Foundations of Artificial Intelligence (FAI) Group
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Spezialized Lecture: Automatic Planning

Organization. The course consists of oral lectures accompanied by programming exercises. Successful participation in the course yields 9 ECTS.

The course has two weekly slots of 90 minutes each, Mondays 10:15 -- 11:45, and Tuesdays 10:15 -- 11:45. Every second week, the Tuesday slot is used for the tutorial. All lectures take place in HS003, Building E1 3. (One exceptional lecture slot will be on Friday, December 18; see course overview below). The tutorials will take place in Building E1 1, room 3.06.

The lectures will be given by Prof. Dr. Joerg Hoffmann and Dr. Alvaro Torralba. The tutorials will be given by Daniel Gnad and Marcel Steinmetz. All lectures and tutorials will be held in English.

The lecture slides will be made available for download here, i.e., on this web page. By contrast, all exercises material and all interaction -- registration, announcements, technical discussions -- will be available and run through our Moodle pages. Apart from the lecture slide publication, this web page here will remain fixed throughout the course. (For the curious amongst you: the lecture slides will be made available here, as opposed to the Moodle pages, so that people from outside Saarland university can access them as well.)

In case you wonder: Yes, the course is very similar (although not identical) to last winter's Automatic Planning course.

Abstract. 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. 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, consisting of 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. The course consists mostly of research results from the last decade, and is very close to the current research frontier in planning.

Prerequisites. Participants should have successfully completed an introductory course in Artificial Intelligence, and should be familiar specifically with the basics of Search (the A* algorithm etc) as well as the basics of logics (propositional formulas etc).

Exercises and ECTS. The course will be accompanied by programming exercises, that involve implementing (simple versions of) some of the techniques discussed, starting from a baseline (implemented in C++) we will provide.

In other words, you will build your own planning system as part of the course! We'll run a competition amongst these systems at the end of term.

We expect this to be interesting and fun for the students, and in light of the work involved in programming we'll allow groups of 3 and give 9 ECTS.

The tutorial sessions will consist of two parts, (1) a classical tutorial discussing the solutions to the already solved exercises, (2) a programming workshop where students can get feedback on the programming exercises they are currently working on.

Exam and final grade. There will be a written exam at the end of the course. The final grade will be determined based on the performance in that exam, and the performance in the exercises.

Precisely, each of the exercises and the exam will yield a maximum of 100 points. 50 points from the exercises are needed for admission to the exam. The final overall points will be determined from either the exam points alone, or from the average of exercise and exam points (0.5*exercises+0.5*exam), whichever one is better. At least 50 overall points are needed to pass the course.

Depending on the outcome of the 1st exam, there may be a 2nd exam in the first half of April. If so, then, in compliance with the new study regulations each of the two exams will count as a separate attempt to pass the course. In particular, the grading rule for each exam (separately) will be as just explained.

Course Material. Due to the recency of the material covered, there exists no text book for this course. There are two kinds of slides, pre-handouts and post-handouts. Pre-handouts do not contain the answers to questions asked during the lecture sessions, and do not contain the details for examples worked during the lecture sessions. The post-handouts do contain all this, and correct any bugs. The pre-handouts are made available one day before the lecture sessions on each chapter, the post-handouts are made available directly after the lecture sessions on a chapter are finished.

Tentative Course Overview.

Date Lecturer Chapter(s) / Tutorials
Mon, 19.10.15 Hoffmann About this Course
Tue, 20.10.15 Hoffmann Planning Formalisms
Mon, 26.10.15 Hoffmann PDDL; Applications
Tue, 27.10.15 Hoffmann Causal Graphs; Progression and Regression
Mon, 02.11.15 Hoffmann Progression and Regression; Heuristic Search
Tue, 03.11.15 Hoffmann Heuristic Search; Critical Path Heuristics
Mon, 09.11.15 Hoffmann Delete Relaxation Heuristics
Tue, 11.11.15 Tutorial 1 (PDDL Modeling), Programming Workshop
Mon, 16.11.15 Hoffmann Delete Relaxation Heuristics
Tue, 17.11.15 Hoffmann Partial Delete Relaxation
Mon, 23.11.15 Hoffmann; Torralba Partial Delete Relaxation; Abstractions
Tue, 24.11.15 Tutorial 2 (Simple Heuristics), Programming Workshop
Mon, 30.11.15 Torralba Abstractions; Pattern Database Heuristics
Tue, 01.12.15 Torralba Pattern Database Heuristics
Mon, 07.12.15 Torralba Merge-and-Shrink Heuristics
Tue, 08.12.15 Tutorial 3 (Critical Path Heuristics), Programming Workshop
Mon, 14.12.15 Torralba Symmetry Reduction
Tue, 15.12.15 Torralba Dominance Pruning
Fri, 18.12.15, 12:15--13:45, E1 1 room 3.06 Torralba Christmas Surprise Lecture (there will be PIZZA)
Mon, 11.01.16 Hoffmann Landmark Heuristics
Tue, 12.01.16 Tutorial 4 (Delete Relaxation Heuristics), Programming Workshop
Mon, 18.01.16 Hoffmann; Torralba Landmark Heuristics; Combining Heuristic Functions
Tue, 19.01.16 Torralba Combining Heuristic Functions
Mon, 25.01.16 Torralba Comparing Heuristic Functions
Tue, 26.01.16 Torralba Partial-Order Reduction
Mon, 01.02.16 Hoffmann Planning Systems and the IPC
Tue, 02.02.16 Tutorial 5 (Competition)
Fri, 05.02.16, 12:15--13:45, E2 1 room 001 Hoffmann Exam Preparation
Tue, 16.02.16, 10:00--12:30, E1 3 HS002 Written Exam
Tue, 12.04.16, 12:00--14:30, E1 3 HS003 Re-Exam