Case learning for CBR-based collision avoidance systems

Authors:
Yuhong Liu;Chunsheng Yang;Yubin Yang;Fuhua Lin;Xuanmin Du;Takayuki Ito
Affiliations:
Shanghai Aerospace Electronic Technology Institute, Shanghai, China;Institute for Information Technology, National Research Council Canada, Ottawa, Canada;State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;Athabasca University, Athabasca, Canada;Shanghai Marine Electronic Equipment Research Institute, Shanghai, China;Nagoya Institute of Technology, Nagoya, Japan
Venue:
Applied Intelligence
Year:
2012

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Abstract

With 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.