A Service Recommendation Using Reinforcement Learning for Network-based Robots in Ubiquitous Computing Environments

Ubiquitous robotic companion (URC ) is a new concept for the network-based robot platform which can enable to be following its master wherever or whenever he/she be in order to provide necessary services. The robot platforms in present normally interest in providing services through the direct interaction in responding to the user´s demands. On the other hand, URC services are required to be provided by the means of recognizing the circumstances and taking a user´s preference into account. In this paper, we propose a service recommendation scheme for URC robots. The proposed service recommendation, developed based on the reinforcement learning, can be used to provide personalized services by learning users´ preferences or tasks through the interaction with users. Using simulation for rapid testing, we evaluate of the proposed scheme under a variety of user modeling types and discount factors.