Descripción del proyecto
Una experiencia digital más humana
Es importante que los avances tecnológicos respalden las actividades e interacciones humanas en áreas como los sistemas sanitarios, de movilidad y de infraestructuras. Por ejemplo, para conseguir que la sanidad sea más humana es necesario usar interfaces digitales que permitan establecer interacciones más humanas con el sistema. Este es el objetivo de los sistemas centrados en el ser humano, en el que el humano es al mismo tiempo un elemento del sistema de control y un criterio de diseño. El proyecto CO-MAN, financiado con fondos europeos, desarrollará un marco para un control basado en datos que se adapta al usuario con garantías de rendimiento. El mayor reto será combinar las técnicas de modelización probabilística no paramétrica de la teoría del aprendizaje estadístico con las nuevas metodologías de control sensibles al riesgo, al tiempo que se incluye un modelado activo de usuarios. El gran cambio es la tendencia actual hacia un aprendizaje automático fiable con resultados novedosos sobre los límites teóricos de la conducta del aprendizaje.
Objetivo
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.
Ámbito científico
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcontrol systems
- natural sciencesbiological sciencesbiological behavioural sciencesethologybiological interactions
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programa(s)
Régimen de financiación
ERC-COG - Consolidator GrantInstitución de acogida
80333 Muenchen
Alemania