Research Engineer
Andreas Kreutz studied computer science at the Technical University of Munich. After graduating, he joined the Fraunhofer Institute for Cognitive Systems IKS in 2020. His research there focuses on the question of how uncertainty can also be used as a basis for decision-making in autonomous systems. For his doctorate, he is focusing specifically on uncertainty modeling and active uncertainty reduction at runtime.
by Andreas Kreutz
To investigate the future of automation and possible transition paths towards it, Fraunhofer IKS is working on extending automation systems for novel applications. Read on to understand how these prototypes have taken shape and what insights they provide.
If uncertainties in an autonomous system are not dealt with, they impair the system’s functionality and can lead to safety risks. In part 1 of our series on uncertainty, we presented a system for classifying uncertainties. In part 2, we will look at specific sources of uncertainty that have been noted in the relevant literature.
Cognitive systems are often affected by the problem of uncertainty, particularly if those systems use machine learning to perceive their environments. A system’s sensors are not the only source of uncertainty, however. The life cycle of any highly complex system involves a large number of decisions that are vulnerable to the effects of uncertain knowledge. Part 1 of our series on uncertainty focuses on the latest developments in this technology.
