After successful completion of the course, students are able to

• to name and explain different logics and logic-based formalisms for knowledge representation,
• to select suitable methods and techniques for a given problem, and
• to critically assess relevant solutions and formalisms.

Knowledge representation is a part of AI dealing with suitable methods to make knowledge about a certain domain accessible to computers. The area of knowledge representation comprises techniques from different disciplines, in particular from logic and ontology.

In this course we deal with different logics for knowledge representation.

Content:

1. Introduction
• Overview
• Logic
2. Knowledge Representation using Classical Logic
• Elements of Propositional Logic
• Propositional Logic for Modelling Natural-Language Arguments
• Elements of Predicate Logic
• Predicate Logic and Natural Language
• Ontological Aspects
3. Nonmonotonic Reasoning
• Problems of Classical Logic
• Methods of Nonmonotonic Reasoning
• Circumscription
• Default Logic
4. Parakonsistent Reasoning
5. Modallogic
• Introduction
• Syntax and Semantics
• Basic Modallogics

For details on the the course, see the German version of this site.

Attention:

• On October 3, the lecture will be held 18:00-20:00!
• No lecture on November 7!

Oral end exam plus evaluation of the exercises.