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

Communication of the course contents through a traditional frontal lecture.

Attention:

• Details on the organisation of the course and the associated exercises, cf. the German description of this site.

ECTS breakdown: 3 ECTS = 75 hours

• Lecture 24h
• Lecture introduction 0.5h
• Preparation for exam 50h
• Oral exam 0.5h

Oral exam.

Slides will be made available for download.