To Ask or Not to Ask: A User Annoyance Aware Preference Elicitation Framework for Social Robots
Balint Gucsi, Danesh Tarapore, William Yeoh, Christopher Amato, Long Tran-Thanh · 2020
In this paper we investigate how social robots can efficiently gather user preferences without exceeding the allowed user annoyance threshold. To do so, we use a Gazebo based simulated office environment with a TIAGo Steel robot. We then formulate the user annoyance aware preference elicitation problem as a combination of tensor completion and knapsack problems. We then test our approach on the aforementioned simulated environment and demonstrate that it can accurately estimate user preferences.