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Allison CogSIMA 2011 situation awareness revised.pdf228.82 kBAdobe PDFView/Open
Title: Situation awareness in context-aware case-based decision support.
Authors: Nwiabu, Nuka D.
Allison, Ian
Holt, Patrik
Lowit, Peter
Oyeneyin, Babs
Keywords: Situation awareness
Context awareness
Domain modelling
Case-based reasoning
Decision support
Action research
Agile user-centred design
Issue Date: Feb-2011
Publisher: IEEE
Citation: NWIABU, N., ALLISON, I., HOLT, P., LOWIT, P. and OYENEYIN, B., 2011. Situation awareness in context-aware case-based decision support. In: IEEE Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA 2011). 22-24 February 2011. Piscataway, New Jersey: IEEE. Pp. 9-16
Abstract: Humans naturally reuse recalled knowledge to solve problems and this includes understanding the information that identify or characterize these problems (context), and the situation. Context-aware case-based reasoning (CBR) applications uses the context of users to provide solutions to problems. The combination of a context-aware CBR with general domain knowledge has been shown to improve similarity assessment, solving domain specific problems and problems of uncertain knowledge. Whilst these CBR approaches in context awareness address problems of incomplete data and domain specific problems, future problems that are situation-dependent cannot be anticipated due to lack of the facility to predict the state of the environment. This paper builds on prior work to present an approach that combines situation awareness, context awareness, case-based reasoning, and general domain knowledge in a decision support system. In combining these concepts the architecture of this system provides the capability to handle uncertain knowledge and predict the state of the environment in order to solve specific domain problems. The paper evaluates the concepts through a trial implementation in the flow assurance control domain to predict the formation of hydrate in sub-sea oil and gas pipelines. The results show a clear improvement in both similarity assessment and problem solving prediction.
ISBN: 9781612847856
Appears in Collections:Conference publications (Computing)

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