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Influence of environmental factors on human-like decision-making for intelligent ship. / Xue, Jie; Chen, Zhijun; Papadimitriou, Eleonora; Wu, Chaozhong; Van Gelder, P. H.A.J.M.

In: Ocean Engineering, Vol. 186, 106060, 08.2019.

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@article{19d7d7bf935b409784af9160fda77b7c,
title = "Influence of environmental factors on human-like decision-making for intelligent ship",
abstract = "To date, the increasing density of water traffic has caused the ship's navigation environment to deteriorate, resulting in frequent water traffic accidents. In addition, a majority of maritime accidents are caused by human factors, and one of the important ways to solve the ship accidents caused by human factors is to utilize intelligent maneuvering of ships. Based on the actual crews' operational data from full-task handling simulation platform, this study combines a 30,000-ton bulk carrier inbound navigation scenario and uses the decision tree method to propose a knowledge learning model under multiple environmental constraints to give intelligent ships the ability to make decisions like a human: An intelligent ship Human-like Decision-making Maneuvering Decision Recognition (HDMDR) model. The decision-making mechanism for the maneuvering behavior of Officer On Watch (OOW) under the influence of the specific water traffic environment in the inbound scenario is analyzed, and the OOW's decision-making knowledge is automatically acquired and represented. The validation tests and the comparative analysis with the classic classification algorithms of k-Nearest Neighbours (k-NN) and Support Vector Machine (SVM) are performed to demonstrate the accuracy of the proposed HDMDR model. This paper provides a feasible basis for the human-like decision-making analysis of intelligent ships.",
keywords = "C4.5 algorithm, Classification rule, Data mining, Decision-making, Intelligent ship, Ship maneuvering",
author = "Jie Xue and Zhijun Chen and Eleonora Papadimitriou and Chaozhong Wu and {Van Gelder}, {P. H.A.J.M.}",
year = "2019",
month = "8",
doi = "10.1016/j.oceaneng.2019.05.042",
language = "English",
volume = "186",
journal = "Ocean Engineering",
issn = "0029-8018",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Influence of environmental factors on human-like decision-making for intelligent ship

AU - Xue, Jie

AU - Chen, Zhijun

AU - Papadimitriou, Eleonora

AU - Wu, Chaozhong

AU - Van Gelder, P. H.A.J.M.

PY - 2019/8

Y1 - 2019/8

N2 - To date, the increasing density of water traffic has caused the ship's navigation environment to deteriorate, resulting in frequent water traffic accidents. In addition, a majority of maritime accidents are caused by human factors, and one of the important ways to solve the ship accidents caused by human factors is to utilize intelligent maneuvering of ships. Based on the actual crews' operational data from full-task handling simulation platform, this study combines a 30,000-ton bulk carrier inbound navigation scenario and uses the decision tree method to propose a knowledge learning model under multiple environmental constraints to give intelligent ships the ability to make decisions like a human: An intelligent ship Human-like Decision-making Maneuvering Decision Recognition (HDMDR) model. The decision-making mechanism for the maneuvering behavior of Officer On Watch (OOW) under the influence of the specific water traffic environment in the inbound scenario is analyzed, and the OOW's decision-making knowledge is automatically acquired and represented. The validation tests and the comparative analysis with the classic classification algorithms of k-Nearest Neighbours (k-NN) and Support Vector Machine (SVM) are performed to demonstrate the accuracy of the proposed HDMDR model. This paper provides a feasible basis for the human-like decision-making analysis of intelligent ships.

AB - To date, the increasing density of water traffic has caused the ship's navigation environment to deteriorate, resulting in frequent water traffic accidents. In addition, a majority of maritime accidents are caused by human factors, and one of the important ways to solve the ship accidents caused by human factors is to utilize intelligent maneuvering of ships. Based on the actual crews' operational data from full-task handling simulation platform, this study combines a 30,000-ton bulk carrier inbound navigation scenario and uses the decision tree method to propose a knowledge learning model under multiple environmental constraints to give intelligent ships the ability to make decisions like a human: An intelligent ship Human-like Decision-making Maneuvering Decision Recognition (HDMDR) model. The decision-making mechanism for the maneuvering behavior of Officer On Watch (OOW) under the influence of the specific water traffic environment in the inbound scenario is analyzed, and the OOW's decision-making knowledge is automatically acquired and represented. The validation tests and the comparative analysis with the classic classification algorithms of k-Nearest Neighbours (k-NN) and Support Vector Machine (SVM) are performed to demonstrate the accuracy of the proposed HDMDR model. This paper provides a feasible basis for the human-like decision-making analysis of intelligent ships.

KW - C4.5 algorithm

KW - Classification rule

KW - Data mining

KW - Decision-making

KW - Intelligent ship

KW - Ship maneuvering

UR - http://www.scopus.com/inward/record.url?scp=85068041358&partnerID=8YFLogxK

U2 - 10.1016/j.oceaneng.2019.05.042

DO - 10.1016/j.oceaneng.2019.05.042

M3 - Article

VL - 186

JO - Ocean Engineering

T2 - Ocean Engineering

JF - Ocean Engineering

SN - 0029-8018

M1 - 106060

ER -

ID: 55038962