Knowledge Representation


Course Objective

-To get acquainted with the broad principles of knowledge
representation, such as the separation of representation and reasoning,
the declarative nature of representations, the universal (domain
independent) nature of inference mechanisms. (Knowledge and
-To get practical experience with two different representation
formalisms. (Apply knowledge and understanding)
-To have some knowledge of the applications of knowledge representation.
(Knowledge and understanding)
-To understand the role of knowledge representation in the broader
context of AI. (Apply knowledge and understanding) (Make judgements)

Course Content

We discuss 4 typical forms of symbolic Knowledge Representation for AI:
propositional logic (SAT solvers), Description Logic, Constraint
Solving, and Qualitative Reasoning. Together, these are a typical sample
of different knowledge representation techniques.

Teaching Methods

Lecture series, plus two programming assignments

Method of Assessment

-two programming assignments (2 x 25%),
-peer reviewing assignment reports (P/F)
- written exam (50%).
The peer reviewing assignment report is marked pass/fail only, but must
be completed to finish the course.


Chapters from the Handbook of Knowledge Representation will be provided

Target Audience

Master students Artificial Intelligence

Recommended background knowledge

Basic knowledge of logic (propositional logic and first-order logic)

General Information

Course Code XM_0059
Credits 6 EC
Period P1
Course Level 400
Language of Tuition English
Faculty Faculty of Science
Course Coordinator prof. dr. F.A.H. van Harmelen
Examiner prof. dr. F.A.H. van Harmelen
Teaching Staff prof. dr. F.A.H. van Harmelen

Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods Lecture, Practical
Target audiences

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