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Journal of Sensors
Volume 2015 (2015), Article ID 954920, 11 pages
http://dx.doi.org/10.1155/2015/954920
Research Article

Automated Space Classification for Network Robots in Ubiquitous Environments

13D Systems Korea, Inc., Seoul 135-917, Republic of Korea
2Department of Multimedia Engineering, Dongguk University-Seoul, Seoul 100-715, Republic of Korea

Received 20 September 2014; Accepted 27 November 2014

Academic Editor: Han-Chieh Chao

Copyright © 2015 Jiwon Choi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Network robots provide services to users in smart spaces while being connected to ubiquitous instruments through wireless networks in ubiquitous environments. For more effective behavior planning of network robots, it is necessary to reduce the state space by recognizing a smart space as a set of spaces. This paper proposes a space classification algorithm based on automatic graph generation and naive Bayes classification. The proposed algorithm first filters spaces in order of priority using automatically generated graphs, thereby minimizing the number of tasks that need to be predefined by a human. The filtered spaces then induce the final space classification result using naive Bayes space classification. The results of experiments conducted using virtual agents in virtual environments indicate that the performance of the proposed algorithm is better than that of conventional naive Bayes space classification.