Cognition and Representation
What is CR?
The term 'representation' usually refers to formalization, including
a deductive or inductive use of formalized knowledge. This view, maintained
by computer science, is opposed to the interpretation of knowledge by
cognitive theory, according to which it is an expression of thoughts
by a human observer. In this course we learn how these two conceptions
of knowledge, formal and meaningful, can be linked with one another.
We raise the questions:
What are the rules of meaningful representation?
How can ontologies be specified systematically?
In order to answer these questions we introduce a knowledge representation
on the basis of a theory of cognitive activity and a theory of signification.
In addition we learn how this representation can be uniformly used for
modeling knowledge in different domains such as logic, reasoning, natural
language and mathematics. We illustrate the potential of our uniform
knowledge representation with an application in meaningful text summarization.
What can you learn in this course?
- What are signs and what can be signified by means of signs?
- How problems can be interpreted as signs?
- How information processing in logic, mathematics and natural language
can be modeled as a sign recognition process?
- What is the formal complexity of the proposed representation?
Form of lecturing
Lectures and classroom exercises
Weekly assignments, submitted via a drop-box on Blackboard.
Deadline of assignments: 5pm on the day preceding
the subsequent next class.
Information over grades: via Blackboard.
The final mark is an average of
the grade for the midterm test (not lower than 5.0)
the grade for the final exam (not lower than 5.0).
This value can be positively adjusted by average of the grades
for the weekly assignments.
Lecture notes and assignments will be made available via Blackboard.
For the research project providing the theory for this course see