Modul

Siehe Teilleistungen.

• The students will be able to model various planning tasks in the PDDL language and solve them using off-the-shelf planners.
• The students will understand the approaches used in automated planning and scheduling algorithms, which will allow them to efficiently model and solve real world planning and scheduling problems by selecting the proper algorithms for the given task.

Siehe Teilleistung.

The course offers an introduction to the methods and techniques used in automated planning and scheduling. The course is focused on classical deterministic planning, i.e., planning in a fully observable deterministic environment. The students will learn how to use automated planners and schedulers and also how they work. The topics covered in the lecture include:

• applications of automated planning in artificial intelligence
• formalization of planning problems and the PDDL language
• computational complexity of planning and scheduling
• basic state space search algorithms (forwards/backwards search)
• heuristic search algorithms and planning heuristics
• plan space planning
• planning graph and the graph plan algorithm
• satisfiability based planning
• hierarchical task network planning
• classical scheduling approaches
• constraint-based scheduling
• planning for virtual agents in computer games

2 SWS Vorlesung + 1 SWS Übungen

(Vor- und Nachbereitungszeiten: 4h/Woche für Vorlesung plus 2h/Woche für Übungen; Prüfungsvorbereitung: 15h)

Gesamtaufwand: (2 SWS + 1 SWS + 4 SWS + 2 SWS) x 15h + 15h Prüfungsvorbereitung = 9x15h + 15h = 150h = 5 ECTS