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


Organization. Lecturers: Prof. Wolfgang Wahlster and Dr. Álvaro Torralba.

The tutorials will be supervised by Dr. Cosmina Croitoru and Daniel Gnad.

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 Tuesdays 16:15--17:45 and Thursdays 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 to Cosmina Croitoru. Note that, for changing the tutorial group, you need a switching partner. You can try to find a switching partner via the "Students 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.)


On our Moodle pages, you can login with your CS department account that you also use to login e.g. to the OwnCloud, GOGS, or possibly other Moodle pages (i.e. your official s8xyabcd username with the new password you got from the CS department). For most students that do not have, or are not aware of having, such an account, it should do to visit this webpage, and request a new password using your UdS eMail address. If you don't get an error message (e.g. "no user found"), you will receive a new password within a few minutes. If you have trouble with the login, contact Daniel Gnad.


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.

The following table will provide the links to the hand-outs as the course progresses:

Chapter Title Dates Pre-Handouts Post-Handouts
0 About this course Tue 10.4 1 on 1;4 on 1
Last change: Tue, 10 Apr 2018
1 on 1;4 on 1
Last change: Tue, 10 Apr 2018
1 Introduction to AI Thu 12.4 1 on 1;4 on 1
Last change: Fri, 13 Apr 2018
2 General Problem Solving Thu 12.4 1 on 1;4 on 1
Last change: Fri, 13 Apr 2018
3 Intelligent Agents Tue 17.4 1 on 1;4 on 1
Last change: Wed, 18 Apr 2018
1 on 1;4 on 1
Last change: Wed, 18 Apr 2018
4 Classical Search, Part I: Basics, and Blind Search Thu 19.4 ( Lecture Hall I in E2 5.);Tue 24.4 1 on 1;4 on 1
Last change: Mon, 23 Apr 2018
1 on 1;4 on 1
Last change: Thu, 26 Apr 2018
5 Classical Search, Part II: Informed Search Tue 24.4;Thu 26.4 1 on 1;4 on 1
Last change: Mon, 23 Apr 2018
1 on 1;4 on 1
Last change: Thu, 26 Apr 2018
6 Adversarial Search Thu 26.4;Thu 3.5;Mon 7.5 10.00 - 12.00 1 on 1;4 on 1
Last change: Sun, 06 May 2018
1 on 1;4 on 1
Last change: Tue, 08 May 2018
7 General Game Playing Mon 7.5 10.00 - 12.00 1 on 1;4 on 1
Last change: Sun, 06 May 2018
1 on 1;4 on 1
Last change: Tue, 08 May 2018
8 Constraint Satisfaction Problems, Part I: Basics, and Naive Search Tue 8.5 1 on 1;4 on 1
Last change: Sun, 06 May 2018
1 on 1;4 on 1
Last change: Tue, 08 May 2018
9 Constraint Satisfaction Problems, Part II: Inference, and Decomposition Methods Tue 8.5;Tue 15.5 1 on 1;4 on 1
Last change: Tue, 15 May 2018
1 on 1;4 on 1
Last change: Wed, 16 May 2018
10 Propositional Reasoning, Part I: Principles Thu 17.5;Tue 22.5 1 on 1;4 on 1
Last change: Wed, 16 May 2018
1 on 1;4 on 1
Last change: Tue, 22 May 2018
11 Propositional Reasoning, Part II: SAT Solvers Tue 22.5;Thu 24.5 1 on 1;4 on 1
Last change: Mon, 21 May 2018
1 on 1;4 on 1
Last change: Thu, 24 May 2018
12 Predicate Logic Reasoning, Part I: Basics Tue 29.5;Wed 30.5 12.00 - 14.00 1 on 1;4 on 1;1 on 1;4 on 1
Last change: Thu, 24 May 2018
1 on 1;4 on 1
Last change: Wed, 30 May 2018
13 Predicate Logic Reasoning, Part II: Reasoning Wed 30.5 12.00 - 14.00;Tue 5.6 1 on 1;4 on 1;1 on 1;4 on 1
Last change: Tue, 29 May 2018
1 on 1;4 on 1
Last change: Tue, 05 Jun 2018
14 Planning, Part I: Framework Thu 7.6;Tue 12.6 1 on 1;4 on 1
Last change: Wed, 06 Jun 2018
1 on 1;4 on 1
Last change: Wed, 13 Jun 2018
15 Planning, Part II: Algorithms Tue 12.6;Thu 14.6;Tue 19.6 1 on 1;4 on 1
Last change: Mon, 11 Jun 2018
1 on 1;4 on 1
Last change: Tue, 19 Jun 2018
16 Non-classical Planning Thu 21.6 1 on 1;4 on 1
Last change: Wed, 27 Jun 2018
17 Knowledge Representation I Tue 26.6 1 on 1
Last change: Wed, 27 Jun 2018
18 Knowledge Representation II Thu 28.6 1 on 1
Last change: Fri, 29 Jun 2018
19 Ontology Web Language Tue 3.7 1 on 1
Last change: Thu, 05 Jul 2018
20 Production Rules Thu 5.7 1 on 1
Last change: Fri, 06 Jul 2018
21 Rete Algorithm, Default Reasoning Tue 10.7 1 on 1
Last change: Wed, 11 Jul 2018
22 Model Based Diagnosis Thu 12.7
23 Exam Preparation (Extra Exercises) Tue 17.7 1 on 1;4 on 1
Last change: Thu, 19 Jul 2018


Course Calendar. The Lecture slots and contents will be overviewed below.

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

Date Place Content Lecturer Material
Tue 10.4 Günter-Hotz About this course Torralba
Thu 12.4 Günter-Hotz Introduction to AI ; General Problem Solving Torralba Russel/Norvig Chapter 1; None
Tue 17.4 Günter-Hotz Intelligent Agents Torralba Russel/Norvig Chapter 2 (Loosely followed)
Thu 19.4 ( Lecture Hall I in E2 5.) Günter-Hotz Classical Search, Part I: Basics, and Blind Search Torralba Russel/Norvig Chapter 3 and parts of Chapter 4 (Loosely followed)
Tue 24.4 Günter-Hotz Classical Search, Part I: Basics, and Blind Search ; Classical Search, Part II: Informed Search Torralba Russel/Norvig Chapter 3 and parts of Chapter 4 (Loosely followed)
Thu 26.4 Günter-Hotz Classical Search, Part II: Informed Search ; Adversarial Search Torralba Russel/Norvig Chapter 3 and parts of Chapter 4 (Loosely followed)
Thu 3.5 Günter-Hotz Adversarial Search Torralba
Mon 7.5 10.00 - 12.00 Günter-Hotz Adversarial Search ; General Game Playing Torralba
Tue 8.5 Günter-Hotz Constraint Satisfaction Problems, Part I: Basics, and Naive Search ; Constraint Satisfaction Problems, Part II: Inference, and Decomposition Methods Torralba Russel/Norvig Chapter 6 (Loosely followed)
Tue 15.5 Günter-Hotz Constraint Satisfaction Problems, Part II: Inference, and Decomposition Methods Torralba Russel/Norvig Chapter 6 (Loosely followed)
Thu 17.5 Günter-Hotz Propositional Reasoning, Part I: Principles Croitoru Russel/Norvig Chapter 7 (Loosely followed)
Tue 22.5 Günter-Hotz Propositional Reasoning, Part I: Principles ; Propositional Reasoning, Part II: SAT Solvers Torralba Russel/Norvig Chapter 7 (Loosely followed)
Thu 24.5 Günter-Hotz Propositional Reasoning, Part II: SAT Solvers Torralba Russel/Norvig Chapter 7 (Loosely followed)
Tue 29.5 Günter-Hotz Predicate Logic Reasoning, Part I: Basics Torralba Russel/Norvig Chapters 8 and 9 (Loosely followed)
Wed 30.5 12.00 - 14.00 Günter-Hotz Predicate Logic Reasoning, Part I: Basics ; Predicate Logic Reasoning, Part II: Reasoning Torralba Russel/Norvig Chapters 8 and 9 (Loosely followed)
Tue 5.6 Günter-Hotz Predicate Logic Reasoning, Part II: Reasoning Torralba Russel/Norvig Chapters 8 and 9 (Loosely followed)
Thu 7.6 Günter-Hotz Planning, Part I: Framework Torralba Does not follow Russel/Norvig (Chapter 10 can serve as general background)
Tue 12.6 Günter-Hotz Planning, Part I: Framework ; Planning, Part II: Algorithms Torralba Does not follow Russel/Norvig (Chapter 10 can serve as general background)
Thu 14.6 Günter-Hotz Planning, Part II: Algorithms Torralba Does not follow Russel/Norvig (Chapter 10 can serve as general background)
Tue 19.6 Günter-Hotz Planning, Part II: Algorithms Torralba Does not follow Russel/Norvig (Chapter 10 can serve as general background)
Thu 21.6 Günter-Hotz Non-classical Planning Torralba
Tue 26.6 Günter-Hotz Knowledge Representation I Wahlster
Thu 28.6 Günter-Hotz Knowledge Representation II Wahlster
Tue 3.7 Günter-Hotz Ontology Web Language Wahlster
Thu 5.7 Günter-Hotz Production Rules Wahlster
Tue 10.7 Günter-Hotz Rete Algorithm, Default Reasoning Wahlster
Thu 12.7 Günter-Hotz Model Based Diagnosis Wahlster
Tue 17.7 Günter-Hotz Exam Preparation (Extra Exercises) Croitoru
Tue 24.07. 10:00--13:00 Guenter Hotz & HS002 Exam
Mon 30.07 14:00--17:00 E1 1, 3.06 Exam Inspection
Tue 9.10 10:00--12:30 Guenter Hotz & HS002 Re-Exam
Fri 12.10. 14:00--16:00 E1 1, 3.06 Exam Inspection