I00054 (I00054)
Cognitie en representatie*
< 2006/2007 > 04-09-2006 t/m 13-01-2007 () H
Informatica - Master variant C (2003) Thematische specialisatie Informatiesystemen (6 ec) Kunstmatige Intelligentie (6 ec) Keuze informatica (6 ec)
Informatica - Master variant E (2003) Keuze informatica (6 ec)
Informatica - Master variant MT (2003) Thematische specialisatie Kunstmatige Intelligentie (6 ec) Informatiesystemen (6 ec) Kunstmatige Intelligentie (6 ec) Informatiesystemen (6 ec) Keuze informatica (6 ec) (6 ec) (6 ec)
Informatica - Master variant O (2003) Thematische specialisatie Informatiesystemen (6 ec) Kunstmatige Intelligentie (6 ec) Keuze informatica (6 ec)
Informatica - Master variant O (2005) Thematische specialisatie Kunstmatige Intelligentie (6 ec) Informatiesystemen (6 ec) Keuze informatica (6 ec)
Informatica - Master na HBO Artificial Intelligence variant MT (2004) Keuze informatica (6 ec)
Informatica - Master na HBO Artificial Intelligence variant O (2004) Thematische specialisatie Kunstmatige Intelligentie (6 ec) Keuze informatica (6 ec)
Informatica - Master na HBO Computer Security variant MT (2003) Keuze informatica (6 ec)
Informatica - Master na HBO Computer Security variant O (2004) Keuze informatica (6 ec)
Informatica - Master na HBO Embedded Systems variant MT (2003) Keuze informatica (6 ec)
Informatica - Master na HBO Embedded Systems variant O (2004) Keuze informatica (6 ec)
Informatica - Master na HBO Information Systems variant MT (2003) Thematische specialisatie (6 ec)
Informatica - Master na HBO Information Systems variant O (2004) Thematische specialisatie (6 ec) Keuze informatica (6 ec)
Informatica - Master na HBO Software Construction variant MT (2003) Keuze informatica (6 ec)
Informatica - Master na HBO Software Construction variant O (2004) Keuze informatica (6 ec)
Informatiekunde - Master (2004) keuzeruimte (6 ec)
omvang
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
investering
6 ec * 28 u/ec + #std * (1 + 6ec * 0.15 u/student/ec)
inzet tentatief

examinator
afdeling
tijdbesteding

dr. Janos Sarbo
das
215u.

speciale web-site
http://www.cs.ru.nl/~janos/CR.html

 

    In computer science, the term`representation' corresponds to formalisation, and we learn how formalized knowledge be generated from knowledge that we already have. In cognitive theory, however, the term `knowledge' or `cognition' is usually associated with

    1. thoughts that are a result of reasoning
    2. observations that are obtained through experience

    which are intensional hence not formal. In this course we learn (i) how these roughly complementary concepts of knowledge can be linked with one another as well as (ii) with the interpretation of `knowledge', as a formal computation. In this course the focus is on the process how knowledge arises through the observation of phenomena. An example of such a process is the studying of problems (which 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 uniform representation of knowledge on the basis of an analysis of the properties of cognition and the processing of signs. Additionally we learn how such a representation can be used for modeling knowledge in the different domains of knowledge like `naive' logic (propositional logic and reasoning), natural language (morphology, syntax and semantics) and `naive' mathematics.

    Leerdoelen

    • In computer science, in particular, the term `representation' corresponds to formalisation. We learn how such formalized knowledge can arise from knowledge that is experienced.
    • In this course we focus on the second understanding of knowledge. We make an attempt to answer the question: What is there in the `real' world, and how can it be systematically specified?
    • We introduce a formal method which based on a theory of signs (semiotics), and learn how that method can be used for modeling knowledge in various domains like logic (for example, propositional logic, reasoning), and natural language (for example, morphology, syntax, semantics).
    • Onderwerpen

      • What are signs, and what differences (or aspects) can be signified by signs?
      • How primitive signs arise, and how from such signs more complex signs can be generated?
      • Can problems, akin to phenomena, be the subject of our observation and specified by means of signs?
      • How can such a framework be applied to logic and natural language?
      • If sentences are signs, how such signs can be used to generate the summarized meaning of a text?
      • What is the relation between signs, which are `real' concepts, and `formal' concepts, introduced by Formal Concept Analysis?

      Werkvormen

      Lecture and classroom exercises

      Vereiste voorkennis

      propositional logic

      Literatuur

      Lecture notes


Evaluatie: studentenquêtes http://www.cs.ru.nl/~janos/CR.html; docentevaluatie
Rendement: 25 begonnen, 18 echt meegedaan, 11 geslaagd met 1e kans, 18 geslaagd totaal
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