I00054 (I00054)
Cognitie en representatie*
< 2008/2009 > 08-09-2008 t/m 18-01-2009 () H
6 ec (168 uur) : 30 uur plenair college, 30 uur groepsgewijs college, 0 uur computerpracticum, 0 uur 'droog' practicum, 4 uur gesprekken met de docent, 0 uur onderling overleg met medestudenten (werkgroepen, projectwerk e.d.), 104 uur zelfstudie
6 ec * 28 u/ec + #std * (1 + 6ec * 0.15 u/student/ec)


dr. Janos Sarbo

speciale web-site


    In computer science the term`representation' refers to formalization and we learn how formalized knowledge can be generated from knowledge that we already have. This is opposed to cognitive theory, in which the term `knowledge' is usually associated with perceptual judgments (observations) and thoughts (generated through reasoning), which are meaningful.

    In this course we learn (i) how these roughly complementary concepts of knowledge can be linked with one another and (ii) with a computational interpretation of knowledge. The focus is on a processual understanding of knowledge, how knowledge arises from the observation of phenomena through cognitive activity. An example of such a process is the process of problem understanding or problem specification, insofar as problems too appear as phenomena.

    The fundamental question raised by this course is this: How can `real' world phenomena be specified systematically? To this end we introduce a representation on the basis of an analysis of the properties of cognitive activity and the properties of signs. In addition, we learn how such a representation can be used for uniformly modeling knowledge in different domains such as `naive' logic and reasoning, natural language, and `naive' mathematics (see also http://www.cs.ru.nl/~janos/CR.html).


    De cursusbeschrijving vind je hier.


    • What are signs and what can be signified by means of signs?
    • What are primitive signs and how can they contribute to more complex signs?
    • How can problems or phenomena be the subject of observation and interpreted as signs?
    • How can the symbols of (`naive') logic, mathematics, and natural language be modeled as a sign recognition process?
    • How can such a model of natural language be used for the generation of meaningful summaries?
    • What is the formal complexity of the proposed uniform representation?


    Lecture and classroom exercises

    Vereiste voorkennis

    propositional logic


    Midterm test (min. 6.0) and final exam.


    Lecture notes

Evaluatie: studentenquêtes www.cs.ru.ml/~janos/CR.html; docentevaluatie
Rendement: 15 begonnen, 12 echt meegedaan, 10 geslaagd met 1e kans, 12 geslaagd totaal