Motivation
Due to continuously rising complexity in the increasing networked, flexible, and individualized production within industry 4.0, employees need effective decision support systems. Thereby the ever-growing volume of data describing the production offers unforeseen opportunities on the one hand but also major challenges at the same time. Especially the high flexibility and variability of the production become a great challenge for data processing.
Portfolio
In the research area “Cognitive Decision Making”, we explore the aspect
of computational decision making, starting with the creation of novel analysis
techniques for big data, through the development of classification systems for
industry, to the development of industrial systems that autonomously predict
undesired system states and take preventive corrective action in an unobtrusive
manner, at all granular system levels from individual production actuators to
ensembles or even the control of complete store floor units. Targeted research
addresses: (i) Data Analytics Method Bases -general cognitive functions for
base- and reality-mining, scientific, semantic visualization of data; (ii)
Computational Data Analytics – providing cognition-based functions for the
handling of data under uncertainty (learning); (iii) Decision Making Method
Bases – models of cognitive functions for decision making including goal and
plan representation, resource allocation and optimization of processes; (iv)
Computational Decision Making – transparent automatic multi-criteria,
multi-resource decision making, choice modelling and heuristics.
