Case learning in CBR-based agent systems for ship collision avoidance

Yuhong Liu; Chunsheng Yang; Yubin Yang; Fuhua Lin; Xuanmin Du

doi:10.1007/978-3-642-11161-7_40

Case learning in CBR-based agent systems for ship collision avoidance

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

School of Computing and Information systems

Research output: Chapter in Book/Report/Conference proceeding › Published Conference contribution › peer-review

4 Citations (Scopus)

Abstract

With the rapid development of case-based reasoning (CBR) techniques, CBR has been widely applied to real-world applications such as agent-based systems for ship collision avoidance. A successful CBR-based system relies on a high-quality case base. Automated case creation technique is highly demanded. In this paper, we propose an automated case learning method for CBR-based agent systems. Building on techniques from CBR and natural language processing, we developed a method for learning cases from maritime affair records. After reviewing the developed agent-based systems for ship collision avoidance, we present the proposed framework and the experiments conducted in case generation. The experimental results show the usefulness and applicability of case learning approach for generating cases from the historic maritime affair records.

Original language	English
Title of host publication	Principles of Practice in Multi-Agent Systems - 12th International Conference, PRIMA 2009, Proceedings
Pages	542-551
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-11161-7_40
Publication status	Published - 2009
Event	12th International Conference on Principles of Practice in Multi-Agent Systems, PRIMA 2009 - Nagoya, Japan Duration: 14 Dec. 2009 → 16 Dec. 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5925 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Principles of Practice in Multi-Agent Systems, PRIMA 2009
Country/Territory	Japan
City	Nagoya
Period	14/12/09 → 16/12/09

Keywords

Case base management/updating
Case learning
Case-based reasoning
Maritime affair records
Multi-agent system
Ship collision avoidance

Access to Document

10.1007/978-3-642-11161-7_40

Cite this

Liu, Y., Yang, C., Yang, Y., Lin, F., & Du, X. (2009). Case learning in CBR-based agent systems for ship collision avoidance. In Principles of Practice in Multi-Agent Systems - 12th International Conference, PRIMA 2009, Proceedings (pp. 542-551). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5925 LNAI). https://doi.org/10.1007/978-3-642-11161-7_40

Liu, Yuhong ; Yang, Chunsheng ; Yang, Yubin et al. / Case learning in CBR-based agent systems for ship collision avoidance. Principles of Practice in Multi-Agent Systems - 12th International Conference, PRIMA 2009, Proceedings. 2009. pp. 542-551 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Case learning in CBR-based agent systems for ship collision avoidance",

abstract = "With the rapid development of case-based reasoning (CBR) techniques, CBR has been widely applied to real-world applications such as agent-based systems for ship collision avoidance. A successful CBR-based system relies on a high-quality case base. Automated case creation technique is highly demanded. In this paper, we propose an automated case learning method for CBR-based agent systems. Building on techniques from CBR and natural language processing, we developed a method for learning cases from maritime affair records. After reviewing the developed agent-based systems for ship collision avoidance, we present the proposed framework and the experiments conducted in case generation. The experimental results show the usefulness and applicability of case learning approach for generating cases from the historic maritime affair records.",

keywords = "Case base management/updating, Case learning, Case-based reasoning, Maritime affair records, Multi-agent system, Ship collision avoidance",

author = "Yuhong Liu and Chunsheng Yang and Yubin Yang and Fuhua Lin and Xuanmin Du",

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Liu, Y, Yang, C, Yang, Y, Lin, F & Du, X 2009, Case learning in CBR-based agent systems for ship collision avoidance. in Principles of Practice in Multi-Agent Systems - 12th International Conference, PRIMA 2009, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5925 LNAI, pp. 542-551, 12th International Conference on Principles of Practice in Multi-Agent Systems, PRIMA 2009, Nagoya, Japan, 14/12/09. https://doi.org/10.1007/978-3-642-11161-7_40

Case learning in CBR-based agent systems for ship collision avoidance. / Liu, Yuhong; Yang, Chunsheng; Yang, Yubin et al.
Principles of Practice in Multi-Agent Systems - 12th International Conference, PRIMA 2009, Proceedings. 2009. p. 542-551 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5925 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Published Conference contribution › peer-review

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T1 - Case learning in CBR-based agent systems for ship collision avoidance

AU - Liu, Yuhong

AU - Yang, Chunsheng

AU - Yang, Yubin

AU - Lin, Fuhua

AU - Du, Xuanmin

PY - 2009

Y1 - 2009

N2 - With the rapid development of case-based reasoning (CBR) techniques, CBR has been widely applied to real-world applications such as agent-based systems for ship collision avoidance. A successful CBR-based system relies on a high-quality case base. Automated case creation technique is highly demanded. In this paper, we propose an automated case learning method for CBR-based agent systems. Building on techniques from CBR and natural language processing, we developed a method for learning cases from maritime affair records. After reviewing the developed agent-based systems for ship collision avoidance, we present the proposed framework and the experiments conducted in case generation. The experimental results show the usefulness and applicability of case learning approach for generating cases from the historic maritime affair records.

AB - With the rapid development of case-based reasoning (CBR) techniques, CBR has been widely applied to real-world applications such as agent-based systems for ship collision avoidance. A successful CBR-based system relies on a high-quality case base. Automated case creation technique is highly demanded. In this paper, we propose an automated case learning method for CBR-based agent systems. Building on techniques from CBR and natural language processing, we developed a method for learning cases from maritime affair records. After reviewing the developed agent-based systems for ship collision avoidance, we present the proposed framework and the experiments conducted in case generation. The experimental results show the usefulness and applicability of case learning approach for generating cases from the historic maritime affair records.

KW - Case base management/updating

KW - Case learning

KW - Case-based reasoning

KW - Maritime affair records

KW - Multi-agent system

KW - Ship collision avoidance

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U2 - 10.1007/978-3-642-11161-7_40

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Liu Y, Yang C, Yang Y, Lin F, Du X. Case learning in CBR-based agent systems for ship collision avoidance. In Principles of Practice in Multi-Agent Systems - 12th International Conference, PRIMA 2009, Proceedings. 2009. p. 542-551. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-11161-7_40

Case learning in CBR-based agent systems for ship collision avoidance

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