Module CS4522-KP12

Duration

2 Semester

Turnus of offer

starts every summer semester

Credit points

12

Course of studies, specific fields and terms:

• Master Computer Science 2019, optional subject, advanced module

Classes and lectures:

• Knowledge Representation and Common Sense Reasoning (exercise, 1 SWS)
• Lab course Common Sense Reasoning and Natural Language Understanding (practical course, 3 SWS)
• Reasoning in Natural Language Understanding (lecture, 2 SWS)
• Knowledge Representation and Common Sense Reasoning (lecture, 3 SWS)

Workload:

• 95 hours private studies
• 135 hours in-classroom work
• 90 hours group work
• 40 hours exam preparation

Contents of teaching:

• This module provides an introduction to the area of knowledge representation, a sub-discipline of computer science in general and artificial intelligence in particular. Knowledge representation and reasoning enable intelligent agents to act in situations not encountered before and solve new problems. The focus of this module are techniques to represent and exploit common sense knowledge. As main area of application, we consider natural language understanding that hinges on the ability to reason about context and background information.
• fundamental concepts: knowledge, abstraction, reasoning, uncertainty, context
• reasoning techniques (analogical, deductive, inductive)
• qualitative algebras and calculi
• constraint-based reasoning
• qualitative reasoning
• spatial logics
• complexity of reasoning

Qualification-goals/Competencies:

• The students can describe fundamental concepts of knowledge representation and common sense reasoning.
• They can overview formalisms for representing everyday knowledge.
• They can apply knowledge representation and reasoning techniques to problems of natural language understanding.
• They can implement and evaluate approaches to common sense reasoning.
• They can analyse and explain challenges in common sense reasoning and natural language understanding.
• They can identify computational properties of common sense reasoning problems.
• They can present scientific results.
• They can implement reasoning techniques for common sense reasoning tasks.
• They can implement selected approaches to natural language understanding.

Grading through:

• Oral examination

Responsible for this module:

• Prof. Dr. Diedrich Wolter

Teacher:

• Institute of Software Technology and Programming Languages
• Prof. Dr. Diedrich Wolter

Literature:

• Gary Marcus, Ernest Davis : Rebooting AI: Building Artificial Intelligence We Can Trust Pantheon 2019
• Kenneth D. Forbus : Qualitative Representations: How People Reason and Learn about the Continuous World MIT Press 2019
• Ronald Brachman, Hector Levesque : Knowledge Representation and Reasoning Elsevier 2004
• Frank van Harmelen, Vladimir Lifschitz, Bruce Porter (editors) : Handbook of Knowledge Representation Elsevier 2007
• Ernest Davis : Benchmarks for Automated Commonsense Reasoning: A Survey February 2023. ACM Computing Surveys, DOI 10.1145/3615355, 2007
• Frank Dylla, Jae Hee Lee, Till Mossakowski, Thomas Schneider, André Van Delden, Jasper Van De Ven, Diedrich Wolter : A Survey of Qualitative Spatial and Temporal Calculi: Algebraic and Computational Properties ACM Computing Surveys, 50:1, Article 7, DOI 10.1145/3038927, 2017
• James Allen : Natural Language Understanding Addison Wesley 1995
• Alexander Clark, Chris Fox, Shalom Lappin (editors) : The Handbook of Computational Linguistics and Natural Language Processing Wiley 2010
• Ernest Davis : Benchmarks for Automated Commonsense Reasoning: A Survey February 2023. ACM Computing Surveys, DOI 10.1145/3615355, 2007
• Nitin Indurkhya, Fred J. Damerau (editors) : Handbook of Natural Language Processing Routledge 2010
• Gerhard Paaß, Sven Giesselbach : Foundation Models for Natural Language Processing, Pre-trained Language Models Integrating Media, Springer 2023

Language:

• English, except in case of only German-speaking participants

Notes:

Last Updated:

02.03.2026