Foundations of Artificial Intelligence (FAI) Group
Seminar: Explainable AI Planning
Basics. Seminar, 7 graded ECTS points.
The seminar will be run in a block format. There will be an initial meeting on Wednesday, October 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.
Supervisors for the seminar are Prof. Jörg Hoffmann, and Daniel Höller.
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 "[XAIP19-20]" 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. Model-based approaches to AI are well suited to explainability in principle, given the explicit nature of their world knowledge and of the reasoning performed to take decisions. AI Planning in particular is relevant in this context as a generic approach to action-decision problems. Indeed, explainable AI Planning (XAIP) has received interest since more than a decade, and has been taking up speed recently along with the general trend to explainable AI. This seminar provides an overview of the area, covering its major work lines pertaining to contrastive explanation, model reconciliation, hierarchical planning with user interaction, and the explanation of unsolvability.
Prerequisites. Participants must have successfully completed an introductory course in Artificial Intelligence. They should be familiar with automatic planning at least to the extent of the material covered in the Artificial Intelligence course; successful participation in one of our AI Planning courses will be an advantage, but is not absolutely necessary to follow the seminar.
Registration. Is via the central seminar registration system.
Grading. The final grading will be based, in this order of importance, on:
Summary Paper. For the summary paper, you must use this tex template. You are required to read at least 2 related papers, for the related work section. You are allowed to modify the section structure given in the template if, for whatever reason, this is more adequate for the work you are summarizing.
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 (tentative!).
Note that each topic is associated with 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). The mentee/mentor assignment will be a "cycle" through each of the four topic areas as listed below: within areas with 2 topics, the two students mentor each other; within areas with 3 topics, the student with topic 1 mentors the student with topic 2, who mentors the student with topic 3, who mentors the student with topic 1. If you want to team up with someone specific, please do state that in your email.
Read the material associated with your topic carefully, and prepare an initial version of your summary paper, using the tex template given above.
NOTE: The following deadlines marked with "(ca.)" are meant as a guideline. You are required to do these things, but if you do them 3-4 days earlier or later, that is no problem.
Topics. Each participant will be assigned one topic, each of which consists of one paper. The overall amount and difficulty of the material associated with each topic is roughly balanced.
Area 1: Contrastive Explanation. Supervisor: Jörg Hoffmann
Area 2: Model Reconciliation. Supervisor: Jörg Hoffmann
Area 3: Explaining Unsolvability. Supervisor: Jörg Hoffmann
Area 4: Explainability in Hierarchical Planning. Supervisor: Daniel Höller