Automated Planning

Course guarantor:

Stefan Edelkamp

Lecturer:

Stefan Edelkamp, Lukáš Chrpa

Tutor:

Stefan Edelkamp, Lukáš Chrpa

Supervisor:

Department of Computer Science

Synopsis:

The goal of the course is to provide comprehensive and detailed information about state-of-the-art methods in the area of symbolic artificial intelligence for automated planning. The students will learn about the classical, but also various extensions of, planning models and related knowledge engineering techniques; the most prevalent search methods, incl. variants of greedy best first search, hill climbing methods, and the A* algorithm in context of automated planning; and thorough description and implementation details of currently most efficient domain-independent heuristics. A student successfully passing the course will understand the principles of automated planning and symbolic AI for sequential decision making. Additionally, they will be equipped with enough knowledge to design and implement novel domain-specific and/or domain-independent heuristics not only in context of automated planning, but applicable generally in tasks.

Requirements:

Syllabus of lectures:

1. Introduction to Automated Planning

2. Classical and Extended Planning Models

3. Transformations Between the Planning Models

4. Knowledge Engineering and Modelling

5. Domain-Independent vs. Domain-Dependent Modeling

6. State Invariants

7. Heuristic State-Space Search

8. Relaxation Heuristics

9. Abstraction heuristics

10. Bisimulation and Symbolic Planning

11. Heuristics using LP formulations

12. Metric and Temporal Planning

13. Preferences and Hybrid Planning

14. Planners and Implementations

Syllabus of tutorials:

Study Objective:

Study materials:

* Malik Ghallab, Dana S. Nau, Paolo Traverso: Automated Planning and Acting. Cambridge University Press 2016, ISBN 978-1-107-03727-4

* Stefan Edelkamp, Stefan Schrödl: Heuristic Search - Theory and Applications. Academic Press 2012, ISBN 978-0-12-372512-7, pp. I_XXIV, 1-836

* Malik Ghallab, Dana S. Nau, Paolo Traverso: Automated planning - theory and practice. Elsevier 2004, ISBN 978-1-55860-856-6, pp. I-XXVIII, 1-635

Note:

Time-table for winter semester 2024/2025:

Time-table is not available yet

Time-table for summer semester 2024/2025:

Time-table is not available yet

The course is a part of the following study plans: