Description:

The course covers the problematic of automated planning in artificial intelligence and focuses especially on domain independent models of planning problems: planning as a search in the space of states (state-space planning), in the space of plans (plan-space planning), heuristic planning, planning in graph representation of planning problems (graph-plan) or hierarchical planning. The students will also learn about the problematic of planning under uncertainty and the planning model as a decision-making in MDP and POMDP.

Contents:

1. Introduction to the problematic of automated planning in artificial intelligence
2. Representation in form of search in the space of states (state-space planning)
3. Heuristic planning using relaxations
4. Heuristic planning using abstractions
5. Structural heuristics
6. The Graphplan algorithm
7. Compilation of planning problems
8. Representation of the planning problem in form of search in the space of plans (plan-space planning)
9. Hierarchical planning
10. Planning under uncertainty
11. Model of a planning problem as a Markov Decision Process (MDP)
12. Model of a planning problem as a Partially Observable Markov Decision Process (POMDP)
13. Introduction to planning in robotics
14. Applications of automated planning

Seminar contents:

1. Planning basics, representation, PDDL and planners
2. State-space planning, Assignment 1
3. Relaxation heuristics, Assignment 1 Consultations
4. Abstraction heuristics, Assignment 1 Deadline
5. Landmark heuristics, Assignment 1 Results/0-point Deadline
6. Linear Program formulation of heuristics
7. Compilations
8. Partial-order planning
9. Hierarchical Planning
10. Planning with uncertainty, Assignment 2
11. Planning for MDPs, Assignment 2 Consultations
12. Planning for POMDPs, Assignment 2 Consultations
13. Monte Carlo tree search, Assignment 2 Deadline
14. Consultations of exam topics, Assignment 2 Results/0-point Deadline, Credit

Recommended literature:

* Malik Ghallab, Dana Nau, Paolo Traverso: Automated Planning: Theory & Practice, Elsevier, May 21, 2004
* https://www.coursera.org/course/aiplan

Abbreviations used:

Semester:

• W ... winter semester (usually October - February)
• S ... spring semester (usually March - June)
• W,S ... both semesters

Mode of completion of the course:

• A ... Assessment (no grade is given to this course but credits are awarded. You will receive only P (Passed) of F (Failed) and number of credits)
• GA ... Graded Assessment (a grade is awarded for this course)
• EX ... Examination (a grade is awarded for this course)
• A, EX ... Examination (the award of Assessment is a precondition for taking the Examination in the given subject, a grade is awarded for this course)

Weekly load (hours per week):

• P ... lecture
• C ... seminar
• L ... laboratory
• R ... proseminar
• S ... seminar