Case learning for CBR-based collision avoidance systems

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DOIResolve DOI: http://doi.org/10.1007/s10489-010-0262-z
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TypeArticle
Journal titleThe International Journal of Applied Intelligence
Pages# of pages: 12
Subjectcase-based reasoning; ship collision avoidance; maritime affair records; case learning; case base management
AbstractWith the rapid development of case-based reasoning (CBR) techniques, CBR has been widely applied to real-world applications such as collision avoidance systems. A successful CBR-based system relies on a high-quality case base, and a case creation technique for generating such a case base is highly required. In this paper, we propose an automated case learning method for CBR-based collision avoidance systems. Building on techniques from CBR and natural language processing, we developed a methodology for learning cases from maritime affair records. After giving an overview on the developed systems, we present the methodology and the experiments conducted in case creation and case evaluation. The experimental results demonstrated the usefulness and applicability of the case learning approach for generating cases from the historic maritime affair records.
Publication date
LanguageEnglish
AffiliationNational Research Council Canada (NRC-CNRC); NRC Institute for Information Technology
Peer reviewedYes
NPARC number16335082
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Record identifiercd58ad9f-bdf5-4134-b5f9-e46d58204f4d
Record created2010-11-10
Record modified2016-05-09
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