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Einführung in die Künstliche Intelligenz

Vorlesung im SS 2004, Prof. Dr. Dr. h.c. mult. Wolfgang Wahlster



The certificates (Scheine) for the 2nd exam are ready. You can fetch them at the secretary's office.


2nd Exam Grades: Grades of the 2nd exam are online. You can have an insight in your exam on Thursday 11th of November, 1 p.m. to 3 p.m.

Nachklausur: The Nachklausur is on this Thursday, the 14th of October 9:00-11:00 a.m. (sharp!) in Hörsaal I in building 27.2 (Math-Building, Mathebau). The topics are the same as for the main exam.You can download the main exam in german and english. Good luck!


Those who failed in the first exam can participate in a second exam (Nachklausur). The date for the 2nd exam (Nachklausur) is Thursday, the 14th of October 9-11 a.m. (sharp!). Details like room-number will follow soon.

Those who passed the first exam can can fetch their certificate (Schein) at the office of our secretary. You will also get your evaluation code there and we ask you to fill out the web-evaluation-form first, before you get the schein.

You can have an insight in your exam on Thursday, 2nd of September 14-16h (Seminar room, Chair of Prof. Wahlster)/ Klausureinsicht ist am Donnerstag, dem 2. September 14-16 Uhr (Seminarraum, Lehrstuhl Prof. Wahlster)

The grades of the exam are online

Qualification list for the exam

Bayesian Networks: Some people got confused by the question in Exercise 10, Problem 3. The probability which was asked is: P(C|-A ^ -B ^ D ^ E). In order to compute this, you have to compute both values P(-A ^ -B ^ C ^ D ^ E) and P(-A ^ -B ^ -C ^ D ^ E) and normalize the first value.

Min-Max and Alpha-Beta: Hans Becker found a nice page about Min-Max and Alpha-Beta.

Unfortunately there was a mistake on the new exercise sheet (problem 1): The given circuit is in fact correct. The new version of the sheet is in Aktuelle Informationen und Materialien . Sorry for the inconvenience.

The exam is on the 22nd July, 9-11 a.m./ Die Klausur findet am 22. Juli 9-11 Uhr statt.

Important dates:

Missionaries and Cannibals:Alina Kopp found a nice Flash-Game that illustrates the Missionaries and Cannibals puzzle. You can download it here .

Hints for And-Or-Tree's: There where some questions about Problem 3 on the new exercise sheet. Here are some hints.

Additional literature:
We have copies of the papers at our offices. If you want to read and/or copy them, please do so.

Exercises: Please have a look at Aktuelle Informationen und Materialien

Tutorial Groups/Übungsgruppen: Follow the link to see the final schedule line of the tutorial groups.

We encourage you to print out the slides , so you can make notes during the lectures. We will put updated slides here

Anmeldung/Registration: Registration-forms will go round in the first few lectures where you can register for the course and the tutorials. You can also register or de-register in the offices of Christian Müller , Michael Schmitz or Tim Schwartz (Building 36.1, second floor, Chair of Prof. Wahlster).

Consultation hours for registration and questions are:
Christian Müller: Tuesdays 10:45-12:30 a.m.
Michael Schmitz: Thursdays 2-4 p.m.
Tim Schwartz: Fridays 2-4 p.m.

Update: Unfortunately, due to regulations in the conditions of study, the lecture will be held in German. Nonetheless there will be English and German tutorials and the exam will be presented in both languages.

Vorlesung: dienstags und donnerstags von 9-11 Uhr, Gebäude 45, Hörsaal 001, Beginn: 22.04.04

Übungen: 2-stündig nach Vereinbarung; theoretische und praktische Übungen

Übungstermine: montags, 16-18 Uhr (English and German), mittwochs, 16-18 Uhr (English), donnerstags, 11-13 Uhr (German), freitags, 14-16 Uhr (German)

Die vierstündige Stammvorlesung bietet eine Einführung in die grundlegenden Konzepte und Methoden der Künstlichen Intelligenz. Es werden die wichtigsten Prinzipien der Wissensrepräsentation und Wissensverarbeitung behandelt. Neben Produktionensystemen, Semantischen Netzen und Terminologischen Logiken werden heuristische Suchverfahren, Vererbungssysteme, Deduktions- und Abduktionsmethoden, Unsicherheitskalküle sowie Planungsverfahren eingeführt. Außerdem werden die Grundprinzipien des nicht-monotonen Schließens, von induktiven Lernverfahren, der Begründungsverwaltung und der modellbasierten Diagnose erläutert. Es werden jeweils Anwendungsbeispiele aus den Bereichen Expertensysteme, Internet-Agenten, Robotik, Planungssysteme sowie natürlichsprachliche und bildverstehende Systeme behandelt.

Vermittelte Fähigkeiten:

Es wird das notwendige Grundwissen vermittelt, um Fortgeschrittenenpraktika und Diplomarbeiten im Themenbereich Künstliche Intelligenz erfolgreich durchführen zu können. Diese Stammvorlesung ist die Grundlage für Vertiefungsvorlesungen, z. B. zu Expertensystemen, Natürlichsprachlichen Systemen, Intelligenten Benutzerschnittstellen, Internet-Agenten und Deduktiven Datenbanken. Die Übungen umfassen theoretische Aufgaben und praktische Tests am Rechner.

Aktuelle Informationen & Materialien

aktualisiert: 13.12.2004