Foundations of Artificial Intelligence (FAI) Group
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Core Lecture: Artificial Intelligence


Organization. Lecturers: Prof. Jana Köhler and Dr. Álvaro Torralba.

The Artificial Intelligence course will take place on Summer 2019. All the data provided in this webpage is subject to change, further details about the course will be given in due time during the winter break.


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 first paper exercise sheet is handed out on May 1st.

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 2-week intervals. The submission deadline will be stated on each sheet. The first practical exercise sheet is handed out on May 8th.

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.


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.