NDLI: Service-oriented context reasoning incorporating patterns and knowledge for understanding human-augmented situations

Content Provider	IEEE Xplore Digital Library
Author	Gi Hyun Lim Kun Woo Kim Byoungjun Chung Il Hong Suh Hyowon Suh Munsang Kim
Copyright Year	2010
Description	Author affiliation: Center for Intelligent Robotics, Korea Institute of Science and Technology, Seoul, Korea (Munsang Kim) \|\| Department of Electronics and Computer Engineering, Hanyang University, Korea (Gi Hyun Lim; Byoungjun Chung; Il Hong Suh) \|\| Department of Industrial Engineering, KAIST, Daejeon, Korea (Kun Woo Kim; Hyowon Suh)
Abstract	Understanding situations has been regarded as a highly difficult task due to its complexity, especially in case of human-augmented ones where the contexts of situations are largely influenced by human activity. Even though the complexity of situations can be solved by modeling the real environment, it is not easy to model which covers uncertain problems of real world effectively. For this, this paper proposes a fusion technology for service-oriented context reasoning combining low-level sensory patterns and high-level semantic knowledge using Hidden Markov Model (HMM) and ontology. Integrated temporal reasoning using our approach enables a service robot to understand human-augmented situations immediately whenever the critical situation happens. Experimental results show that the proposed method can successfully extract situations from continuous sensory signals with 80% percent reliability.
Starting Page	144
Ending Page	150
File Size	1352698
Page Count	7
File Format	PDF
ISBN	9781424479917
ISSN	19449445
e-ISBN	9781424479900
DOI	10.1109/ROMAN.2010.5598667
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-13
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Hidden Markov models Context modeling Cognition Ontologies Robot sensing systems
Content Type	Text
Resource Type	Article

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