
No sittings with at least 5 students.
Show small sittings (2)
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
About the course
Aim
Planning is the practice of thinking before acting: not only reacting to the environment, but using knowledge about the world to determine which actions are necessary to achieve a given goal. Automated planning is a central topic in AI and it is essential for constructing robust autonomous systems.
Recently, research in AI planning has seen a great deal of excitement, with a variety of new approaches vastly outperforming older techniques in terms of speed as well as applicability and expressive power. Planning technologies are currently used with great success in applications ranging from production lines and elevators to unmanned aerial vehicles (UAVs) and space applications such as the Hubble Space Telescope and the Mars rovers.
The aim of this course is to provide a comprehensive view of state-of-the-art planning techniques, as well as hands-on experience in constructing and modeling planning domains to solve specific planning problems.
On completion of the course, students should be able to:
- explain and contrast central concepts, models and algorithms in AI planning
- model classical and probabilistic planning problems
- implement, apply and evaluate algorithms for AI planning
- analyze AI planning research literature
Content
- AI planning models
- algorithms for AI planning
- classical planning heuristics
- planning under uncertainty
- latest research in AI planning
- Examiner
- Jendrik Seipp
- Scheduled hours
- 64 h
- Main field of study
- Informationsteknologi, Datateknik, Datavetenskap