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Conference Paper A Service Recommendation Using Reinforcement Learning for Network-based Robots in Ubiquitous Computing Environments
Cited 5 time in scopus Share share facebook twitter linkedin kakaostory
Authors
Ae Kyung Moon, Tae Gun Kang, Hyoung Sun Kim, Hyun Kim
Issue Date
2007-08
Citation
International Symposium on Robot and Human Interactive Communication (RO-MAN) 2007, pp.821-826
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ROMAN.2007.4415198
Abstract
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. ©2007 IEEE.
KSP Keywords
Network-based robot, Personalized service, Recommendation scheme, Reinforcement Learning(RL), Robot platform, Service Recommendation, Ubiquitous computing environment, User modeling, User's Preference, direct interaction, robotic companion