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 ContentWe 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 MethodsLecture 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.
LiteratureChapters from the Handbook of Knowledge Representation will be provided
Target AudienceMaster students Artificial Intelligence
Recommended background knowledgeBasic knowledge of logic (propositional logic and first-order logic)
|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|
prof. dr. F.A.H. van Harmelen
You need to register for this course yourself
Last-minute registration is available for this course.
|Teaching Methods||Lecture, Practical|
This course is also available as: