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.
Similar content being viewed by others
References
Sato Y, Ishii H (1998) Study of collision-avoidance system for ships. Control Eng Pract 6:1141–1149
Liu Y, Yang W (2004) The structure design of an intelligent decision support system for navigation collision avoidance. In: IEEE third international conference on machine learning and cybernetics, vol 1, pp 302–307
Liu Y (1999) A design and study on intelligence collision prevention expert system for navigation. PhD Thesis, Harbin Engineering University, China
Yang C (1995) An expert system for collision avoidance and its application. PhD Thesis, Hiroshima University, Japan
Wang CH (2002) The integrated design of fuzzy collision avoidance and H ∞-autopilots on ships. J Navig 55(1):117–136
Liu Y, Du X, Yang S (2006) The design of a fuzzy-neural network for ship collision avoidance. In: ICMLC 2005. Lecture notes in computer science, vol 3930, pp 804–812
Zhao J, Wang F (1997) The collision regulations and cases. Dalian Maritime University Press, pp 560–685
Shiu SCK, Pal SK (2004) Case-based reasoning: concepts, features and soft computing. Int J Appl Intell 21(3):233–238
Policastro CA, Carvalho ACPLF, Delbem ACB (2008) A hybrid case adaptation approach for case-based reasoning. Int J Appl Intell 28(2):101–119
Liu Y, Wen M, Du Z (2009) A case learning model for ship collision avoidance based on automatic text analysis. In: The proceedings of IEEE the fifth international conference on machine learning and cybernetics, July 2009, Baoding, China
Liu Y, Yang C, Du X (2008) Multi-agent planning for ship collision avoidance. In: The proceedings of IEEE international conferences on cybernetics & intelligent systems (CIS) and robotics, automation & mechatronics (RAM) (CIS-RAM 2008), Chengdu, China, June, 2008
Liu Y, Yang C, Du X (2008) A CBR-based approach for ship collision avoidance. In: The proceedings of the 21st international conference on industrial & engineering applications of artificial intelligence & expert systems (IEA/AIE-2008), Wroclaw, Poland, June 2008
Park M, Lee KK, Shon KM, Yoon WC (2001) Automating the diagnosis and rectification of deflection Yoke production using hybrid knowledge acquisition and case-based reasoning. Int J Appl Intell 15(1):25–40
Féret MP, Glasgow JI (1997) Combining case-based and model-based reasoning for the diagnosis of complex devices. Int J Appl Intell 7(1):57–78
Leake DB, Wilson DC (2001) A case-based framework for interactive capture and reuse of design knowledge. Int J Appl Intell 14(1):77–94
Kravis S, Irrgang R (2005) A case based system for oil and gas well design with risk assessment. Int J Appl Intell 23(1):39–53
Avesani P, Perini A, Ricci F (2000) Interactive case-based planning for forest fire management. Int J Appl Intell 13(1):41–57
Kim K (2000) Toward global optimization of case-based reasoning systems for financial forecasting. Int J Appl Intell 21(3):239–249
Bichindaritz I, Montani S, Portinale L (2007) Special issue on case-based reasoning in the health sciences, Int J Appl Intell 28(3)
Watson I, Marir F (1994) Case-base reasoning: a review, Knowl Eng Rev 9(4)
Yang C, Orchard B, Farley B, Zaluski M (2003) Automated case base creation and management. In: The proceedings of international conference on industrial & engineering. Applications of artificial intelligence & expert system (IEA/AIE-2003)
Therani M, Zhao J, Marshall B (2004) A case-based reasoning framework for workflow model management. Data Knowl Eng 50:87–115
Avesani P, Ferrari S, Susi A (2003) Case-based ranking for decision support systems. In: ICCBR 2003. LNAI, vol 2689, pp 35–49
McSherry D (2003) Similarity and compromise. In: ICCBR 2003. LNAI, vol 2689, pp 291–305
Nordlund J, Schafer H (2006) Case-based reasoning in a support system. Master’s Thesis, UMEA University, Sweden
Yang C, Farley B, Orchard B (2008) Automated case creation and management for diagnostic CBR systems. Int J Appl Intell 28(1):17–28
Labrou Y (2001) Standardizing agent communication. Multi-agents systems and applications. In: Lecture notes in computer science, pp 74-9
Rao AS, Georgeff MP (1991) Modeling rational agents within a BDI-architecture. In: The proceedings of the 2nd international conference on principles of knowledge representation and reasoning, pp 473–484
Gong H, Gong C, Zhou C (2004) Chinese word segmentation system research. J Beijing Inst Mach 19(3):52–61
Qiu J, Wen T, Zhou L (2005) Research of Chinese automatic segmentation and content analysis method. J China Soc Sci Tech Inf 24(3):309–317
China MSA (Maritime Safety Administration) (2007) Typical water traffic accident cases. China Communications Press, pp 1–89
Buzek EJ, Holder HMC (1984) Collision cases judgments and diagrams. Lloyd’s of London Press Ltd
Liu Y, Liu H (2006) Case learning based on evaluation system for vessel collision avoidance. In: The proceedings of IEEE the fifth international conference on machine learning and cybernetics, vol 4, pp 2064–2069
Liu Y, Hu S (2005) An evaluation system for single-target ship collision avoidance based on data fusion. Navig China 65(4):40–45
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, Y., Yang, C., Yang, Y. et al. Case learning for CBR-based collision avoidance systems. Appl Intell 36, 308–319 (2012). https://doi.org/10.1007/s10489-010-0262-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-010-0262-z