Case learning for CBR-based collision avoidance systems

Yuhong Liu, Chunsheng Yang, Yubin Yang, Fuhua Lin, Xuanmin Du, Takayuki Ito

Research output: Contribution to journalJournal Articlepeer-review

18 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)308-319
Number of pages12
JournalApplied Intelligence
Issue number2
Publication statusPublished - Mar. 2012


  • Case base management
  • Case learning
  • Case-based reasoning
  • Maritime affair records
  • Ship collision avoidance


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