Intelligent Systems

Intelligent Systems

Lecture (infInS-01a)

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


Exercise (ÜinfInS-01a)

language of instruction: English
Winter Term 2019-20


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.



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