Project description
Making the digital experience more human
It is important for advances in technology to support human activities and interactions in the areas of healthcare, mobility and infrastructure systems. For instance, making healthcare more human requires digital interfaces to allow for more human interactions with the system. This is the goal of human-centric systems in which the human is both an element of the control system and a design criterion. The EU-funded CO-MAN project will develop a framework for user-adaptive data-driven control with performance guarantees. The biggest challenge will be to merge probabilistic non-parametric modelling techniques from statistical learning theory with novel risk-aware control methodologies while including active user modelling. The game changer is the current push towards reliable machine learning with novel results on theoretical bounds for learning behaviour.
Objective
Many control systems of the future involve a tight interaction or even symbiosis with the human user. High-impact application domains of human-centric systems include healthcare, mobility, and infrastructure systems. In human-centric systems the human is both, an element of the control system, and a design criterion with individual requirements that need to be satisfied. Safety - despite the high uncertainty of human behavior - and maximization of individual user experience are the primary objectives for control design in human-centric systems. The visionary goal of CO-MAN is to contribute to the fundamental understanding and principled approach to the control of smart human-centric systems. We will develop a novel framework for user-adaptive data-driven control with performance guarantees in order to address the scientific challenges of high uncertainty and individual user requirements. The grand challenge is to unify the two previously separate paradigms of model-based control with its rigorous guarantees but limited modeling base and machine learning algorithms with its flexible modeling concepts but lack of guarantees. The breakthrough enabling idea is to merge probabilistic non-parametric modeling techniques from statistical learning theory with novel risk-aware control methodologies while including active user modeling. The game changer is the current push towards reliable machine learning with novel results on theoretical bounds for learning behavior. Because of favorable properties we will focus on Gaussian Processes to model user behavior and preferences and translate the naturally quantified model uncertainty into closed loop behavior guarantees through a confidence-driven human-interactive control approach. The PI is in a perfect position to achieve the envisioned goal of super-individualized data-driven control with performance guarantees given the highly visible preliminary results and leadership in the area of human-cyber-physical systems.
Fields of science
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcontrol systems
- natural sciencesbiological sciencesbiological behavioural sciencesethologybiological interactions
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programme(s)
Funding Scheme
ERC-COG - Consolidator GrantHost institution
80333 Muenchen
Germany