Lecture: Planning and Optimization

Course Number 45400-01
Lecturers Malte Helmert
Gabriele Röger
Assistants Martin Wehrle
Thomas Keller
Tutors Salome Eriksson
Time and Location Mon 14:15 - 16:00; Seminarraum 00.003, Spiegelgasse 1
Thu 14:15 - 16:00; Seminarraum 00.003, Spiegelgasse 1
Start 22-09-2016
Exercises Thu 16:00 - 17:30; Seminarraum 00.003, Spiegelgasse 1
Prerequisites Good knowledge in the foundations and core areas of computer science are assumed (in particular algorithms and data structures, complexity theory, mathematical logic, programming).

Good knowledge of the contents of the course "Foundations of Artificial Intelligence" (13548) is assumed, in particular the chapters on state-space search. Students who have not previously passed the prerequisite course are strongly advised to learn the necessary material in self-study prior to the beginning of this course. If you are interested in participating in this course but do not yet have strong knowledge on state-space search, we strongly encourage you to contact the lecturers prior to the semester to discuss a possible self-study plan.
Objectives The participants get to know the theoretical and algorithmic foundations of action planning as well as their practical implementation. They understand the fundamental concepts underlying modern planning algorithms as well as the theoretical relationships that connect them. They are equipped to understand research papers and conduct projects in this area.
Literature There is no textbook for the course. The course slides will be made available to the participants, and additional research papers complementing the course materials will be uploaded to the course webpage during the semester.
Assessment Lehrveranst.-begleitend

Please note: Oral examination, January 30 - February 01, 2017
Marked homework exercises will be handed out weekly in order to verify the learning progress. To qualify for the oral examination, students must obtain at least 50% of the total marks from the exercises. Exercise marks do not contribute to the final grade for the course, which is exclusively based on the oral examination.
Credit Points 8
Grades 1-6 0,5
Modules Modul Kerninformatik (MSF - Informatik (Studienbeginn vor 01.08.2016))
Modul Kerninformatik (Master Informatik 10)
Wahlbereich Master Informatik: Empfehlungen (Master Informatik 10)
Modul Praxis aktueller Informatikmethoden (MSF - Informatik (Studienbeginn vor 01.08.2016))
Modul Applications of Distributed Systems (Master Computer Science 16)
Modul Concepts of Machine Intelligence (Master Computer Science 16)
Modul Concepts of Machine Intelligence (MSF - Computer Science)
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