Exact Sciences Master Guide2005/2006


Organisation Dynamics and Self Organisation



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Organisation Dynamics and Self Organisation

  1. Programme


Future perspective:

After completion of this Master programme, the student



  • has an overview of the literature an practice in the area of organisation dynamics and self organisation

  • has mastered methods and techniques for modelling various types of organisations and their dynamics

  • is capable of constructing models of dynamic organisations with which can be simulated and experimented

  • is capable of conducting application-directed AI research in combination with other fields of research.

Students of this programme can function in industry through a variety of different often management-related professions, within a diversity of institutions and companies, for example, in strategic management and organisation advising.

 

Audience:

Students with an interest in analysing, modelling, simulating and experimenting with dynamics properties.

 

Objectives and final attainment level:

In this multi-disciplinary programme, elements from biology, social sciences, economics and computer science are integrated.

By continuously generating new and diverse variants, organisms of increasingly large complexity are realised during biological evolution. Methods have been developed in AI (evolutionary methods, genetic algorithms), with which these evolution processes can be simulated. In nature, evolution leads to organisms that have such adapted to their environments that they can survive; in AI, evolution leads to acceptable solutions for problems. These methods turn out to be well applicable for the discovery of structure in large quantities of data (datamining).

In biological populations, the interaction between organisms can also be further analysed, determining largely how successfully an organism can function; this is the subject of ecology and population dynamics.

Concerning social sciences, models have been developed with which dynamic organisations can be described and simulated. Such methods are used in organisation theory to analyse large organisation and analyse and support processes of change within such organisations.

In economics, simulation models are being developed in the area of market mechanisms, in which the behaviour of individuals leads to patterns in the economics as a whole.

 
The student graduated in this Programme:



  • has a good overview of the contemporary literature and practice in the area of self organising systems

  • has mastered methods and techniques for the modelling of dynamic organisation forms

  • is capable of, in cooperation with people from related scientific disciplines, modelling, simulating, and experimenting with dynamic organisation forms

  • is capable of doing applied AI research in cooperation with other scientific disciplines.
      1. Programme for students having a Bachelor in AI

First year


Students must follow optional courses for a total of 30 cp.


Course code

Course name

Cr.

Period

60422070

Advanced Methods for Applied Economic Research

6

1

400111

Evolutionary Computing

6

1

470063

Evolutiebiologie

6

13.03.2006 -07.04.2006

400188

Organisational Dynamics

6

3

703000

Organisatietheorie

6

5e



Second year


Students must follow optional courses for a total of 18 cp.


Course code

Course name

Cr.

Period

400125

Knowledge Management and Modeling

6

1 and 2

400113

Behaviour Dynamics

6

1 and 2

400285

Master Project Artificial Intelligence

30

4, 5 and 6



Recommended optional courses





Course code

Course name

Cr.

Period

400290

Qualitative Research Methods for the Information Sciences

3

3

400108

Data Mining Techniques

6

4 and 5

150005

Inleiding wijsgerige antropologie

6

5

470503

Dynamic Energy Budgets

6

08.02.2007 - 19.04.2007

470053

Evolutionaire genetica

6

28.11.2005 - 23.12.2005



      1. Programme for students having a Bachelor in Biology or Medical Biology

First year


Students must follow optional courses for a total of 2 cp.


Course code

Course name

Cr.

Period

400054

Design of Multi-Agent Systems

6

1 and 2

400132

Neural Networks

6

1 and 2

400125

Knowledge Management and Modeling

6

1 and 2

400090

Zelforganiserende systemen

5

1 en 2

400150

Inleiding programmeren I

4

1 en 2

400188

Organisational Dynamics

6

3

400126

Kennissystemen

4

4

400151

Inleiding programmeren II

3

4

400270

Programmeren in Prolog

4

5

400381

Project Knowledge Systems

6

5 and 6

703000

Organisatietheorie

6

5e



Second year


Students must follow optional courses for a total of 6 cp.


Course code

Course name

Cr.

Period

400111

Evolutionary Computing

6

1

400154

Machine Learning

6

1 and 2

400113

Behaviour Dynamics

6

1 and 2

400108

Data Mining Techniques

6

4 and 5

400285

Master Project Artificial Intelligence

30

4, 5 and 6



Recommended optional courses





Course code

Course name

Cr.

Period

400290

Qualitative Research Methods for the Information Sciences

3

3

400108

Data Mining Techniques

6

4 and 5

150005

Inleiding wijsgerige antropologie

6

5

470503

Dynamic Energy Budgets

6

08.02.2007 - 19.04.2007

470053

Evolutionaire genetica

6

28.11.2005 - 23.12.2005



    1. Technical Artificial Intelligence


Programme for students having a Bachlor in Computer Science
      1. Programme


Future perspective:

The graduate student of this Master programme is capable of applying techniques from Computer Science to problems of an Artificial Intelligence nature, e.g., designing knowledge-based systems or multi-agent systems. This technically adapt graduate, furthermore, has learned proven AI techniques in the areas of machine learning, neural networks, knowledge representation, and evolutionary computing. Graduate students are well equiped for work in companies that create intelligent applications.

 

Audience:

The programme aims at students with a Bachelor in Computer Science, having an interest in Artificial Intelligence.

 

Objectives and final attainment level:

The objective of the programme is to endow the student with the developments on the area of artificial intelligence and computer science, and to prepare for a profession as researcher or expert in a company. The curriculum must give a good preparation on PhD research. It is expected from the graduate to be critical in a scientific manner, to be aware of societal aspects of information technology and of the creation of intelligent systems and to possess knowledge, capabilities and insights related to:



  • knowledge acquisition and knowledge modelling

  • methods of designing AI systems, specifically based on intelligent agents

  • techniques for search and optimalization

  • adaptive applications

  • the contemporary literature on adaptive applications and agent-based systems

and the graduate is expected to be capable of

  • working individually as well as in teams

  • reporting verbally and written professionally on his/her work

  • consulting specialist literature.
      1. First year





Course code

Course name

Cr.

Period

400111

Evolutionary Computing

6

1

400054

Design of Multi-Agent Systems

6

1 and 2

400132

Neural Networks

6

1 and 2

400154

Machine Learning

6

1 and 2

400059

Design of Multi-Agent Systems Practical

7

3 en 4

400083

Web-gebaseerde kennisrepresentatie

6

4 en 5

400381

Project Knowledge Systems

6

5 and 6

400123

Facetten van de AI

8

5 en 6



      1. Second year


Students must follow optional courses for a total of 19 cp.


Course code

Course name

Cr.

Period

400152

Intelligent Interactive Distributed Systems

8

2 and 3

400285

Master Project Artificial Intelligence

30

4, 5 and 6

811012

Inleiding in de Psychologie voor AI

4






      1. Recommend optional courses


The course Dutch for foreigners is also an option.


Course code

Course name

Cr.

Period

400232

Dutch as a second language II

3




400125

Knowledge Management and Modeling

6

1 and 2

400389

Automated Reasoning in AI

6

2

811003

Functieleer (Experimental psychology)

6

2 en 3

400188

Organisational Dynamics

6

3

400108

Data Mining Techniques

6

4 and 5





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