Intelligent Systems

Computational Intelligence

Lecture (infCi-01a)

4 SWS, ECTS Credits: 8
Language of instruction: German
Lecture period winter term 2019-20
 

Exercise (ÜinfCI-01a)

2 SWS
Language of instruction: german
Lecture period winter term 2019-20

 

Summary

The term "Computational Intelligence" (CI) describes a sub-area of ​​artificial intelligence. Essentially, it summarizes three biologically motivated areas of information processing: Based on algorithms of fuzzy logic and artificial neural networks as well as on evolutionary algorithms, the aim is to master complex systems and combine them with other, typically biologically-inspired, processes. Originally coined in the 1990s by the Institute of Electrical and Electronics Engineers (IEEE), the term is now often used synonymously with soft computing. All sub-areas included have in common that they make mechanisms of natural (i.e. in particular biological, physical or social) problem-solving strategies usable for mathematical or engineering-technical questions. The aim is not a direct transfer or "technical copy", but an understanding and imitation of the basic mechanisms. The methods developed in this way are in contrast to exact mathematical methods - one rather freely follows the motto: "What works is allowed".

 

Learning goals

The aim of the event is to provide an initial overview of the field of computational intelligence in theory and practice. Building on this, students should be given a basic understanding and appropriate approaches so that the following goals in particular can be achieved:

  • The students have a basic understanding of the complexity of technical systems and know how it can be mastered.
  • The students know how seemingly complex relationships can be easily described with mechanisms of computational intelligence.
  • Techniques from the field of Computational Intelligence / Soft Computing and their advantages and disadvantages in comparison are known.

 

Workload

60 hours of lectures, 30 hours of face-to-face exercises, 150 hours of self-study

 

Intelligent Systems

Lecture (infInS-01a)

4 SWS, ECTS credits: 8
Language of instruction: English
Winter Term 2019-20

 

Exercise (ÜinfInS-01a)

2 SWS
language of instruction: English
Winter Term 2019-20
 

Summary

An "Intelligent system" is a computing system capable of operating under difficult conditions (e.g. time-varying environments, emergent situations or disturbances) by autonomously adapting its behaviour to changing conditions and learning autonomously. The main goal of engineering intelligent systems is to counter the challenges of complexity by means of integrating desired characteristics such as robustness, flexibility or resilience into technical systems. This is combined with a continuous improvement of the system behaviour. The improvement process is achieved by different approaches of machine learning, e.g. from the fields of reinforcing, active or semi-supervised learning. Besides these learning-related aspects, the design and organisation of large-scale intelligent systems consisting of a potentially large group of autonomous subsystems requires techniques for self-organisation as well as mechanisms for trust relations and fairness.

The lecture gives an introduction to the design and realisation of intelligent systems. It is based on the insights of research initiatives such as "Organic Computing" and "Autonomic Computing".

 

Learning goals

The overall goal of the course is to derive a basic understanding of the motivation, the general concept, and engineering methods of intelligent systems. Based on this, students will learn about machine learning techniques capable of gathering and describing the environmental and internal conditions of an intelligent systems as well as for improving the behaviour autonomously at runtime.

Particular goals are:

  • Students understand the motivation and the need for intelligent systems that act autonomously without (or with only limited) user intervention or guidance.
  • Students can define the terms "Intelligent System", "Organic Computing", and "Autonomic Computing"
  • Students are able to design intelligent systems by assessing and selecting a suitable basic model.
  • Students can implement selected methods for clustering and classifying situations based on data gathered by sensors.
  • Students can compare algorithms for learning from feedback and implement the most promising variant.
  • Students are capable of quantifying system aspects of large-scale organisations of autonomous intelligent systems with respect to characteristics such as robustness, emergence, self-organisation, autonomy, or adaptivity.

 

Workload

60 h lecture, 30 h exercise, 150 h self-study

 

Self-organised Systems

Bachelor seminar (infBSemIS-01a)

2 SWS, ECTS credits: 4
Language of instruction: German
Winter term 2019-20

 

Summary

The seminar is dedicated to the topic of "Self-organised Systems". In the context of this seminar, this is understood to mean sets of autonomous and technical units that work together to achieve a common goal. Different questions arise that have to be mastered.

The seminar in the winter semester 2019/20 is dedicated to the consistent, fast and reliable storage and the associated finding of content within such self-organized systems. This is examined using algorithms from peer-to-peer computing.

The seminar in summer term 2020 is dedicated to the cooperation of autonomous intelligent systems and the associated interaction patterns within such self-organized systems. This is examined using examples from autonomic and organic computing.

 

Learning goals

The students learn to independently acquire complex issues using the compact presentation, as is customary in scientific publications (i.e. papers, articles or specialist books) and to prepare them in an understandable form.

 

Workload

30 hours of participation in the seminar, 90 hours of self-study