Foundations of Artificial Intelligence (FAI) Group

Core Lecture: Artificial Intelligence

Organization. Lecturers: Prof. Joerg Hoffmann, and Prof. Wolfgang Wahlster.

The tutorials will be supervised by Dr. Alvaro Torralba, Daniel Gnad, and Marcel Steinmetz.

Successful participation in the course yields 9 ECTS. The course consists of oral lectures, 2*90 minutes per week, as well as exercises that will be supervised in tutorial groups (90 minutes/week). All lectures and tutorials will be held in English.

The lectures will normally be held Mondays 10:15--11:45 and Tuesdays 16:15--17:45, in Guenter-Hotz-Hoersaal (E2 2). There will be some exceptions, in day of the week, time, and/or place. Exact details will be made available in the "Course Calendar" below.

Registration for the course/exams is in HISPOS as usual (contact Evelyn Kraska in case of questions). Registration for the tutorials is via our Moodle pages (next item). For details regarding tutorials registration (and other organizational aspects of the course), see the "About this Course" slide handouts (Chapter 0) below. If you have difficulties registering, or wish to change tutorials, please send an email Alvaro Torralba. Note that, for changing the tutorial group, you need a switching partner. You can try to find a switching partner via the "Technical Discussion" forum on the Moodle pages.

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 the FAI 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.)

For all organizational questions that go beyond the information on this page, PLEASE SEE THE ABOUT THIS COURSE SLIDES.

Abstract. This course explores key concepts of Artificial Intelligence (AI), including formal knowledge representation, automated deduction, heuristic search algorithms, the automatic generation of heuristic functions in planning, reasoning under uncertainty, rule-based systems, and description logics. We will highlight how these concepts are used in several AI application-fields like spoken-dialog systems, expert systems, and intelligent network security. Upon completion of the course, students should be able to write Bachelor and Master theses in AI. This core lecture is also the prerequisite for advanced courses such as Automatic Planning, Intelligent User Interfaces, and Semantic Web. Interested students will have the unique opportunity to participate in exciting AI research projects at DFKI or in the FAI group.

Exercises. There will be two kinds of exercises, paper exercises and practical exercises. Each of the two kinds of exercises will be counted separately; each will have a seprate series of exercise sheets, and will have a separate maximum amount of points possible. To qualify for the exam, you need to obtain at least 50 points in each, paper exercises and practical exercises. (If you want to participate in future editions of this course, then you need to qualify anew; see also below.)

The paper exercises will involve applying the introduced concepts and algorithms to examples, and leading simple proofs. The paper exercise sheets will be handed out and submitted in a weekly cycle, i.e., in 1-week intervals. The submission deadline will be stated on each sheet.

The practical exercises will consist of experimentation with off-the-shelf AI problem-solving formalisms and tools. On each exercise sheet, you will be given an example problem, and will be asked to model that problem in a particular formalism, and solve your model with an off-the-shelf tool. Your model will be checked manually by the tutors, and will be graded based on its correctness. The practical exercise sheets will be handed out and submitted in 1-week or 2-week intervals depending on the topic and lecture timing. The submission deadline will be stated on each sheet.

Students can form groups of up to 3 authors for the exercise solutions. All group members must be registered into the same tutorial group. Every student can be member of at most one group, i.e., the same group must address both the paper and the practical exercises.

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 the exam. A re-exam will be held beginning October.

Each exam counts as a separate attempt to pass the course. The re-exam is an opportunity to improve your grade, i.e., the better one of the two grades will count for your curriculum.

ATTENTION! The re-exam is your only chance to improve your grade.

• By the study regulations, if you do not pass this edition of the course (if you fail, or are absent, in both the exam and the re-exam), then you can participate in future editions of the course as additional attempts to pass the course. (In this case, you need to qualify anew for the exam of that edition of the course.)
• If you do pass this edition of the course, then you cannot improve your grade anymore. The only exception to the latter is if you already pass the exam: in that case, you can try to improve your grade in the re-exam.

Course Material. For most lectures, there will be 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.

Most of the course follows the standard AI text book by Russel and Norvig (RN). HOWEVER, several chapters do NOT follow that book one-to-one, and some do not follow it at all. The ground truth throughout the course are the results as stated in the post-handouts. A few details about the relevant chapters of RN are given in the table at the end of this page, as well as at the end of each topic in the post-handouts.

Course Calendar. The Lecture slots and contents are overviewed below.

ATTENTION: Lecture slots displayed in red in this table deviate from the regular lecture days/times.