Doctoral Consortium – Program
Elmar Rückert: Probabilistic Robot Control and Learning (keynote)
Artificial intelligence is regarded as one of the most groundbreaking developments of recent times. However, in controlling autonomous systems we are still far from achieving the human motor intelligence of a newborn or young child. In this talk I will discuss why current algorithms for autonomous systems and robot learning methods have not yet reached the required autonomy and performance needed to enter daily life. I will present current developments of biologically inspired decision models for autonomous systems and will discuss probabilistic prediction models that can be implemented in massively parallelizable neural networks. These neural networks are trained by a combination of supervised and unsupervised neuroinspired learning rules and enable complex decisions based on learned internal prediction models. The efficient learning rules allow the model to react to new environmental conditions within seconds and to process high dimensional tactile and visual data. These model properties are essential for adaptive, reliable, explainable and robust artificial systems.
Robust Task Planning with Uncertain Models
Towards Ordering Sets of Arguments
Argumentation-driven Medical Research Publication Analysis and Query System
Recognition and Extraction of Game Patterns from Text Supported by Interactive Learning from Board Game Instructions
Algorithms for Inconsistency Measurement
Efficient Query Answering in Nonparametric Probabilistic Graphical Models
To facilitate a fruitful exchange of knowledge and to activate interesting discussions, we want to emphasize the fact that you can also join the StudentDay track to listen to their keynote and talks. More information on the technical aspects of that will follow shortly.