Foundations of Artificial Intelligence (FAI) Group

Seminar: Planning and Learning

Basics. Seminar, 7 graded ECTS points.

The seminar will be run in a block format. There will be an initial meeting on Wednesday, April 16, 16:15--17:45. All student presentations will be given on a single day after the end of term. A detailed schedule is given below.

All meetings will take place in room 3.06, Building E1 1. The seminar language is English throughout.

The seminar supervisors are Prof. Dr. Jörg Hoffmann, Dr. Peter Kissmann, and Dr. Alvaro Torralba.

Your task will be to read and understand a piece of research, to write a summary paper in your own words, to give a presentation, and to provide detailed feedback for the paper and presentation of a fellow student.

All email interaction must be pre-fixed with "[PAL14]" in the email subject.

No plagiarism. It is Ok (and encouraged!) to use web resources to further your understanding of your assigned topic. However, it is inadmissible to use pieces of such material for your summary paper or presentation. Any plagiarism will result in disqualification from the seminar.

Content. Automatic 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. A ubiquituous property of planning applications in practice is that the algorithms are run on similar instances -- from the same domain, controlling the same kind of agent in the same kind of environment -- over and over again. Naturally, we want to be able to learn from this experience in order to improve performance over time. The seminar includes a number of recent works in that direction. Specifically, we will cover three different areas where learning is used: performance prediction and portfolio configuration; learning and improving heuristic functions; learning policies (i.e., strategies of action aimed at solving instances from the domain at hand).

Prerequisites. Participants should have successfully completed an introductory course in Artificial Intelligence, and should be familiar with the area of planning to the extent of the material covered in the Artificial Intelligence course.

It is not a necessary prerequisite to have completed the Automatic Planning course, although that is of course an advantage.

Registration. Seminar, 7 graded ECTS points. The seminar has 8 participation slots for students. Registration for the seminar will be open from April 1 until April 14 (midnight). Please do not try to register ahead of time; we will only consider applications reaching us within the given time window!

To apply for registration, send an email to Peter Kissmann. In the email, give a brief description of your relevant background. In particular, say whether you got a BSc and from which university, and describe previous lectures/seminars you completed in the areas of Artificial Intelligence, Planning, and Machine Learning. Say a few words regarding why you are interested in participating in the seminar.

You will be notified by email on April 15, informing you whether or not you are registered.

Grading. The final grading will be based on:

• The quality of the feedback you provide to other students.
• The quality of your summary write-up.
• The ability to stick to deadlines.
• The quality of your final presentation.
• The interaction during the final presenations.

Summary Paper. For the summary paper, you must use this tex template. Note in particular that you are required to read at least 2 related papers, for the related work section.The seminar paper should be about 4 pages long (not counting the literature list, and in the double-column format of the template). This is a rough guideline, not a strict rule. If you need, say, 5-6 pages to do your paper justice then definitely do so.

Schedule and Deadlines.

• April 1--14: Apply for registration.
• April 15: Registration notification.
• April 16, 16:15--17:45: Initial meeting; brief explanation of the 8 topics; opportunity for you to ask questions.

• April 21: Send a ranked top-5 list of the topics you would like to take, by email to Peter Kissmann. That is, send something like "7,3,8,5,1".

• April 23: Receive your assignment: A topic (each of which comes with fixed supervisor, see the topic list below); a mentee student (to whom you will provide feedback, see the following deadlines); and a mentor student (who will provide feedback to you, see the following deadlines). In topic areas 1 and 2, the mentee/mentor assignment will be a "cycle" through the respective 3 topics; in topic area 3, the mentee/mentor will be the same student, i.e., the student working on the respective other topic within area3.

  Read the material associated with your topic carefully, and prepare an initial version of your summary paper, using the tex template given above.

• May 15--30: Make an appointment with your supervisor to discuss your paper: Give a brief oral summary of the paper (15--20 minutes); ask questions; do not bring the summary paper (yet).

• June 6: Send your summary paper to your mentor student (cc supervisor).
• June 20: Send feedback regarding the summary paper to your mentee student (cc supervisor).
• July 4: Send revised summary paper to your mentor student (cc supervisor).

• July 16 (16:15--17:45): Meeting with everyone: Hints on how to give a good presentation. You can download the slides here.

• July 11: Send feedback regarding the revised summary paper to your mentee student (cc supervisor).

• July 18: Send presentation slides to mentor student (cc supervisor).
• July 25: Send feedback regarding the presentation slides to your mentee student (cc supervisor).
• August 1: Send revised presentation slides to mentor student (cc supervisor).
• August 8: Send feedback regarding the revised presentation slides to your mentee student (cc supervisor).

• August 10: Send summary paper and presentation slides by email to Ellen Wintringer.
• August 11: Give a presentation (25 minutes talk, plus 15 minutes discussion) in the block seminar. Attendance to all talks is required. For each talk we will build a panel of three "discussants" (including your mentee and mentor students): Each of them is supposed to ask at least one non-trivial question.

Topics. Each participant will be assigned one topic, each of which may consists of either a single paper, a part of a single paper, or up to two papers. The overall level of difficulty of the material associated with each topic is roughly balanced. The topics are distributed across the three different areas mentioned above.

Area 1: Performance prediction and portfolio configuration. (Supervisor: Peter Kissmann)

1. Learning from planner performance: The pioneering investigation on predicting planner performance based on simple features (published 2009), and the most recent work on prediction (published 2014). Paper: Area 1 Topic 1a (excluding sections 4.3, 4.4 and 6); Area 1 Topic 1b.
2. Prediction and portfolio configuration: Intermediate work on prediction (published 2012; this lies "in between" the two Area 1 Topic 1 papers, so these two topics will interact tightly), and a follow-up by the same authors on using prediction for automatic portfolio configuration. Papers: Area 1 Topic 2a; Area 1 Topic 2b.
3. Portfolio design and analysis: The simple portfolio design mechanisms leading to the winner of the most recent international planning competition; and an alternative design approach and analysis using optimization. Papers: Area 1 Topic 3a; Area 1 Topic 3b.

Area 2: Learning and improving heuristic functions. (Supervisor: Alvaro Torralba)

4. Learning heuristic functions by bootstrapping: How to learn distance estimators, with no prior input, by starting with small examples and incrementally going to larger ones. Paper: Area 2 Topic 1 (excluding section 4).
5. Learning heuristic functions from others: How to learn a better overall estimator when given a set of estimators as input. Papers: Area 2 Topic 2a; Area 2 Topic 2b.
6. Learning to improve a heuristic: How to learn the difference between the delete-relaxed plan distance estimate, and the real distance. Paper: Area 2 Topic 3.

Area 3: Learning policies. (Supervisor: Jörg Hoffmann)

7. Learning a strategy of action per-domain: How to learn a list of decision rules for action selection. Paper: Area 3 Topic 1 (excluding sections 4 and 6 as well as the appendix).
8. Learning weighted rule sets for greedy search: Related to the previous topic, but in a more refined version prioritizing the rules and using them in a search. Paper: Area 3 Topic 2. As additional background, an earlier simpler version is relevant: Area 3 Topic 2 Background.