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Intelligent Systems

General question : What is intelligence ?

Connectionistic Intelligence

Introduction : From single neurons to brain structures

Learning in biological systems

Modelling of artificial neurons and artificial neural networks

General features and training of such networks

Realisations : Neural gates, perceptrons, linear associators, recurrent networks, and self organizing feature maps

Current trends : Spiking neural networks

Adaptive Intelligence

Introduction : Genes and evolution

Basics : Variation and selection

Realisation and applications of genetic algorithms

Theoretical foundations : Schemata and building blocks

Current trends : Population structures, migration and diffusion models

Cognitive Intelligence

Introduction : Cognitve psychology and the symbol system hypotheses

Logic systems : from assertations to predicates

Reasoning with STRIPS

Concepts : Production systems, search in state spaces, and heuristics

Representations : Frames, scripts, and semantic networks

Realisation : Expert systems

Situated Intelligence

Discussion of embodied intelligence and autonomous agents

Roboter programming :

Braitenberg's vehicles and Brooks' subsumption architecture

Emergent Intelligence

Foundations and applications of multi-agent systems

Students should also be able to plan and realize projects based on the presented concepts.

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Developed by Simon Linimair and Matthias Habringer