AI Planning Prof. Dr. Jörg Hoffmann Advanced Lecture (9 CP), Winter Semester 2019

News

12.11.2019

Proof Relaxed Plan Heuristic Correctness

Hi all,

for lack of a more adequate means to communicate this information, I'm posting this as "news" for the time being:

Regarding the discussion today in the lecture, proof of correctness for relaxed plan extraction:The proof as stated was correct, it just... Read more

Hi all,

for lack of a more adequate means to communicate this information, I'm posting this as "news" for the time being:

Regarding the discussion today in the lecture, proof of correctness for relaxed plan extraction:The proof as stated was correct, it just didn't spell out in sufficient detail why the case of a precondition in Closed isn't problematic. I have fixed this in the post-handouts. (The reason is simply that, then, the best supporter for that precondition must have been selected beforehand)

best,

Jörg

 

22.10.2019

Paper Exercises

We just uploaded the first Paper Exercise Sheet.

This is just to remind you that you do not need to submit solutions to the sheet to qualify for the exam.

You can do so to obtain some feedback on your solution, however. Submission will be via CMS until one day... Read more

We just uploaded the first Paper Exercise Sheet.

This is just to remind you that you do not need to submit solutions to the sheet to qualify for the exam.

You can do so to obtain some feedback on your solution, however. Submission will be via CMS until one day before the tutorial in which the sheet is discussed. Please read the submission instructions on the sheet for further details.

 

17.10.2019

Programming Projects

The programming projects overview is now online (see course material). In this sheet, you will find all the organizational details regarding the projects (repository setup, nightly tests, grading), as well as a short description for each individual subproject and... Read more

The programming projects overview is now online (see course material). In this sheet, you will find all the organizational details regarding the projects (repository setup, nightly tests, grading), as well as a short description for each individual subproject and their dependencies.

There will be a programming workshop on Thursday October 31st at 2:15pm in our seminar room (E1 1, room 3.06). In the workshop, we will give you a brief overview of the projects you can choose to implement. We will also give you an introduction to the Fast Downward framework with some live coding to prepare you for the projects (in fact, you might already be able to finish the first project in the workshop). Before the workshop, you should have read the projects overview, and have set up your repository so you can follow the live coding in the workshop.

Please use the forums for general questions about the projects. If you have specific questions about your code, you can visit our offices (E1 1, rooms 3.14 and 3.08) during the office hours (Wednesdays 2-4pm) or send a mail to Maximilian Fickert.

17.10.2019

2. Planning Formalisms: hand-outs available

I uploaded the hand-outs now. Sorry, I had forgotten to upload the pre-handouts before the lecture yesterday.

15.10.2019

1. About this Course: post-handout available

The post-handouts for Chapter 1. About this Course are now available under Materials.

15.10.2019

Welcome!

Welcome everybody to the 19/20 edition of our AI Planning course!

If you have any questions, please do not hesitate to send emails to me or Maximilian Fickert.

Good luck!

Show all
 

AI Planning

AI Planning is one of the fundamental sub-areas of Artificial Intelligence, concerned with algorithms that can generate strategies of action for arbitrary autonomous agents in arbitrary environments. The course will address so-called classical planning, where the actions and environment are assumed to be deterministic; this is a central area in planning, and has been the source of many influential ideas. It is also successfully applied in practice, as we will exemplify in the course. We will examine the technical core of the current research on solving this kind of problem. We will consider four different paradigms for automatically generating heuristic functions (lower bound solution cost estimators): critical paths, ignoring delete lists, abstractions, landmarks. Apart from understanding these techniques themselves, we will learn how to analyze, combine, and compare such estimators. We will furthermore consider optimality-preserving pruning techniques based on partial-order reduction, symmetries, and dominance pruning. The course contains many research results from the last decade, close to the current research frontier in planning.

Prerequisites. Ideally, participating students should have successfully completed an introductory course in Artificial Intelligence. However, the course is self-contained and any student with a solid basis in Computer Science -- algorithms, data structures, programming, propositional logic, NP-hardness -- should in principle be able to follow. Prior knowledge about search (the A* algorithm etc) is an advantage. Students who have already passed Automatic Planning in previous years are not allowed to attend the course.



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