The Clinician Engineer Hub is an international network that brings together the clinical and biomedical engineering fields and provides medical students and clinicians with an exposure to the endless possibilities created by this intertwinement. This event acts as the first Robotic Webinar of the Clinican Engineer Hub series, and will host Dr. Mauro Dragone! Dr. Dragone will give a talk on the use of Internet of Things (IoT) and Robotic technology for Ambient Assisted Living (AAL) applications. He will provide an overview of the OpenAAL project, which has used a combination of robotic telepresence, cloud technologies, virtual reality, and digital twins technology to provide a platform where researchers, industry, and care providers alongside end-users of assisted living services to co-create technology, where time and distance is no longer a barrier – any time, anyplace access. His research focuses on building smart spaces combining sensors, actuators and robots.

We tackle the problem of constructive preference elicitation, that is the problem of learning user preferences over verylarge decision problems, involving a combinatorial space of possible outcomes. In this setting, the suggested configuration is synthesized on-the-fly by solving a constrained optimization problem, while the preferences are learned iteratively by interacting with the user. Previous work has shown that Coactive Learning is a suitable method for learning userpreferences in constructive scenarios. In Coactive Learning the user provides feedback to the algorithm in the form of an improvement to a suggested configuration. When the problem involves many decision variables and constraints, this type of interaction poses a significant cognitive burden on the user. We propose a decomposition technique for large preference-based decision problems relying exclusively on inference and feedback over partial configurations. This has the clear advantage of drastically reducing the user cognitive load. Additionally, part-wise inference can be (up to exponentially) less computationally demanding than inference over full configurations. We discuss the theoretical implications of working with parts and present promising empirical results on one synthetic and two realistic constructive problems.

We tackle the problem of constructive preference elicitation, that is the problem of learning user preferences over very large decision problems, involving a combinatorial space of possible outcomes. In this setting, the suggested configuration is synthesized on-the-fly by solving a constrained optimization problem, while the preferences are learned itera tively by interacting with the user. Previous work has shown that Coactive Learning is a suitable method for learning user preferences in constructive scenarios. In Coactive Learning the user provides feedback to the algorithm in the form of an improvement to a suggested configuration. When the problem involves many decision variables and constraints, this type of interaction poses a significant cognitive burden on the user. We propose a decomposition technique for large preference-based decision problems relying exclusively on inference and feedback over partial configurations. This has the clear advantage of drastically reducing the user cognitive load. Additionally, part-wise inference can be (up to exponentially) less computationally demanding than inference over full configurations. We discuss the theoretical implications of working with parts and present promising empirical results on one synthetic and two realistic constructive problems.